2021/07/12 更新

写真a

モンデン アキト
門田 暁人
MONDEN Akito
所属
自然科学学域 教授
職名
教授
ホームページ
外部リンク

学位

  • 博士(工学) ( 奈良先端科学技術大学院大学 )

研究キーワード

  • Empirical Software Engineering

  • Software Protection

  • 実証的ソフトウェア工学

  • ソフトウェアプロテクション

研究分野

  • 情報通信 / ソフトウェア

学歴

  • 奈良先端科学技術大学院大学   情報科学研究科  

    - 1998年

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    国名: 日本国

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  • 奈良先端科学技術大学院大学   Graduate School, Division of Information Science  

    - 1998年

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  • 名古屋大学   工学部   電気工学科

    - 1994年

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    国名: 日本国

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  • 名古屋大学   Faculty of Engineering  

    - 1994年

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経歴

  • 奈良先端科学技術大学院大学

    2015年 - 2017年

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  • Visiting Professor,Nara Institute of Science and Technology

    2015年 - 2017年

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  • - 岡山大学自然科学研究科 教授

    2015年

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  • - Professor,Graduate School of Natural Science and Technology,Okayama University

    2015年

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  • 奈良先端科学技術大学院大学

    2007年 - 2015年

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  • Associate Professor,Nara Institute of Science and Technology

    2007年 - 2015年

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  • 奈良先端科学技術大学院大学

    2004年 - 2007年

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  • Associate Professor,Nara Institute of Science and Technology

    2004年 - 2007年

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  • ニュージーランド オークランド大学 Honorary Research Fellow

    2003年 - 2004年

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  • Honorary Research Fellow,The University of Auckland

    2003年 - 2004年

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  • 奈良先端科学技術大学院大学

    1998年 - 2004年

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  • Research Associate,Nara Institute of Science and Technology

    1998年 - 2004年

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▼全件表示

所属学協会

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書籍等出版物

  • ソフトウェア開発データリポジトリの分析

    一般財団法人経済調査会  2015年 

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  • Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2012 (Studies in Computational Intelligence, Vol. 443)

    Springer  2012年 

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MISC

  • MAHAKIL: Diversity Based Oversampling Approach to Alleviate the Class Imbalance Issue in Software Defect Prediction

    Kwabena Ebo Bennin, Jacky Keung, Passakorn Phannachitta, Akito Monden, Solomon Mensah

    IEEE Transactions on Software Engineering   44 ( 6 )   534 - 550   2018年6月

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    記述言語:英語   出版者・発行元:Institute of Electrical and Electronics Engineers Inc.  

    Highly imbalanced data typically make accurate predictions difficult. Unfortunately, software defect datasets tend to have fewer defective modules than non-defective modules. Synthetic oversampling approaches address this concern by creating new minority defective modules to balance the class distribution before a model is trained. Notwithstanding the successes achieved by these approaches, they mostly result in over-generalization (high rates of false alarms) and generate near-duplicated data instances (less diverse data). In this study, we introduce MAHAKIL, a novel and efficient synthetic oversampling approach for software defect datasets that is based on the chromosomal theory of inheritance. Exploiting this theory, MAHAKIL interprets two distinct sub-classes as parents and generates a new instance that inherits different traits from each parent and contributes to the diversity within the data distribution. We extensively compare MAHAKIL with SMOTE, Borderline-SMOTE, ADASYN, Random Oversampling and the No sampling approach using 20 releases of defect datasets from the PROMISE repository and five prediction models. Our experiments indicate that MAHAKIL improves the prediction performance for all the models and achieves better and more significant pf values than the other oversampling approaches, based on Brunner's statistical significance test and Cliff's effect sizes. Therefore, MAHAKIL is strongly recommended as an efficient alternative for defect prediction models built on highly imbalanced datasets.

    DOI: 10.1109/TSE.2017.2731766

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  • Impact of the Distribution Parameter of Data Sampling Approaches on Software Defect Prediction Models

    Kwabena Ebo Bennin, Jacky Keung, Akito Monden

    Proceedings - Asia-Pacific Software Engineering Conference, APSEC   2017-   630 - 635   2018年3月

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    記述言語:英語   出版者・発行元:IEEE Computer Society  

    Sampling methods are known to impact defect prediction performance. These sampling methods have configurable parameters that can significantly affect the prediction performance. It is however, impractical to assess the effect of all the possible different settings in the parameter space for all the several existing sampling methods. A constant and easy to tweak parameter present in all sampling methods is the distribution of the defective and non-defective modules in the dataset known as Pfp (% of fault-prone modules). In this paper, we investigate and assess the performance of defect prediction models where the Pfp parameter of sampling methods are tweaked. An empirical experiment and assessment of seven sampling methods on five prediction models over 20 releases of 10 static metric projects indicate that (1) Area Under the Receiver Operating Characteristics Curve (AUC) performance is not improved after tweaking the Pfp parameter, (2) pf (false alarms) performance degrades as the Pfp is increased. (3) a stable predictor is difficult to achieve across different Pfp rates. Hence, we conclude that the Pfp parameter setting can have a large impact on the performance (except AUC) of defect prediction models. We thus recommend researchers experiment with the Pfp parameter of the sampling method since the distribution of training datasets vary.

    DOI: 10.1109/APSEC.2017.76

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  • MAHAKIL: Diversity based oversampling approach to alleviate the class imbalance issue in software defect prediction

    Kwabena Ebo Bennin, Jacky Keung, Passakorn Phannachitta, Akito Monden, Solomon Mensah

    Proc. 40th International Conference on Software Engineering, Journal First Paper   699 - 699   2018年

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  • Predictability classification for software effort estimation

    Naoki Kinoshita, Akito Monden, Masateru Tsunoda, Zeynep Yucel

    Proc. 3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science Engineering (BCD2018)   53 - 58   2018年

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  • Extended association rule mining with correlation functions

    Hidekazu Saito, Akito Monden, Zeynep Yucel

    Proc. 3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science Engineering (BCD2018)   83 - 88   2018年

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  • Should duration and team size be used for effort estimation?

    Takeshi Kakimoto, Masateru Tsunoda, Akito Monden

    Studies in Computational Intelligence   721   91 - 105   2018年

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    記述言語:英語   出版者・発行元:Springer Verlag  

    Project management activities such as scheduling and project progress management are important to avoid project failure. As a basis of project management, effort estimation plays a fundamental role. To estimate software development effort by mathematical models, variables which are fixed before the estimation are used as independent variables. Some studies used team size and project duration as independent variables. Although they are sometimes fixed because of the limitation of human resources or business schedule, they may change by the end of the project. For instance, when delivery is delayed, actual duration and estimated duration is different. So, although using team size and project duration may enhance estimation accuracy, the error may also lower the accuracy. To help practitioners to select independent variables, we analyzed whether team size and duration should be used or not, when we consider the error included in the team size and the duration. In the experiment, we assumed that duration and team size include errors when effort is estimated. To analyze influence of the errors, we add n% errors to duration and team size. As a result, using duration as an independent variable was not very effective in many cases. In contrast, using maximum team size as an independent variable was effective when the error rate is equal or less than 50%.

    DOI: 10.1007/978-3-319-62048-0_7

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  • Evaluating algorithmic thinking ability of primary schoolchildren who learn computer programming

    Hidekuni Tsukamoto, Yasumasa Oomori, Hideo Nagumo, Yasuhiro Takemura, Akito Monden, Ken ichi Matsumoto

    Proceedings - Frontiers in Education Conference, FIE   2017-October   1 - 8   2017年12月

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    © 2017 IEEE. In this research, a tool for evaluating algorithmic thinking ability of the primary schoolchildren was developed. This tool is based on the three categories of operations used to construct algorithms, namely, sequential operations, conditional branching operations, and iterative operations. Each question in the tool checks to see if the examinee understands the concept of one of the three categories. The tool was developed to evaluate the educational effect of programming education for middle to upper grade (third to sixth grade) primary schoolchildren. Since both Visual Programming Language (VPL) and Textual Programming Language (TPL) could be used, it was required that the tool could be used by both the group of children who use VPLs and the group of children who use TPLs. To make it possible, no programming language appeared in the questions in the tool. The teaching materials for the programming education were also developed in such a way that the three basic concepts of algorithm, namely, sequential processing, conditional branching, and repetitive processing, were clearly taught. The target VPL in this research was Scratch. The evaluation tool was conducted in a weekend class of programming education for primary schoolchildren, and the algorithmic thinking ability of the schoolchildren was analyzed.

    DOI: 10.1109/FIE.2017.8190609

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  • The Significant Effects of Data Sampling Approaches on Software Defect Prioritization and Classification

    Kwabena Ebo Bennin, Jacky Keung, Akito Monden, Passakorn Phannachitta, Solomon Mensah

    International Symposium on Empirical Software Engineering and Measurement   2017-   364 - 373   2017年12月

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    記述言語:英語   出版者・発行元:IEEE Computer Society  

    Context: Recent studies have shown that performance of defect prediction models can be affected when data sampling approaches are applied to imbalanced training data for building defect prediction models. However, the magnitude (degree and power) of the effect of these sampling methods on the classification and prioritization performances of defect prediction models is still unknown. Goal: To investigate the statistical and practical significance of using resampled data for constructing defect prediction models. Method: We examine the practical effects of six data sampling methods on performances of five defect prediction models. The prediction performances of the models trained on default datasets (no sampling method) are compared with that of the models trained on resampled datasets (application of sampling methods). To decide whether the performance changes are significant or not, robust statistical tests are performed and effect sizes computed. Twenty releases of ten open source projects extracted from the PROMISE repository are considered and evaluated using the AUC, pd, pf and G-mean performance measures. Results: There are statistical significant differences and practical effects on the classification performance (pd, pf and G-mean) between models trained on resampled datasets and those trained on the default datasets. However, sampling methods have no statistical and practical effects on defect prioritization performance (AUC) with small or no effect values obtained from the models trained on the resampled datasets. Conclusions: Existing sampling methods can properly set the threshold between buggy and clean samples, while they cannot improve the prediction of defect-proneness itself. Sampling methods are highly recommended for defect classification purposes when all faulty modules are to be considered for testing.

