研究者詳細 - 門田　暁人

論文 - 門田　暁人

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A Cost-Effectiveness Metric for Association Rule Mining in Software Defect Prediction 査読

Kinari Nishiura, Takeki Kasagi, Akito Monden

Proc. 21st International Conference on Software Engineering Research and Practice (SERP'23) 2023年7月

担当区分：最終著者,　責任著者記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：Research Square Platform LLC

Abstract

This paper proposes a cost-effectiveness metric for association rule mining suitable for software defect prediction where conditions of defective modules are characterized as association rules.Given a certain amount of test effort (or the number of test cases), the proposed metric is the expected number of defects to be discovered in modules that meet an association rule.Since the amount test effort is limited in general and full testing of all modules is ineffective, the proposed metric is useful to focus on the most cost effective set of modules to be tested with limited test effort. The proposed metric is defined based on the exponential Software Reliability Growth Model (SRGM) extended with the module size parameter, assuming that a larger module require more effort to discover defects. To evaluate the effectiveness of the proposed metric, association rules were extracted and prioritized based on the proposed metric using data sets of four open source software projects. The LOC-based cumulative-lift chart, which is often used to evaluate the cost effectiveness of defect prediction, shows that the proposed metric can focus on the rules that can discover more defects than the conventional association rule metrics, confidence and odds ratio.

DOI： 10.21203/rs.3.rs-1988568/v1

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その他リンク： https://www.researchsquare.com/article/rs-1988568/v1.html

Artificial Neural Network Based Audio Reinforcement for Computer Assisted Rote Learning 査読

Parisa Supitayakul, Zeynep Yücel, Akito Monden

IEEE Access 11 39466 - 39483 2023年

Analysis of Programming Performance Based on 2-grams of Keystrokes and Mouse Operations. 査読

Kazuki Matsumoto, Kinari Nishiura, Mariko Sasakura, Akito Monden

Proc. 21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications (SERA2023) 301 - 306 2023年

A Dynamic Model Selection Approach to Mitigate the Change of Balance Problem in Cross-Version Bug Prediction 査読

Hiroshi Demanou, Akito Monden, Masateru Tsunoda

Proc. 10th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2022 2022年12月

Investigation of the relation between task engagement and eye gaze 査読

Shogo Hamachi, Parisa Supitayakul, Zeynep Yücel, Akito Monden

2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter) 2022年12月

Examination of the relation between affective content of images and gaze behavior 査読

Terumi Kasahara, Parisa Supitayakul, Zeynep Yücel, Akito Monden

2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter) 2022年12月

Developing a web application for RBSC-based solution of the subset selection problem 査読

Chigusa Ikeda, Parisa Supitayakul, Zeynep Yücel, Akito Monden

2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter) 2022年12月

Preliminary Analysis of Review Method Selection Based on Bandit Algorithms 査読

Takuto Kudo, Masateru Tsunoda, Amjed Tahir, Kwabena Ebo Bennin, Koji Toda, Keitaro Nakasai, Akito Monden, Kenichi Matsumoto

2022 29th Asia-Pacific Software Engineering Conference (APSEC) 2022年12月

Clone Detection Method Selection Based on Bandit Algorithm: a Preliminary Analysis 査読

Takeru Tabo, Takuto Kudo, Masateru Tsunoda, Amjed Tahir, Kwabena Ebo Bennin, Koji Toda, Keitaro Nakasai, Akito Monden

2022 IEEE 16th International Workshop on Software Clones (IWSC) 2022年10月

相関ルールとランダムフォレストを組合わせたfault-proneモジュール予測の追実験査読

瀬戸俊輝, 西浦生成, 門田暁人

情報処理学会論文誌 63 ( 8 ) 1352 - 1360 2022年8月

Improvement and Evaluation of Data Consistency Metric CIL for Software Engineering Data Sets 査読

Maohua Gan, Zeynep Yucel, Akito Monden

IEEE Access 10 70053 - 70067 2022年

Using Bandit Algorithms for Selecting Feature Reduction Techniques in Software Defect Prediction. 査読

Masateru Tsunoda, Akito Monden, Koji Toda, Amjed Tahir, Kwabena Ebo Bennin, Keitaro Nakasai, Masataka Nagura, Kenichi Matsumoto

MSR 670 - 681 2022年

Investigating the effect of various types of audio reinforcement on memory retention 査読

