研究者詳細 - 門田　暁人

MISC - 門田　暁人

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キーストロークとマウス操作に基づくプログラミング能力の分析査読

松本和樹, 西浦生成, 笹倉万里子, 門田暁人

ソフトウェア工学の基礎29, ソフトウェア工学の基礎ワークショップFOSE2022 165 - 170 2022年11月

提案依頼書におけるセキュリティ要件の実態調査査読

村越竜介, 西浦生成, 笹倉万里子, 門田暁人

ソフトウェア工学の基礎29, ソフトウェア工学の基礎ワークショップFOSE2022 229 - 230 2022年11月

育成の観点を取り入れたプロジェクト管理ゲーム査読

山形宥太, 西浦生成, 笹倉万里子, 門田暁人

ソフトウェア工学の基礎29, ソフトウェア工学の基礎ワークショップFOSE2022 205 - 206 2022年11月

Fault-proneモジュール予測における第三者データに基づいた外れ値除去査読

西浦生成, 門田暁人

ソフトウェア工学の基礎29, ソフトウェア工学の基礎ワークショップFOSE2022 153 - 158 2022年11月

ソフトウェア開発者の信頼度の評価に向けて査読

池田海斗, 西浦生成, 笹倉万里子, 門田暁人

ソフトウェア工学の基礎29, ソフトウェア工学の基礎ワークショップFOSE2022 215 - 216 2022年10月

データ断片からのソフトウェア開発データ復元の実験評価

西脇将樹, 門田暁人, 笹倉万里子, 西浦生成

信学技報 121 ( 416 ) 60 - 65 2022年3月

ソフトウェアテストの途中結果を用いたバグ予測

廣瀬椋一, 門田暁人, 角田雅照, 笹倉万里子, 西浦生成

信学技報 121 ( 416 ) 19 - 24 2022年3月

From Academia to Software Development: Publication Citations in Source Code Comments

Akira Inokuchi, Yusuf Sulistyo Nugroho, Fumiaki Konishi, Hideaki Hata, Akito Monden, Kenichi Matsumoto

2019年10月

掲載種別：機関テクニカルレポート，技術報告書，プレプリント等

Academic publications have been evaluated with the impact on research 
communities based on the number of citations. On the other hand, the impact of 
academic publications on industry has been rarely studied. This paper 
investigates how academic publications contribute to software development by 
analyzing publication citations in source code comments in open source software 
repositories. We propose an automated approach of detecting academic 
publications based on Named Entity Recognition, and achieve 0.90 in $F_1$ as 
detection accuracy. We conduct a large-scale study of publication citations 
with 319,438,977 comments collected from active 25,925 repositories written in 
seven programming languages. Our findings indicate that academic publications 
can be knowledge sources of software development, and there can be potential 
issues of obsoleting knowledge.

arXiv

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Estimation of affect scores accounting for user responsiveness

NGUYEN Hoang, KOYAMA Serina, YUCEL Zeynep, MONDEN Akito, SASAKURA Mariko

日本ロボット学会学術講演会予稿集(CD-ROM) 36th ROMBUNNO.1I2‐01 2018年9月

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

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

Scopus

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N-gram IDFによるバグレポートの分類の試行

神吉, 裕次, 門田, 暁人, 畑, 秀明

ウィンターワークショップ2018・イン・宮島論文集 2018 34 - 35 2018年1月

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

Predictability Classification for Software Effort Estimation

Naoki Kinoshita, Akito Monden, Masateru Tsunoda, Zeynep Yucel

2018 IEEE/ACIS 3RD INTERNATIONAL CONFERENCE ON BIG DATA, CLOUD COMPUTING, DATA SCIENCE & ENGINEERING (BCD 2018) 43 - 48 2018年

記述言語：英語出版者・発行元：IEEE

In this paper, focusing on the problem that estimation accuracy of software development effort greatly varies among software projects, we propose a predictability classification method for software projects before conducting effort estimation. In the proposed method, given a project to be estimated, we first evaluate whether the effort can be accurately estimated or not by identifying the project as "predictable" or "unpredictable". In case of predictable projects, we conduct the effort estimation. Otherwise, estimation is avoided. As a result of an experiment to assess the effectiveness of the proposed method using six industry datasets, (i) the mean square residual and residual variance are shown to be suitable measures for recognition of predictability; and (ii) the average absolute error is significantly reduced in five datasets, by avoiding the estimation when a project belongs to the unpredictable class, which proves the effectiveness of the proposed method. By using the proposed method, practitioners become aware of cases when they can rely on the estimation and when they cannot.

DOI： 10.1109/BCD2018.2018.00015

Web of Science

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Extended Association Rule Mining with Correlation Functions

Hidekazu Saito, Akito Monden, Zeynep Yucel

2018 IEEE/ACIS 3RD INTERNATIONAL CONFERENCE ON BIG DATA, CLOUD COMPUTING, DATA SCIENCE & ENGINEERING (BCD 2018) 79 - 84 2018年

記述言語：英語出版者・発行元：IEEE

This paper proposes extended association rule mining that can deal with correlation functions. The extended association rule is expressed in the form of: A double right arrow Correl(X; Y) where Correl(X; Y) is a correlation function with two variables X and Y. By this extension, data analysts can discover the condition A that lead to low (or high) correlation between two given variables from a large dataset. In order to show the efficacy of the proposed method, a case study is performed on an industry dataset of software developments, assuming the scenario of discovering a condition, where software development effort is predictable (or unpredictable) from the size of the project, i.e. there exists a significantly high (or low) correlation between size and effort. Since such a condition cannot be obtained by conventional association rule mining, we confirm the efficiency of the proposed extended association rule mining.

DOI： 10.1109/BCD2018.2018.00020

Web of Science

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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

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

Scopus

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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

© 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

Scopus

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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

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

Scopus

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Calibration of distributed multimodal sensor networks using cross‐correlation of arrival processes

SUPITAYAKUL Parisa, YUCEL Zeynep, ZANLUNGO Francesco, MONDEN Akito, LEELAPRUTE Pattara

日本ロボット学会学術講演会予稿集(CD-ROM) 35th ROMBUNNO.1C3‐05 2017年9月

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)

住吉倫明, 門田暁人

電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 116 ( 426 ) 95 - 100 2017年1月

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)

松井聖, 門田暁人

電子情報通信学会技術研究報告 = IEICE technical report : 信学技報 116 ( 426 ) 101 - 106 2017年1月

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