    DOI: 10.1109/ESEM.2017.50

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  • Scaling up software birthmarks using fuzzy hashing

    Takehiro Tsuzaki, Teruaki Yamamoto, Haruaki Tamada, Akito Monden

    International Journal of Software Innovation   5 ( 3 )   89 - 102   2017年7月

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    記述言語:英語   出版者・発行元:Taru Publications  

    To detect the software theft, software birthmarks have been proposed. Software birthmark systems extract software birthmarks, which are native characteristics of software, from binary programs, and compare them by computing the similarity between birthmarks. This paper proposes a new procedure for scaling up the birthmark systems. While conventional birthmark systems are composed of the birthmark extraction phase and the birthmark comparison phase, the proposed method adds two new phases between extraction and comparison, namely, compression phase, which employs fuzzy hashing, and pre-comparison phase, which aims to increase distinction property of birthmarks. The proposed method enables us to reduce the required time in the comparison phase, so that it can be applied to detect software theft among many larger scale software products. From an experimental evaluation, the authors found that the proposed method significantly reduces the comparison time, and keeps the distinction performance, which is one of the important properties of the birthmark. Also, the preservation performance is acceptable when the threshold value is properly set.

    DOI: 10.4018/IJSI.2017070107

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  • Examining Software Engineering Beliefs about System Testing Defects

    Akito Monden, Masateru Tsunoda, Mike Barker, Kenichi Matsumoto

    IT PROFESSIONAL   19 ( 2 )   58 - 64   2017年3月

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    記述言語:英語   出版者・発行元:IEEE COMPUTER SOC  

    Software engineering beliefs-short, attention-getting, practically useful statements-can help to justify process improvements. The authors empirically validate four selected beliefs in relation to the increase or decrease of defects in system testing.

    DOI: 10.1109/MITP.2017.31

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  • Industry Application of Software Development Task Measurement System: TaskPit

    Pawin Suthipornopas, Pattara Leelaprute, Akito Monden, Hidetake Uwano, Yasutaka Kamei, Naoyasu Ubayashi, Kenji Araki, Kingo Yamada, Ken-ichi Matsumoto

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E100D ( 3 )   462 - 472   2017年3月

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    記述言語:英語   出版者・発行元:IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

    To identify problems in a software development process, we have been developing an automated measurement tool called TaskPit, which monitors software development tasks such as programming, testing and documentation based on the execution history of software applications. This paper introduces the system requirements, design and implementation of TaskPit; then, presents two real-world case studies applying TaskPit to actual software development. In the first case study, we applied TaskPit to 12 software developers in a certain software development division. As a result, several concerns (to be improved) have been revealed such as (a) a project leader spent too much time on development tasks while he was supposed to be a manager rather than a developer, (b) several developers rarely used e-mails despite the company's instruction to use e-mail as much as possible to leave communication records during development, and (c) several developers wrote too long e-mails to their customers. In the second case study, we have recorded the planned, actual, and self reported time of development tasks. As a result, we found that (d) there were unplanned tasks in more than half of days, and (e) the declared time became closer day by day to the actual time measured by TaskPit. These findings suggest that TaskPit is useful not only for a project manager who is responsible for process monitoring and improvement but also for a developer who wants to improve by him/herself.

    DOI: 10.1587/transinf.2016EDP7222

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  • A stability assessment of solution adaptation techniques for analogy-based software effort estimation

    Passakorn Phannachitta, Jacky Keung, Akito Monden, Kenichi Matsumoto

    EMPIRICAL SOFTWARE ENGINEERING   22 ( 1 )   474 - 504   2017年2月

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    記述言語:英語   出版者・発行元:SPRINGER  

    Among numerous possible choices of effort estimation methods, analogy-based software effort estimation based on Case-based reasoning is one of the most adopted methods in both the industry and research communities. Solution adaptation is the final step of analogy-based estimation, employed to aggregate and adapt to solutions derived during the case-based reasoning process. Variants of solution adaptation techniques have been proposed in previous studies; however, the ranking of these techniques is not conclusive and shows conflicting results, since different studies rank these techniques in different ways. This paper aims to find a stable ranking of solution adaptation techniques for analogy-based estimation. Compared with the existing studies, we evaluate 8 commonly adopted solution techniques with more datasets (12), more feature selection techniques included (4), and more stable error measures (5) to a robust statistical test method based on the Brunner test. This comprehensive experimental procedure allows us to discover a stable ranking of the techniques applied, and to observe similar behaviors from techniques with similar adaptation mechanisms. In general, the linear adaptation techniques based on the functions of size and productivity (e.g., regression towards the mean technique) outperform the other techniques in a more robust experimental setting adopted in this study. Our empirical results show that project features with strong correlation to effort, such as software size or productivity, should be utilized in the solution adaptation step to achieve desirable performance. Designing a solution adaptation strategy in analogy-based software effort estimation requires careful consideration of those influential features to ensure its prediction is of relevant and accurate.

    DOI: 10.1007/s10664-016-9434-8

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  • Benchmarking IT operations cost based on working time and unit cost

    Masateru Tsunoda, Akito Monden, Kenichi Matsumoto, Sawako Ohiwa, Tomoki Oshino

    SCIENCE OF COMPUTER PROGRAMMING   135   75 - 87   2017年2月

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    記述言語:英語   出版者・発行元:ELSEVIER SCIENCE BV  

    Recently, size of information system gets large, and the information system operation (IT operations) is often outsourced. When IT operations of the large system is outsourced, high cost is needed, and troubles in IT operations may affect the activity of the company. Therefore, the information system operation is important for the companies. Cost is one of the important factors when the system operation is outsourced. However, it is not easy for the customers (system users) to judge whether the operation cost is valid or not. To support the judgment, we focus on information which the customers can know (e.g., size of software), to estimate the working time. In the analysis, we clarified the factors which affect the working time. Then, data was stratified based on the factors, to show the benchmark of working time. Using the benchmark, customers estimate the working time roughly. Also, we clarified the factors which affect the unit cost, and showed the benchmark. Operation cost can be estimated, by estimated working time multiplied by the estimated unit cost. The analysis results showed that the process standardization relates to the working time of operators. Also, the network range and the contract type have a relationship to the unit cost of operators. Work efficiency and unit cost do not affect operation quality. (C) 2016 Elsevier B.V. All rights reserved.

    DOI: 10.1016/j.scico.2016.10.006

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  • COCOMO IIをベースとした工数見積りモデルの研究

    大岩 佐和子, 押野 智樹, 門田 暁人, 松本 健一

    プロジェクトマネジメント学会誌   19 ( 4 )   53 - 58   2017年

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  • 提案依頼書に含まれる無理難題の分類

    門田暁人, 住吉倫明, 神谷芳樹

    SEC journal   13 ( 3 )   18 - 25   2017年

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  • Unsupervised Bug Report Categorization Using Clustering and Labeling Algorithm

    Nachai Limsettho, Hideaki Hata, Akito Monden, Kenichi Matsumoto

    INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING   26 ( 7 )   1027 - 1053   2016年9月

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    記述言語:英語   出版者・発行元:WORLD SCIENTIFIC PUBL CO PTE LTD  

    Bug reports are one of the most crucial information sources for software engineering offering answers to many questions. Yet, getting these answers is not always easy; the information in bug reports is often implicit and some processes are required to extract the meaning of these reports. Most research in this area employ a supervised learning approach to classify bug reports so that required types of reports could be identified. However, this approach often requires an immense amount of time and effort, the resources that already too scarce in many projects.We aim to develop an automated framework that can categorize bug reports, according to their grammatical structure without the need for labeled data.Our framework categorizes bug reports according to their text similarity using topic modeling and a clustering algorithm. Each group of bug reports are labeled with our new clustering labeling algorithm specifically made for clusters in the topic space. Our framework is highly customizable with a modular approach and options to incorporate available background knowledge to improve its performance, while our cluster labeling approach make use of natural language process (NLP) chunking to create the representative labels.Our experiment results demonstrate that the performance of our unsupervised framework is comparable to a supervised learning one. We also show that our labeling process is capable of labeling each cluster with phrases that are representative for that cluster's characteristics.Our framework can be used to automatically categorize the incoming bug reports without any prior knowledge, as an automated labeling suggestion system or as a tool for obtaining knowledge about the structure of the bug report repository.

