Faculty Profiles - MONDEN Akito

MISC - MONDEN Akito

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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

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

Language：English Publisher：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

Web of Science

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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

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

Language：English Publisher：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

Language：English Publisher：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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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

Language：English Publisher：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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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 ( 11 ) 32 - 37 2012

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

Language：English Publisher：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.

DOI： 10.1145/2372251.2372267

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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

Language：English

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

Language：English

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

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

An empirical evaluation of outlier deletion methods for analogy-based cost estimation Reviewed

Masateru Tsunoda, Akito Monden, Takeshi Kakimoto, Ken-ichi Matsumoto

Proceedings of the 7th International Conference on Predictive Models in Software Engineering 17 1 - 10 2011.9

Reduction of Association Rules for Fault-prone Module Detection

2011 ( 20 ) 1 - 6 2011.3

D-3-4 An Approach of Software Development Effort Estimation by Random Forests

Konishi Fumiaki, Uchida Shinji, Toda Koji, Monden Akito

Proceedings of the IEICE General Conference 2011 ( 1 ) 2011.2

B-18-2 A Study on Test Case Allocation by Fault-prone Module Detection

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

Proceedings of the Society Conference of IEICE 2 406 - 406 2011

Applying Outlier Deletion to Analosgy Based Cost Estimation Reviewed

Masateru Tsunoda, Akito Monden, Mizuho Watanabe, Takeshi Kakimoto, Ken-ichi Matsumoto

In Proceedings of International Workshop on Empirical Software Engineering in Practice 13 - 18 2010.12

Working Time Measurement for Improving Awareness in Deskwork

SHIMOMURA YOSUKE, KURAMOTO TATSUYA, MONDEN AKITO, MATSUMOTO KEN-ICHI

2010 ( 1 ) 1 - 8 2010.3

An Analysis of Relationship between Coding Style Metrics and Source Code Plagiarism

TAKEDA TAKAYUKI, USHIMADO TOMOYOSHI, YAMAUCHI HIROKI, MONDEN AKITO, MATSUMOTO KEN-ICHI

2010 ( 8 ) 1 - 8 2010.3

Influence of Imbalance Data toward Cost Overrun Project Prediction

SHIBATA JUN-ICHIRO, TSUNODA MASATERU, MONDEN AKITO, MATSUMOTO KEN-ICHI

2010 ( 36 ) 1 - 6 2010.3

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