    DOI: 10.1142/S0218194016500352

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  • Investigating and Projecting Population Structures in Open Source Software Projects: A Case Study of Projects in GitHub

    Saya Onoue, Hideaki Hata, Akito Monden, Kenichi Matsumoto

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E99D ( 5 )   1304 - 1315   2016年5月

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    記述言語:英語   出版者・発行元:IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

    GitHub is a developers' social networking service that hosts a great number of open source software (OSS) projects. Although some of the hosted projects are growing and have many developers, most projects are organized by a few developers and face difficulties in terms of sustainability. OSS projects depend mainly on volunteer developers, and attracting and retaining these volunteers are major concerns of the project stakeholders. To investigate the population structures of OSS development communities in detail and conduct software analytics to obtain actionable information, we apply a demographic approach. Demography is the scientific study of population and seeks to identify the levels and trends in the size and components of a population. This paper presents a case study, investigating the characteristics of the population structures of OSS projects on GitHub, and shows population projections generated with the well-known cohort component method. We found that there are four types of population structures in OSS development communities in terms of experiences and contributions. In addition, we projected the future population accurately using a cohort component population projection method. This method predicts a population of the next period using a survival rate calculated from past population. To the best of our knowledge, this is the first study that applied demography to the field of OSS research. Our approach addressing OSS-related problems based on demography will bring new insights, since studying population is novel in OSS research. Understanding current and future structures of OSS projects can help practitioners to monitor a project, gain awareness of what is happening, manage risks, and evaluate past decisions.

    DOI: 10.1587/transinf.2015EDP7363

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  • LSA-X: Exploiting Productivity Factors in Linear Size Adaptation for Analogy-Based Software Effort Estimation

    Passakorn Phannachitta, Akito Monden, Jacky Keung, Kenichi Matsumoto

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E99D ( 1 )   151 - 162   2016年1月

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    記述言語:英語   出版者・発行元:IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

    Analogy-based software effort estimation has gained a considerable amount of attention in current research and practice. Its excellent estimation accuracy relies on its solution adaptation stage, where an effort estimate is produced from similar past projects. This study proposes a solution adaptation technique named LSA-X that introduces an approach to exploit the potential of productivity factors, i.e., project variables with a high correlation with software productivity, in the solution adaptation stage. The LSA-X technique tailors the exploitation of the productivity factors with a procedure based on the Linear Size Adaptation (LSA) technique. The results, based on 19 datasets show that in circumstances where a dataset exhibits a high correlation coefficient between productivity and a related factor (r = 0.30), the proposed LSA-X technique statistically outperformed (95% confidence) the other 8 commonly used techniques compared in this study. In other circumstances, our results suggest using any linear adaptation technique based on software size to compensate for the limitations of the LSA-X technique.

    DOI: 10.1587/transinf.2015EDP7237

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  • Filter-INC: Handling Effort-Inconsistency in Software Effort Estimation Datasets

    Passakorn Phannachitta, Jacky Keung, Kwabena Ebo Bennin, Akito Monden, Kenichi Matsumoto

    2016 23RD ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE (APSEC 2016)   185 - 192   2016年

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    記述言語:英語   出版者・発行元:IEEE  

    Effort-inconsistency is a situation where historical software project data used for software effort estimation (SEE) are contaminated by many project cases with similar characteristics but are completed with significantly different amount of effort. Using these data for SEE generally produces inaccurate results; however, an effective technique for its handling is yet made to be available. This study approaches the problem differently from common solutions, where available techniques typically attempt to remove every project case they have detected as outliers. Instead, we hypothesize that data inconsistency is caused by only a few deviant project cases and any attempt to remove those other cases will result in reduced accuracy, largely due to loss of useful information and data diversity. Filter-INC (short for Filtering technique for handling effort-INConsistency in SEE datasets) implements the hypothesis to decide whether a project case being detected by any existing technique should be subject to removal. The evaluation is carried out by comparing the performance of 2 filtering techniques between before and after having Filter-INC applied. The results produced from 8 real-world datasets together with 3 machine-learning models, and evaluated by 4 performance measures show a significant accuracy improvement at the confident interval of 95%. Based on the results, we recommend our proposed hypothesis as an important instrument to design a data preprocessing technique for handling effort-inconsistency in SEE datasets, definitely an important step forward in preprocessing data for a more accurate SEE model.

    DOI: 10.1109/APSEC.2016.48

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  • Task purpose estimation in software development based on automatic measurement data and machine learning

    Ryota Ohashi, Hidetake Uwano, Akito Monden, Kenji Araki, Kingo Yamada, Kenichi Matsumoto

    Computer Software   33 ( 2 )   139 - 150   2016年

  • 自動計測データと機械学習に基づくソフトウェア開発の作業目的の推定

    大橋亮太, 上野秀剛, 門田暁人, 荒木健史, 山田欣吾, 松本健一

    コンピュータソフトウェア   33 ( 2 )   139 - 150   2016年

  • Measuring difficulty of program comprehension based on brain activation

    Takao Nakagawa, Ysutaka Kamei, Hidetake Uwano, Akito Monden, Naoyasu Ubayashi, Kenichi Matsumoto

    Computer Software   33 ( 2 )   78 - 89   2016年

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  • 脳活動に基づくプログラム理解の困難さ測定

    中川尊雄, 亀井靖高, 上野秀剛, 門田暁人, 鵜林尚靖, 松本健一

    コンピュータソフトウェア   33 ( 2 )   78 - 89   2016年

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  • Investigating the Effects of Balanced Training and Testing Datasets on Effort-Aware Fault Prediction Models

    Kwabena Ebo Bennin, Jacky Keung, Akito Monden, Yasutaka Kamei, Naoyasu Ubayashi

    PROCEEDINGS 2016 IEEE 40TH ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE WORKSHOPS, VOL 1   154 - 163   2016年

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    記述言語:英語   出版者・発行元:IEEE COMPUTER SOC  

    To prioritize software quality assurance efforts, fault prediction models have been proposed to distinguish faulty modules from clean modules. The performances of such models are often biased due to the skewness or class imbalance of the datasets considered. To improve the prediction performance of these models, sampling techniques have been employed to rebalance the distribution of fault-prone and non-fault-prone modules. The effect of these techniques have been evaluated in terms of accuracy/geometric mean/F1-measure in previous studies; however, these measures do not consider the effort needed to fix faults. To empirically investigate the effect of sampling techniques on the performance of software fault prediction models in a more realistic setting, this study employs Norm(Popt), an effort-aware measure that considers the testing effort. We performed two sets of experiments aimed at (1) assessing the effects of sampling techniques on effort-aware models and finding the appropriate class distribution for training datasets (2) investigating the role of balanced training and testing datasets on performance of predictive models. Of the four sampling techniques applied, the over-sampling techniques outperformed the under-sampling techniques with Random Over-sampling performing best with respect to the Norm(Popt) evaluation measure. Also, performance of all the prediction models improved when sampling techniques were applied between the rates of (20-30)% on the training datasets implying that a strictly balanced dataset (50% faulty modules and 50% clean modules) does not result in the best performance for effort-aware models. Our results also indicate that performances of effort-aware models are significantly dependent on the proportions of the two types of the classes in the testing dataset. Models trained on moderately balanced datasets are more likely to withstand fluctuations in performance as the class distribution in the testing data varies.

    DOI: 10.1109/COMPSAC.2016.144

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  • Identifying recurring association rules in software defect prediction

    Takashi Watanabe, Akito Monden, Yasutaka Kamei, Shuji Morisaki

    Proc. IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS2016)   1 - 6   2016年

  • A fuzzy hashing technique for large scale software birthmarks

    Takehiro Tsuzaki, Teruaki Yamamoto, Haruaki Tamada, Akito Monden

    Proc. IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS2016)   1 - 6   2016年

  • Analysis of information system operation cost based on working time and unit cost

    Masateru Tsunoda, Akito Monden, Kenichi Matsumoto, Sawako Ohiwa, Tomoki Oshino

    Proc. IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS2016)   1 - 6   2016年

  • Influence of outliers on analogy based software development effort estimation

    Kenichi Ono, Masateru Tsunoda, Akito Monden, Kenichi Matsumoto

    Proc. IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS2016)   1 - 6   2016年

  • ソフトウェアテストにおける静的解析ツールの段階的適用による不具合修正工数の更なる低減 ―民生用音響・映像機器向け組み込みソフトウェア開発へのQACの段階的適用とその実証評価―

    鶴田雅明, 大平雅雄, 門田暁人, 松本健一

    情報社会学会誌   11 ( 1 )   5 - 16   2016年

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  • Empirical Evaluation of Cross-Release Effort-Aware Defect Prediction Models

    Kwabena Ebo Bennin, Koji Toda, Yasutaka Kamei, Jacky Keung, Akito Monden, Naoyasu Ubayashi

    2016 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE QUALITY, RELIABILITY AND SECURITY (QRS 2016)   214 - 221   2016年

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    記述言語:英語   出版者・発行元:IEEE  

    To prioritize quality assurance efforts, various fault prediction models have been proposed. However, the best performing fault prediction model is unknown due to three major drawbacks: (1) comparison of few fault prediction models considering small number of data sets, (2) use of evaluation measures that ignore testing efforts and (3) use of n-fold cross validation instead of the more practical cross-release validation. To address these concerns, we conducted cross-release evaluation of 11 fault density prediction models using data sets collected from 2 releases of 25 open source software projects with an effort-aware performance measure known as Norm(P-opt). Our result shows that, whilst M5 and K* had the best performances; they were greatly influenced by the percentage of faulty modules present and size of data set. Using Norm(P-opt) produced an overall average performance of more than 50% across all the selected models clearly indicating the importance of considering testing efforts in building fault-prone prediction models.

    DOI: 10.1109/QRS.2016.33

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  • Textual vs. visual programming languages in programming education for primary schoolchildren

    Hidekuni Tsukamoto, Yasuhiro Takemura, Yasumasa Oomori, Isamu Ikeda, Hideo Nagumo, Akito Monden, Ken-ichi Matsumoto

    Proc. 46th IEEE Frontiers in Education Conference (FIE2016)   1 - 7   2016年

  • Pinpointing and hiding surprising fragments in an obfuscated program

    Yuichiro Kanzaki, Clark Thomborson, Akito Monden, Christian Collberg

    ACM International Conference Proceeding Series   08-   8:1-8:9   2015年12月

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    記述言語:英語   出版者・発行元:Association for Computing Machinery  

    In this paper, we propose a pinpoint-hide defense method, which aims to improve the stealth of obfuscated code. In the pinpointing process, we scan the obfuscated code in a few small code fragment level and identify all surprising fragments, that is, very unusual fragments which may draw the attention of an attacker to the obfuscated code. In the hiding process, we transform the pinpointed surprising fragments into unsurprising ones while preserving semantics. The obfuscated code transformed by our method consists only by unsurprising code fragments, therefore is more difficult for attackers to be distinguished from unobfuscated code than the original. In the case study, we apply our pinpoint-hide method to some programs transformed by well-known obfuscation techniques. The result shows our method can pinpoint surprising fragments such as dummy code that does not fit in the context of the program, and instructions used in a complicated arithmetic expression. We also confirm that instruction camouflage can make the pinpointed surprising fragments unsurprising ones, and that it runs correctly.

    DOI: 10.1145/2843859.2843862

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  • Reducing the cost of debugging through progressive use of static code analysis tools

    Masaaki Tsuruta, Akito Monden, Kenichi Matsumoto

    Proc. International Conference on Project Management (ProMAC2015)   2015年

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  • Predicting faults after unit testing using design phase metrics in embedded software development

    Masateru Tsunoda, Akito Monden, Kenichi Matsumoto

    11 ( 2 )   16 - 23   2015年

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  • Bug Report Recommendation for Code Inspection

    Shin Fujiwara, Hideaki Hata, Akito Monden, Kenichi Matsumoto

    2015 IEEE 1ST INTERNATIONAL WORKSHOP ON SOFTWARE ANALYTICS (SWAN)   9 - 12   2015年

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    記述言語:英語   出版者・発行元:IEEE  

    Large software projects such as Mozilla Firefox and Eclipse own more than ten thousand bug reports that have been reported but left unresolved. To utilize such a great amount of unresolved bug reports and accelerate bug detection and removal, we propose to a way recommend programmers a bug report that is likely to contain failure descriptions related to a source file being inspected. We employ the vector space model (VSM) to make a relevancy ranking of bug reports to a given source file. The result of an experiment using data of three open source software projects showed that the accuracies of recommendations ranged from 21.74% to 60.05% in terms of the percentage of recommendations that contained relevant bug reports in a top 10 recommended list.

    DOI: 10.1109/SWAN.2015.7070481

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  • Programming Education for Primary Schoolchildren Using a Textual Programming Language

    Hidekuni Tsukamoto, Yasuhiro Takemura, Isamu Ikeda, Akito Monden, Kenichi Matsumoto

    Proc. 45th Frontiers in Education Conference (FIE2015)   1 - 7   2015年

  • 著作権守る電子透かし

    門田暁人

    読売新聞奈良版 寄稿連載「ドキ★ワク先端科学」   2015年

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  • Case consistency: A necessary data quality property for software engineering data sets

    Passakorn Phannachitta, Akito Monden, Jacky Keung, Kenichi Matsumoto

    Proc. 19th International Conference on Evaluation and Assessment in Software Engineering (EASE2015)   19:1-19:10   2015年

  • Code Artificiality: A Metric for the Code Stealth Based on an N-gram Model

    Yuichiro Kanzaki, Akito Monden, Christian Collberg

    2015 IEEE/ACM 1ST INTERNATIONAL WORKSHOP ON SOFTWARE PROTECTION (SPRO)   31 - 37   2015年

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    記述言語:英語   出版者・発行元:IEEE  

    This paper proposes a method for evaluating the artificiality of protected code by means of an N-gram model. The proposed artificiality metric helps us measure the stealth of the protected code, that is, the degree to which protected code can be distinguished from unprotected code. In a case study, we use the proposed method to evaluate the artificiality of programs that are transformed by well-known obfuscation techniques. The results show that static obfuscating transformations (e.g., control flow flattening) have little effect on artificiality. However, dynamic obfuscating transformations (e.g., code encryption), or a technique that inserts junk code fragments into the program, tend to increase the artificiality, which may have a significant impact on the stealth of the code.

    DOI: 10.1109/SPRO.2015.14

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  • Benchmarking Software Maintenance Based on Working Time

    Masateru Tsunoda, Akito Monden, Kenichi Matsumoto, Sawako Ohiwa, Tomoki Oshino

    3RD INTERNATIONAL CONFERENCE ON APPLIED COMPUTING AND INFORMATION TECHNOLOGY (ACIT 2015) 2ND INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND INTELLIGENCE (CSI 2015)   20 - 27   2015年

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    記述言語:英語   出版者・発行元:IEEE  

    Software maintenance is an important activity on the software lifecycle. Software maintenance does not mean only removing faults found after software release. Software needs extensions or modifications of its functions due to changes in a business environment, and software maintenance also indicates them. In this research, we try to establish a benchmark of work efficiency for software maintenance. To establish the benchmark, factors affecting work efficiency should be clarified, using a dataset collected from various organizations (cross-company dataset). We used dataset includes 134 data points collected by Economic Research Association in 2012, and analyzed factors affected work efficiency of software maintenance. We defined the work efficiency as number of modified modules divided by working time. The main contribution of our research is illustrating factors affecting work efficiency, based on the analysis using cross-company dataset and working time. Also, we showed work efficiency, classified the factor. It can be used to benchmark an organization. We empirically illustrated that using Java and restriction of development tool affect to work efficiency.

    DOI: 10.1109/ACIT-CSI.2015.13

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  • 組込みソフトウェア開発における設計関連メトリクスに基づく下流試験欠陥数の予測

    角田雅照, 門田暁人, 松本健一

    SEC journal   11 ( 2 )   16 - 23   2015年

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  • Scaling up analogy-based software effort estimation: A Comparison of multiple hadoop implementation schemes

    Passakorn Phannachitta, Jacky Keung, Akito Monden, Kenichi Matsumoto

    International Workshop on Innovative Software Development Methodologies and Practices, InnoSWDev 2014 - Proceedings   65 - 72   2014年11月

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    記述言語:英語   出版者・発行元:Association for Computing Machinery, Inc  

    Analogy-based estimation (ABE) is one of the most time consuming and compute intensive method in software de- velopment effort estimation. Optimizing ABE has been a dilemma because simplifying the procedure can reduce the estimation performance, while increasing the procedure com- plexity with more sophisticated theory may sacrifice an ad- vantage of the unlimited scalability for a large data input. Motivated by an emergence of cloud computing technology in software applications, in this study we present 3 different implementation schemes based on Hadoop MapReduce to optimize the ABE process across multiple computing in- stances in the cloud-computing environment. We experimentally compared the 3 MapReduce implementation schemes in contrast with our previously proposed GPGPU approach (named ABE-CUDA) over 8 high-performance Amazon EC2 instances. Results present that the Hadoop solution can pro- vide more computational resources that can extend the scalability of the ABE process. We recommend adoption of 2 different Hadoop implementations (Hadoop streaming and RHadoop) for accelerating the computation specifically for compute-intensive software engineering related tasks.

    DOI: 10.1145/2666581.2666582

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  • Prediction of the change of learners' motivation in programming education for non-computing majors

    Hidekuni Tsukamoto, Yasuhiro Takemura, Hideo Nagumo, Akito Monden, Kenichi Matsumoto

    Proc. 44th IEEE Frontiers in Education Conference (FIE2014)   1 - 7   2014年

  • Evaluation of request for proposal (RFP) focusing on non-functional requirements

    Yasuhiro Saito, Akito Monden, Kenichi Matsumoto

    10 ( 3 )   30 - 37   2014年

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  • コードの「不自然さ」に基づくソフトウェア保護機構のステルシネス評価

    神﨑 雄一郎, 尾上 栄浩, 門田 暁人

    情報処理学会論文誌   55 ( 2 )   1005 - 1015   2014年

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  • 熟練者判断を取り入れたソフトウェア開発工数見積もりモデル

    角田雅照, 門田暁人, Jacky Keung, 松本 健一

    情報処理学会論文誌   55 ( 2 )   994 - 1004   2014年

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  • Simulation of effort allocation strategies in software testing using bug module prediction

    Daisuke Nakano, Akito Monden, Yasutaka Kamei, Kenichi Matsumoto

    Computer Software   31 ( 2 )   118 - 128   2014年

  • Industry Questions About Open Source Software in Business: Research Directions and Potential Answers

    Akinori Ihara, Akito Monden, Ken-ichi Matsumoto

    2014 6TH INTERNATIONAL WORKSHOP ON EMPIRICAL SOFTWARE ENGINEERING IN PRACTICE (IWESEP 2014)   55 - 59   2014年

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    記述言語:英語   出版者・発行元:IEEE  

    As open source software (OSS) has become an integral part of today's software businesses, many software companies rely on OSS to develop their customer solutions and products. On the other hand, they face various concerns in using OSS, such as technical support, quality, security and licensing issues. This paper focuses on OSS-related FAQ in industry, and tries to answer them or to provide research directions based on lessons learned from recent mining OSS repository researches.

    DOI: 10.1109/IWESEP.2014.12

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  • Automatic Unsupervised Bug Report Categorization

    Nachai Limsettho, Hideaki Hata, Akito Monden, Kenichi Matsumoto

    2014 6TH INTERNATIONAL WORKSHOP ON EMPIRICAL SOFTWARE ENGINEERING IN PRACTICE (IWESEP 2014)   7 - 12   2014年

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    記述言語:英語   出版者・発行元:IEEE  

    Background: Information in bug reports is implicit and therefore difficult to comprehend. To extract its meaning, some processes are required. Categorizing bug reports is a technique that can help in this regard. It can be used to help in the bug reports management or to understand the underlying structure of the desired project. However, most researches in this area are focusing on a supervised learning approach that still requires a lot of human afford to prepare a training data. Aims: Our aim is to develop an automated framework than can categorize bug reports, according to their hidden characteristics and structures, without the needed for training data. Method: We solve this problem using clustering, unsupervised learning approach. It can automatically group bug reports together based on their textual similarity. We also propose a novel method to label each group with meaningful and representative names. Results: Experiment results show that our framework can achieve performance comparable to the supervised learning approaches. We also show that our labeling process can label each cluster with representative names according to its characteristic. Conclusion: Our framework could be used as an automated categorization system that can be applied without prior knowledge or as an automated labeling suggestion system.

    DOI: 10.1109/IWESEP.2014.8

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  • バグモジュール予測を用いたテスト工数割り当て戦略のシミュレーション

    中野 大輔, 門田 暁人, 亀井 靖高, 松本 健一

    コンピュータ ソフトウェア   31 ( 2 )   118 - 128   2014年

  • Quantifying Programmers' Mental Workload during Program Comprehension Based on Cerebral Blood Flow Measurement: A Controlled Experiment

    Takao Nakagawa, Yasutaka Kamei, Hidetake Uwano, Akito Monden, Kenichi Matsumoto, Daniel M. German

    36TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE COMPANION 2014)   448 - 451   2014年

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    記述言語:英語   出版者・発行元:ASSOC COMPUTING MACHINERY  

    Program comprehension is a fundamental activity in software development that cannot be easily measured, as it is performed inside the human brain. Using a wearable Near Infra-red Spectroscopy (NIRS) device to measure cerebral blood flow, this paper tries to answer the question: Can the measurement of brain blood-flow quantify programmers' mental workload during program comprehension activities? We performed a controlled experiment with 10 subjects; 8 of them showed high cerebral blood flow while understanding strongly obfuscated programs (requiring high mental workload). This suggests the possibility of using NIRS to measure the mental workload of a person during software development activities.

    DOI: 10.1145/2591062.2591098

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  • On measuring the difficulty of program comprehension based on cerebral blood flow

    Takao Nakagawa, Yasutaka Kamei, Hidetake Uwano, Akito Monden, Kenichi Matsumoto

    Computer Software   31 ( 3 )   270 - 276   2014年

  • プログラム理解の困難さの脳血流による計測の試み

    中川尊雄, 亀井靖高, 上野秀剛, 門田暁人, 松本健一

    コンピュータ ソフトウェア   31 ( 3 )   270 - 276   2014年

  • 非機能要件に着目したRequest For Proposal (RFP)の評価

    齊藤康廣, 門田暁人, 松本健一

    SEC journal   10 ( 3 )   30 - 37   2014年

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  • Assessing the Cost Effectiveness of Fault Prediction in Acceptance Testing

    Akito Monden, Takuma Hayashi, Shoji Shinoda, Kumiko Shirai, Junichi Yoshida, Mike Barker, Kenichi Matsumoto

    IEEE TRANSACTIONS ON SOFTWARE ENGINEERING   39 ( 10 )   1345 - 1357   2013年10月

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    記述言語:英語   出版者・発行元:IEEE COMPUTER SOC  

    Until now, various techniques for predicting fault-prone modules have been proposed and evaluated in terms of their prediction performance; however, their actual contribution to business objectives such as quality improvement and cost reduction has rarely been assessed. This paper proposes using a simulation model of software testing to assess the cost effectiveness of test effort allocation strategies based on fault prediction results. The simulation model estimates the number of discoverable faults with respect to the given test resources, the resource allocation strategy, a set of modules to be tested, and the fault prediction results. In a case study applying fault prediction of a small system to acceptance testing in the telecommunication industry, results from our simulation model showed that the best strategy was to let the test effort be proportional to "the number of expected faults in a module x log(module size)." By using this strategy with our best fault prediction model, the test effort could be reduced by 25 percent while still detecting as many faults as were normally discovered in testing, although the company required about 6 percent of the test effort for metrics collection, data cleansing, and modeling. The simulation results also indicate that the lower bound of acceptable prediction accuracy is around 0.78 in terms of an effort-aware measure, Norm(P-opt). The results indicate that reduction of the test effort can be achieved by fault prediction only if the appropriate test strategy is employed with high enough fault prediction accuracy. Based on these preliminary results, we expect further research to assess their general validity with larger systems.

    DOI: 10.1109/TSE.2013.21

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  • ソフトウェアリポジトリマイニング

    門田 暁人, 伊原 彰紀, 松本 健一

    コンピュータ ソフトウェア   30 ( 2 )   52 - 65   2013年

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  • 重回帰分析とプロジェクト類似性を用いたハイブリッド工数見積もり方法の提案

    戸田 航史, 角田 雅照, 門田 暁人, 松本 健一

    コンピュータ ソフトウェア   30 ( 2 )   227 - 233   2013年

  • Why is collaboration needed in OSS projects? A case study of eclipse project

    Hironori Hayashi, Akinori Ihara, Akito Monden, Ken-Ichi Matsumoto

    2013 5th International Workshop on Social Software Engineering, SSE 2013 - Proceedings   17 - 20   2013年

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    記述言語:英語  

    In open source software development, the collaboration among developers is the key to improve software quality. In particular, to fix a bug related to various parts of a system, developers need collaboration because each developer usually has very limited knowledge about a large software system. This paper aims to clarify how narrow (or how wide) is each developer's knowledge area in the Eclipse project, and how often do developers need to collaborate with each other. As a result of analysis, we found that 50 % of committers take care of just one or two modules, which indicates the necessity of collaboration when a bug-fix affects multiple modules. In addition, we also found the significant relationship between committers' collaborations and the re-opened bugs. We conclude that a committer should be aware the risk of re-opened bugs caused by the collaboration. Copyright 2013 ACM.

    DOI: 10.1145/2501535.2501539

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  • The effects of teaching material remediation with ARCS-strategies for programming education

    Hidekuni Tsukamoto, Yasuhiro Takemura, Hideo Nagumo, Akito Monden, Kenichi Matsumoto

    Proc. IEEE Frontiers in Education Conference (FIE2013)   717 - 723   2013年

  • ARCS動機づけ方略と統計的検定に基づくプログラミング教材の改善とその評価

    塚本英邦, 南雲秀雄, 門田暁人, 松本健一

    日本産業技術教育学会誌   55 ( 4 )   307 - 315   2013年

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  • Patch Reviewer Recommendation in OSS Projects

    John Boaz Lee, Akinori Ihara, Akito Monden, Ken-ichi Matsumoto

    2013 20TH ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE (APSEC 2013), VOL 2   2   1 - 6   2013年

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    記述言語:英語   出版者・発行元:IEEE COMPUTER SOC  

    In an Open Source Software (OSS) project, many developers contribute by submitting source code patches. To maintain the quality of the code, certain experienced developers review each patch before it can be applied or committed. Ideally, within a short amount of time after its submission, a patch is assigned to a reviewer and reviewed. In the real world, however, many large and active OSS projects evolve at a rapid pace and the core developers can get swamped with a large number of patches to review. Furthermore, since these core members may not always be available or may choose to leave the project, it can be challenging, at times, to find a good reviewer for a patch. In this paper, we propose a graph-based method to automatically recommend the most suitable reviewers for a patch. To evaluate our method, we conducted experiments to predict the developers who will apply new changes to the source code in the Eclipse project. Our method achieved an average recall of 0.84 for top-5 predictions and a recall of 0.94 for top-10 predictions.

    DOI: 10.1109/APSEC.2013.103

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  • Improving Analogy-based Software Cost Estimation through Probabilistic-based Similarity Measures

    Passakorn Phannachitta, Jacky Keung, Akito Monden, Ken-Ichi Matsumoto

    2013 20TH ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE (APSEC 2013), VOL 1   541 - 546   2013年

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    記述言語:英語   出版者・発行元:IEEE COMPUTER SOC  

    The performance of software cost estimation based on analogy reasoning depends upon the measures that specifying the similarity between software projects. This paper empirically investigates the use of probabilistic-based distance functions to improve the similarity measurement. The probabilistic-based distance functions are considerably more robust, because they collect the implicit correlation between the occurrences of project feature attributes. This information gain enables the constructed estimation model to be more concise and comprehensible. The study compares 6 probabilistic-based distance functions against the commonly-used Euclidian distance. We empirically evaluate the implemented cost estimation model using 5 real-world datasets collected from the PROMISE repository. The result shows a significant improvement in terms of error reduction, that implies an estimation based on probabilistic-based distance functions achieve higher accuracy on average, and the peak performance significantly outperforms the Euclidian distance based on Wilcoxon signed-rank test.

    DOI: 10.1109/APSEC.2013.78

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  • 命令のランダム性に基づくプログラム難読化の評価

    二村 阿美, 門田 暁人, 玉田 春昭, 神崎 雄一郎, 中村 匡秀, 松本 健一

    コンピュータ ソフトウェア   30 ( 3 )   18 - 24   2013年

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  • An instruction folding method to prevent reverse engineering in java platform

    Tetsuya Ohdo, Haruaki Tamada, Yuichiro Kanzaki, Akito Monden

    SNPD 2013 - 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing   517 - 522   2013年

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    記述言語:英語  

    To improve tamper resistance of programs against illegal modification, this paper proposes instruction folding applicable to Java platform. In the proposed method, at first, similar methods are selected in a Java program. Next, these methods are merged into one method and diffs among these methods are stored in the program. Then, at runtime, when one of the merged methods is executed, diffs are restored by self-modification, which is realized by the Java instrumentation mechanism. The proposed method is resilient against tampering of folded method. Even if an adversary modifies the folded method, the program goes crash because the method is repeatedly modified at runtime. © 2013 IEEE.

    DOI: 10.1109/SNPD.2013.31

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  • 11種類のfault密度予測モデルの実証的評価

    小林 寛武, 戸田 航史, 亀井 靖高, 門田 暁人, 峯 恒憲, 鵜林 尚靖

    電子情報通信学会論文誌D   J96-D ( 8 )   1892 - 1902   2013年

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  • Customizing GQM Models for Software Project Monitoring

    Akito Monden, Tomoko Matsumura, Mike Barker, Koji Torii, Victor R. Basili

    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS   E95D ( 9 )   2169 - 2182   2012年9月

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    記述言語:英語   出版者・発行元:IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG  

    This paper customizes Goal/Question/Metric (GQM) project monitoring models for various projects and organizations to take advantage of the data from the software tool EPM and to allow the tailoring of the interpretation models based upon the context and success criteria for each project and organization. The basic idea is to build less concrete models that do not include explicit baseline values to interpret metrics values. Instead, we add hypothesis and interpretation layers to the models to help people of different projects make decisions in their own context. We applied the models to two industrial projects, and found that our less concrete models could successfully identify typical problems in software projects.

    DOI: 10.1587/transinf.E95.D.2169

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  • 受託開発ソフトウェアの保守における作業効率の要因

    角田 雅照, 門田 暁人, 松本 健一, 押野 智樹

    コンピュータ ソフトウェア   29 ( 3 )   157 - 163   2012年

  • 生産性に基づくソフトウェア開発工数予測モデル

    門田 暁人, 松本 健一, 大岩 佐和子, 押野 智樹

    経済調査研究レビュー   11   32 - 37   2012年

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  • Handling Categorical Variables in Effort Estimation

    Masateru Tsunoda, Sousuke Amasaki, Akito Monden

    PROCEEDINGS OF THE ACM-IEEE INTERNATIONAL SYMPOSIUM ON EMPIRICAL SOFTWARE ENGINEERING AND MEASUREMENT (ESEM'12)   99 - 102   2012年

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    記述言語:英語   出版者・発行元:IEEE  

    Background: Accurate effort estimation is the basis of the software development project management. The linear regression model is one of the widely-used methods for the purpose. A dataset used to build a model often includes categorical variables denoting such as programming languages. Categorical variables are usually handled with two methods: the stratification and dummy variables. Those methods have a positive effect on accuracy but have shortcomings. The other handing method, the interaction and the hierarchical linear model (HLM), might be able to compensate for them. However, the two methods have not been examined in the research area. Aim: giving useful suggestions for handling categorical variables with the stratification, transforming dummy variables, the interaction, or HLM, when building an estimation model. Method: We built estimation models with the four handling methods on ISBSG, NASA, and Desharnais datasets, and compared accuracy of the methods with each other. Results: The most effective method was different for datasets, and the difference was statistically significant on both mean balanced relative error (MBRE) and mean magnitude of relative error (MMRE). The interaction and HLM were effective in a certain case. Conclusions: The stratification and transforming dummy variables should be tried at least, for obtaining an accurate model. In addition, we suggest that the application of the interaction and HLM should be considered when building the estimation model.

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  • Analysis of attributes relating to custom software price

    Masateru Tsunoda, Akito Monden, Kenichi Matsumoto, Sawako Ohiwa, Tomoki Oshino

    Proceedings - 2012 4th International Workshop on Empirical Software Engineering in Practice, IWESEP 2012   16 - 22   2012年

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    記述言語:英語  

    Price of custom software is very important for a user (end user company). However, there is very little information which helps the user judge the validity of the custom software price. So, one of our research goals is building price estimation model and showing its accuracy for the user to judge the validity of the custom software price. The other goal is how to get value for money custom software. In the analysis, we used 31 projects collected from Japanese organizations. First, we analyzed relationships of unit price of effort, unit price of FP (function point), and productivity. The analysis result showed productivity is more important variable than unit price of effort for the custom software price estimation. Next, relationships of other variables were analyzed to identify important variables for the price estimation. The result suggested some variables such as system architecture are important for that. At the end, the price was estimated based on FP and effort. In the analysis, when the price was estimated based on FP, the median balanced relative error (BRE) was 86.6%, and when it was estimated based on effort, median BRE was 20.2%. © 2012 IEEE.

    DOI: 10.1109/IWESEP.2012.19

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  • An ensemble approach of simple regression models to cross-project fault prediction

    Satoshi Uchigaki, Shinji Uchida, Koji Toda, Akito Monden

    Proceedings - 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, SNPD 2012   476 - 481   2012年

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    記述言語:英語  

    In software development, prediction of fault-prone modules is an important challenge for effective software testing. However, high prediction accuracy may not be achieved in cross-project prediction, since there is a large difference in distribution of predictor variables between the base project and the target project.@In this paper we propose an prediction technique called gan ensemble of simple regression modelsh to improve the prediction accuracy of cross-project prediction. The proposed method uses weighted sum of outputs of simple logistic regression models to improve the generalization ability of logistic models. To evaluate the performance of the proposed method, we conducted cross-project prediction using datasets of projects from NASA IV&amp
    V Facility Metrics Data Program. As a result, the proposed method outperformed conventional logistic regression models in terms of AUC of the Alberg diagram. © 2012 IEEE.

    DOI: 10.1109/SNPD.2012.34

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  • ソフトウェア開発プロジェクトをまたがるfault-proneモジュール判別の試み - 18プロジェクトの実験から得た教訓

    藏本 達也, 亀井 靖高, 門田 暁人, 松本 健一

    電子情報通信学会論文誌D   J95-D ( 3 )   425 - 436   2012年

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  • 類似性に基づくソフトウェア開発工数見積もりにおける外れ値除去法の比較

    角田 雅照, 門田 暁人, 渡邊 瑞穂, 柿元 健, 松本 健一

    電子情報通信学会論文誌 D   J95-D ( 4 )   895 - 908   2012年

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  • An Investigation on Software Bug-Fix Prediction for Open Source Software Projects-A Case Study on the Eclipse Project

    Akinori Ihara, Yasutaka Kamei, Akito Monden, Masao Ohira, Jacky Wai Keung, Naoyasu Ubayashi, Ken-ichi Matsumoto

    2012 19TH ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE WORKSHOPS (APSECW), VOL. 2   2   112 - 119   2012年

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    記述言語:英語   出版者・発行元:IEEE COMPUTER SOC  

    Open source software projects (OSS) receive a large number of bug reports from various contributors and developers alike, where many planned to be fixed by OSS developers. Given the next release cycle information, OSS users can be more effective and flexible in planning and to fix the bugs that are not to be fixed in the next release. It is therefore vital for OSS users to learn which bugs the OSS developers will fix, unfortunately such information may not be readily available, nor there is a prediction framework exists to serve such an important purpose. In this study, we would like to answer the question "Will this bug be fixed by the next release?", this is addressed by building a bug fixing prediction model based on the characteristics of a bug-related metric and by incorporating the progress of bug fixing measures such as status, period and developer metrics to provide aggregated information for the OSS users. The proposed model calculates the deviance of each variable to analyze the most important metrics, and it has been experimented using a case study with Eclipse platform. Result shows a bug fixing prediction model using both base metrics and state metrics provide significantly better performance in precision (139%) and recall (114%) than the standard model using only base metrics.

    DOI: 10.1109/APSEC.2012.86

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  • Evaluation of Non Functional Requirements in a Request For Proposal (RFP)

    Yasuhiro Saito, Akito Monden, Kenichi Matsumoto

    PROCEEDINGS OF THE 2012 JOINT CONFERENCE OF THE 22ND INTERNATIONAL WORKSHOP ON SOFTWARE MEASUREMENT AND THE 2012 SEVENTH INTERNATIONAL CONFERENCE ON SOFTWARE PROCESS AND PRODUCT MEASUREMENT (IWSM-MENSURA 2012)   106 - 111   2012年

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    記述言語:英語   出版者・発行元:IEEE  

    In the beginning of a contracted based software development project, the RFP is provided by a software user company and used as an initial system requirements specification to ask software developer companies to propose their technical plans to fulfill the requirements. In this process, it is very important to evaluate the quality of the RFP to make sure that basic user requirements are written enough. Especially, nonfunctional requirements (NFRs) are important since the system architecture greatly depends on the NFRs such as response time and security issues. This paper proposes a simple evaluation model of NFRs included in the RFP, mainly focusing on the user maintenance and operation issues. This model consists of NFR categories, NFR metrics, description level grading and weight to each NFR. As a case study, RFPs of 29 projects were evaluated by the proposed model. As a result, we confirmed that the model could identify poorly-written NFR aspects in the RFP, which need refinement before asking the developer company for a proposal.

    DOI: 10.1109/IWSM-MENSURA.2012.23

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  • Program Encryption Based on the Execution Time

    Hideshi Sakaguchi, Yuichiro Kanzaki, Akito Monden

    Proc. International Symposium on Technology for Sustainability (ISTS2012)   188 - 191   2012年

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  • A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction

    Akito Monden, Jacky Keung, Shuji Morisaki, Yasutaka Kamei, Ken-ichi Matsumoto

    2012 19TH ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE (APSEC), VOL 1   838 - 847   2012年

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    記述言語:英語   出版者・発行元:IEEE COMPUTER SOC  

    Background: Association rules are more comprehensive and understandable than fault-prone module predictors (such as logistic regression model, random forest and support vector machine). One of the challenges is that there are usually too many similar rules to be extracted by the rule mining.
    Aim: This paper proposes a rule reduction technique that can eliminate complex (long) and/or similar rules without sacrificing the prediction performance as much as possible.
    Method: The notion of the method is to removing long and similar rules unless their confidence level as a heuristic is high enough than shorter rules. For example, it starts with selecting rules with shortest length (length=1), and then it continues through the 2nd shortest rules selection (length=2) based on the current confidence level, this process is repeated on the selection for longer rules until no rules are worth included.
    Result: An empirical experiment has been conducted with the Mylyn and Eclipse PDE datasets. The result of the Mylyn dataset showed the proposed method was able to reduce the number of rules from 1347 down to 13, while the delta of the prediction performance was only .015 (from .757 down to .742) in terms of the F1 prediction criteria. In the experiment with Eclipsed PDE dataset, the proposed method reduced the number of rules from 398 to 12, while the prediction performance even improved (from .426 to .441.)
    Conclusion: The novel technique introduced resolves the rule explosion problem in association rule mining for software proneness prediction, which is significant and provides better understanding of the causes of faulty modules.

    DOI: 10.1109/APSEC.2012.103

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  • Incorporating Expert Judgment into Regression Models of Software Effort Estimation

    Masateru Tsunoda, Akito Monden, Jacky Keung, Kenichi Matsumoto

    2012 19TH ASIA-PACIFIC SOFTWARE ENGINEERING CONFERENCE (APSEC), VOL 1   374 - 379   2012年

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    記述言語:英語   出版者・発行元:IEEE COMPUTER SOC  

    One of the common problems in building an effort estimation model is that not all the effort factors are suitable as predictor variables. As a supplement of missing information in estimation models, this paper explores the project manager's knowledge about the target project. We assume that the experts can judge the target project's productivity level based on his/her own expert knowledge about the project. We also assume that this judgment can be further improved, because using the expert's judgment solely could incur subjective perception. This paper proposes a regression model building/selection method to address this challenge. In the proposed method, a fit dataset for model building is divided into two or three subsets by project productivity, and an estimation model is built on each data subset. The expert judges the productivity level of the target project and selects one of the models to be used. In the experiment, we used three datasets to evaluate the produced effort estimation models. In the experiment, we adjusted the error rate of the judgment and analyzed the relationship between the error rate and the estimation accuracy. As a result, the judgment-incorporating models produced significantly higher estimation accuracy than the conventional linear regression model, where the expert's error rate is less than 37%.

    DOI: 10.1109/APSEC.2012.58

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▼全件表示

講演・口頭発表等

  • Extended association rule mining with correlation functions

    3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science Engineering (BCD2018)  2018年 

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  • 計算機上のソフトウェア開発作業の自動推定の検討

    ウィンターワークショップ2018・イン・宮島  2018年 

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  • N-gram IDFによるバグレポートの分類の試行

    ウィンターワークショップ2018・イン・宮島  2018年 

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  • 尖度と歪度を考慮した予測モデルの検討

    ウィンターワークショップ2018・イン・宮島  2018年 

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  • 分岐命令のカムフラージュに基づくプログラムの制御フローの隠ぺい

    情報処理学会九州支部 火の国情報シンポジウム2018  2018年 

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  • 難読化されたプログラムの自動解析への耐性に関する考察

    情報処理学会第80回全国大会  2018年 

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  • MAHAKIL: Diversity based oversampling approach to alleviate the class imbalance issue in software defect prediction

    40th International Conference on Software Engineering  2018年 

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  • GitHub上のプログラマ名鑑ボットの作成

    ソフトウェアシンポジウム2018, WG11: ソーシャルコーディングとソフトウェア進化  2018年 

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  • Predictability classification for software effort estimation

    3rd IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science Engineering (BCD2018)  2018年 

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  • ソフトウェアバグの行レベル予測の試み

    ソフトウェア工学の基礎ワークショップFOSE2017  2017年 

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  • ソフトウェア開発の提案依頼書における無理難題の分析

    ソフトウェアサイエンス研究会  2017年 

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  • ソフトウェアエンジニアに求められる技術の求人票に基づく分析

    ソフトウェアサイエンス研究会  2017年 

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  • Should duration and team size be used for effort estimation?

    IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD2017)  2017年 

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  • プロジェクト間バグ予測方法の実験的評価

    ソフトウェアサイエンス研究会  2017年 

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  • 機密を保持したままソフトウェア開発データの分析を行う方法についての一考察

    ソフトウェア工学の基礎ワークショップFOSE2017  2017年 

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  • Impact of the distribution parameter of data sampling approaches on software defect prediction models

    24th Asia-Pacific Software Engineering Conference (APSEC2017)  2017年 

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  • Calibration of distributed multimodal sensor networks using cross-correlation of arrival processes

    35th Annual Conference of the Robotics Society of Japan (RSJ 2017), International Session  2017年 

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  • ソフトウェア開発に関するスモールデータの分析技術

    日本ファンクションポイントユーザ会2017年度第3回会合  2017年 

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  • Evaluating Algorithmic Thinking Ability of Primary Schoolchildren Who Learn Computer Programming

    47th IEEE Frontiers in Education Conference (FIE2017)  2017年 

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  • The significant effects of data sampling approaches on software defect prioritization and classification

    ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM2017)  2017年 

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  • GitHub上のプログラマ名鑑の作成に向けて

    ソフトウェア工学の基礎ワークショップFOSE2017  2017年 

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  • ランキング上位者のプログラミング作法の評価

    ソフトウェア工学の基礎ワークショップFOSE2017  2017年 

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  • ソフトウェア開発工数の二段階予測方法の実験的評価

    ソフトウェア工学の基礎ワークショップFOSE2017  2017年 

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  • Filter-INC: Handling Effort-Inconsistency in Software Effort Estimation Datasets

    23rd Asia-Pacific Software Engineering Conference (APSEC2016)  2016年 

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  • Influence of outliers on analogy based software development effort estimation

    15th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2016)  2016年 

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  • Analysis of information system operation cost based on working time and unit cost

    15th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2016)  2016年 

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  • Identifying Recurring Association Rules in Software Defect Prediction

    15th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2016)  2016年 

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  • Empirical Evaluation of Cross-Release Effort-Aware Defect Prediction Models

    IEEE International Conference on Software Quality, Reliability & Security (QRS2016)  2016年 

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  • Investigating the effects of balanced training and testing datasets on effort-aware fault prediction models

    IEEE Computer Software and Applications Conference (COMPSAC 2016)  2016年 

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  • A Fuzzy Hashing Technique for Large Scale Software Birthmarks

    15th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2016)  2016年 

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  • ソフトウェアリポジトリマイニングのすすめ

    日本ソフトウェア科学会第33回大会  2016年 

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  • Textual vs. Visual Programming Languages in Programming Education for Primary Schoolchildren

    46th IEEE Fronties in Education Conference (FIE2016)  2016年 

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  • バイナリプログラム圧縮によるソフトウェア流用検出

    ソフトウェアサイエンス研究会  2016年 

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  • バイナリコード中の文字列に着目したソフトウェアの流用検出

    ソフトウェアサイエンス研究会  2016年 

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  • 原型分析を用いたソフトウェアバグ分析

    ソフトウェアサイエンス研究会  2016年 

     詳細を見る

  • 非公開データからの類似データ生成

    ソフトウェア工学研究会  2015年 

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  • 保護コードの自然さに着目した命令カムフラージュ

    情報処理学会第77回全国大会  2015年 

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  • コード断片の不自然さの比較による保護機構の発見困難さの評価

    情報処理学会第77回全国大会  2015年 

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  • ソフトウェア開発工数見積もりにおける外れ値の実験的評価

    ソフトウェアシンポジウム2015  2015年 

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  • 公開不可データの変換技術

    ソフトウェアエンジニアリングシンポジウム2015ワークショップ  2015年 

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  • Toward Plagiarism Detection in Binary Programs Using Program Compression

    MSR Asia Summit 2015  2015年 

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  • 大量のプログラムを対象としたファジーハッシュを用いたバースマーク手法

    2015年暗号と情報セキュリティシンポジウム(SCIS2015)  2015年 

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  • テスト戦略の最適化に向けて

    ウィンターワークショップ2015・イン・宜野湾  2015年 

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  • COCOMO IIをベースとした工数見積りモデルの研究 - Javaを開発言語とするプロジェクトへの適用 -

    プロジェクトマネジメント学会春季大会  2015年 

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  • 再現性のあるアソシエーションルールの選定 ~ソフトウェアバグ予測を題材として~

    ソフトウェア工学研究会  2015年 

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  • ソフトウェア開発プロジェクトデータセットからの典型的なプロジェクトの抽出

    ソフトウェア工学研究会  2015年 

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  • コードクローンと使用ライブラリに着目したオープンソースソフトウェアの進化の定量化

    ソフトウェア工学研究会  2015年 

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  • 人口ピラミッドによるOSSプロジェクト貢献者の流動性分析

    第21回ソフトウェア工学の基礎ワークショップ  2014年 

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  • N-gram モデルを用いたソフトウェア保護機構の不自然さ評価

    2014年暗号と情報セキュリティシンポジウム  2014年 

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  • 作業時間に基づくソフトウェア保守ベンチマーキングの試み

    ソフトウェア工学研究会  2014年 

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  • 故障モジュール中の欠陥メソッド特定

    ソフトウェアエンジニアリングシンポジウム2014  2014年 

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  • ソフトウェアメトリクス活用テクニック ~「予測」のためのデータクリーニング,モデル化,ルール発見手法~

    ソフトウェア品質シンポジウム・チュートリアル講演  2014年 

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  • ソフトウェアリポジトリマイニングの技術動向と開発現場への活用

    プロジェクトマネジメント学会関西支部平成26年度春季シンポジウム  2014年 

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  • ソフトウェア開発データの無矛盾性の評価

    第21回ソフトウェア工学の基礎ワークショップ  2014年 

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  • 自動計測データに基づくソフトウェア開発の作業目的の予測

    第21回ソフトウェア工学の基礎ワークショップ  2014年 

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  • Java バイトコード命令のオペコード、オペランドを用いた難読化手法のステルシネス評価

    2014年暗号と情報セキュリティシンポジウム  2014年 

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  • ソフトウェア開発データあれこれ

    ウィンターワークショップ2014・イン・大洗  2014年 

     詳細を見る

  • コードレビューにおいて発見されるバグの特徴分析

    ウィンターワークショップ2014・イン・大洗  2014年 

     詳細を見る

  • 作業履歴中の主要な作業に着目した作業目的予測

    ソフトウェア工学研究会  2014年 

     詳細を見る

  • モジュール理解のためのバグレポート推薦

    第21回ソフトウェア工学の基礎ワークショップ  2014年 

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  • ソフトウェア開発行動記録システムTaskPitの開発現場への適用

    ソフトウェアエンジニアリングシンポジウム2013 併設ワークショップ 「開発マネジメントにおける産学の問題共有と連携強化」  2013年 

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  • リポジトリマイニングの研究成果の産業化への応用

    ウィンターワークショップ2013・イン・那須  2013年 

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  • ソフトウェアリポジトリマイニングの技術動向とその応用, ITフォーラムセッション

    ソフトウェアジャパン2013  2013年 

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  • 組込みRTOSにおけるタスク起動機構冗長化とタスク起動順位保持

    システムソフトウェアとオペレーティング・システム研究会  2013年 

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  • OSS開発におけるコミッターの協調作業についての一考察

    第88回GN・第5回SPT合同研究発表会  2013年 

     詳細を見る

  • 学習データの時間的変化に伴う欠陥モジュール予測モデルの評価

    ソフトウェアエンジニアリングシンポジウム2013 ポスターセッション  2013年 

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  • OSS開発における協調作業と不具合再修正の分析

    ソフトウェアエンジニアリングシンポジウム2013 ポスターセッション  2013年 

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  • ソフトウェア開発企業における開発タスクの自動計測

    第20回ソフトウェア工学の基礎ワークショップ  2013年 

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  • 脳血流計測に基づくプログラム理解行動の定量化

    第20回ソフトウェア工学の基礎ワークショップ  2013年 

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  • オープンソースソフトウェアにおける学術論文の引用状況の分析

    第20回ソフトウェア工学の基礎ワークショップ  2013年 

     詳細を見る

  • 学習データ計測時点による欠陥モジュール予測精度の比較

    第20回ソフトウェア工学の基礎ワークショップ  2013年 

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  • OSS開発における一般開発者の協調作業と不具合の再修正に関する一考察

    ルチメディア,分散,協調とモバイル (DICOMO2013) シンポジウム  2013年 

     詳細を見る

  • 開発要員数とその誤差を考慮した工数見積もり方法

    ソフトウェア工学研究会  2013年 

     詳細を見る

  • コードの「めずらしさ」に基づく保護機構のステルシネス考察

    第11回情報科学技術フォーラム(FIT2012)  2012年 

     詳細を見る

  • あとどのくらいレビューすればよいのか?

    ウインターワークショップ2012・イン・琵琶湖  2012年 

     詳細を見る

  • Java言語を対象とした実行時多様化の試み

    2012年暗号と情報セキュリティシンポジウム  2012年 

     詳細を見る

  • 即効!開発リーダーとメンバーに役に立つソフトウェアメトリクス ~今あるデータを有効に活用するリポジトリマイニングのノウハウ~, テクニカルセッション

    Embedded Technology West 2012  2012年 

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  • 多変量予測モデルにおける目的変数の選択について

    ソフトウェアエンジニアリングシンポジウム2011 併設ワークショップ 「ソフトウェア開発マネジメントの実践と課題」  2012年 

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  • 実行時間に依存したプログラムの暗号化

    第11回情報科学技術フォーラム(FIT2012)  2012年 

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  • プログラミング学習におけるモチベーション因子と教材の関連

    日本産業技術教育学会近畿支部第29回研究発表会  2012年 

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  • 脳血流量に基づくプログラム理解行動の計測

    ソフトウェアプロジェクト研究会  2012年 

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  • 命令の乱雑さに基づくプログラム理解性の評価

    第19回ソフトウェア工学の基礎ワークショップ  2012年 

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  • バグモジュール予測を用いたテスト工数割り当て戦略

    第19回ソフトウェア工学の基礎ワークショップ  2012年 

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  • Javaソフトウエア開発における使用ライブラリの分析

    ソフトウェア信頼性研究会第8回ワークショップ  2012年 

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  • A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction

    19th Asia-Pacific Software Engineering Conference (APSEC2012)  2012年 

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受賞

  • 情報処理学会ソフトウェア工学研究会 卓越研究賞

    2018年  

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    受賞国:日本国

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  • 岡山大学工学部研究功績賞

    2018年  

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    受賞国:日本国

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  • 2017年SEC journal SEC所長賞

    2017年  

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    受賞国:日本国

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  • Best Paper Award

    2017年  

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    受賞国:日本国

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  • 2015年SEC journal SEC所長賞

    2015年  

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    受賞国:日本国

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  • 優秀論文賞

    2013年  

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    受賞国:日本国

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  • Best Paper Award

    2012年  

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    受賞国:日本国

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  • Best Short Paper Award

    2012年  

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担当授業科目

  • オートマトンと言語理論 (2021年度) 第2学期  - 火3,火4,金1,金2

  • コンピュータ科学基礎2 (2021年度) 第2学期  - その他

  • ソフトウェア分析学 (2021年度) 前期  - その他

  • ソフトウェア工学 (2021年度) 第4学期  - 月3,月4

  • ソフトウェア設計 (2021年度) 第3学期  - 月1,月2,金5,金6

  • データベース (2021年度) 第2学期  - 月1,月2,木1,木2

  • データベース論 (2021年度) 第2学期  - 月1,月2,木1,木2

  • 定量的ソフトウェア開発管理 (2021年度) 前期  - 火1~2,金5~6

  • 情報系概論 (2021年度) 特別  - その他

  • 情報系概論 (2021年度) 特別  - その他

  • 技術英語 (2021年度) 後期  - その他

  • 知能ソフトウェア基礎学演習 (2021年度) 通年  - その他

  • 表現技法1 (2021年度) 前期  - その他

  • 表現技法2 (2021年度) 後期  - その他

  • 計算機数学I (2021年度) 第2学期  - 火3,火4,金1,金2

  • 電子情報システム工学特別研究 (2021年度) 通年  - その他

  • オートマトンと言語理論 (2020年度) 第2学期  - 火3,火4,金1,金2

  • コンピュータ科学基礎1 (2020年度) 第1学期  - 金3,金4

  • コンピュータ科学基礎2 (2020年度) 第2学期  - 金3,金4

  • ソフトウェア分析学 (2020年度) 前期  - その他

  • ソフトウェア工学 (2020年度) 第4学期  - 月3,月4

  • ソフトウェア設計 (2020年度) 第3学期  - 月1,月2,金5,金6

  • データベース (2020年度) 第2学期  - 月1,月2,木1,木2

  • データベース論 (2020年度) 第2学期  - 月1,月2,木1,木2

  • プログラミング言語論 (2020年度) 第4学期  - 月3,月4

  • 定量的ソフトウェア開発管理 (2020年度) 前期  - その他

  • 情報系概論 (2020年度) 夏季集中  - その他

  • 情報系概論 (2020年度) 夏季集中  - その他

  • 技術英語 (2020年度) 後期  - その他

  • 物理学基礎(力学)1 (2020年度) 第3学期  - 火5,火6

  • 物理学基礎(力学)2 (2020年度) 第4学期  - 火5,火6

  • 物理学基礎1(力学) (2020年度) 3・4学期  - 火5,火6

  • 知能ソフトウェア基礎学演習 (2020年度) 通年  - その他

  • 表現技法1 (2020年度) 前期  - その他

  • 表現技法2 (2020年度) 後期  - その他

  • 計算機数学I (2020年度) 第2学期  - 火3,火4,金1,金2

  • 論理型言語 (2020年度) 第3学期  - 月1,月2,金5,金6

  • 電子情報システム工学特別研究 (2020年度) 通年  - その他

▼全件表示