Parisa Supitayakul, Yücel, Misato Nose, Akito Monden

Proc. 12th International Congress on Advanced Applied Informatics (IIAI-AAI2022) 250 - 255 2022年

掲載種別：研究論文（国際会議プロシーディングス）

Most e-learning systems deliver solely visual information, even though they boast a huge potential for supporting the learners using various other capabilities (e.g. camera, speakers) of the hosting platform (i.e. computer, smart phone etc.). In this study, we focus deploying one such potential, namely audio stimuli (informative and non-informative), for supporting rote learning of different types of learning material (i.e. easy verbal, hard verbal and numerical). Our results indicate that audio stimuli do not provide a significant benefit for studying easy verbal content, but there is a big room for improvement concerning other content types (hard verbal and numerical). Interestingly, despite the general implications of dual-coding theory, human-readout of hard verbal contents is observed not to provide any significant improvement over visual-only stimuli. However, to our surprise, non-informative audio stimuli (i.e. bell sound) are observed to provide an improvement, whereas numerical content is observed to benefit in a similar way from informative and non-informative audio. Based on these results, in the future we aim developing an automatic learning support system, which triggers the appropriate audio stimuli, taking in consideration the type of content.

DOI： 10.1109/IIAIAAI55812.2022.00057

Scopus

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A computationally efficient approach for solving RBSC-based formulation of the subset selection problem 査読

Kohei Furuya, Zeynep Yücel, Parisa Supitayakul, Akito Monden

Proc. 12th International Congress on Advanced Applied Informatics (IIAI-AAI2022) 341 - 347 2022年

掲載種別：研究論文（国際会議プロシーディングス）

This study focuses on a specific type of subset selection problem, which is constrained in terms of the rank bi-serial correlation (RBSC) coefficient of the outputs. For solving such problems, we propose an approach with several advantages such as (i) providing a clear insight into the feasibility of the problem with respect to the hyper-parameters, (ii) being non-iterative, (iii) having a foreseeable running time, and (iv) with the potential to yield non-deterministic (diverse) outputs. In particular, the proposed approach is based on starting from a composition of subsets with an extreme value of the RBSC coefficient (e.g. ρ=1) and swapping certain elements of the subsets in order to adjust ρ into the desired range. The proposed method is superior to the previously proposed RBSC-SubGen, which attempts to solve the problem before confirming its feasibility, taking random steps, and has unforeseeable running times and saturation issues.

DOI： 10.1109/IIAIAAI55812.2022.00076

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Neg/pos-Normalized Accuracy Measures for Software Defect Prediction 査読

Maohua Gan, Zeynep Yücel, Akito Monden

IEEE Access 10 134580 - 134591 2022年

担当区分：最終著者,　責任著者掲載種別：研究論文（学術雑誌）

In evaluating the performance of software defect prediction models, accuracy measures such as precision and recall are commonly used. However, most of these measures are affected by neg/pos ratio of the data set being predicted, where neg is the number of negative cases (defect-free modules) and pos is the number of positive cases (defective modules). Thus, it is not fair to compare such values across different data sets with different neg/pos ratios and it may even lead to misleading or contradicting conclusions. The objective of this study is to address the class imbalance issue in assessing performance of defect prediction models. The proposed method relies on computation of expected values of accuracy measures based solely on the value of the neg and pos values of the data set. Based on the expected values, we derive the neg/pos-normalized accuracy measures, which are defined as their divergence from the expected value divided by the standard deviation of all possible prediction outcomes. The proposed measures enable us to provide a ranking of predictions across different data sets, which can distinguish between successful predictions and unsuccessful predictions. Our results derived from a case study of defect prediction based on 19 defect data sets indicate that ranking of predictions is significantly different than the ranking of conventional accuracy measures such as precision and recall as well as composite measures F1-value, AUC of ROC, MCC, G-mean and Balance. In addition, we conclude that MCC attains a better defect prediction accuracy than F1-value, AUC of ROC, G-mean and Balance.

DOI： 10.1109/ACCESS.2022.3232144

Scopus

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Gaze Analysis in Spot the Difference. 査読

Mariko Sasakura, Syouta Toda, Akito Monden

26th International Conference Information Visualisation(IV) 161 - 166 2022年

Exploring the limits of an RBSC-based approach in solving the subset selection problem 査読

Furuya Kohei, Zeynep Yücel, Parisa Supitayakul, Akito Monden, Pattara Leelaprute

International Conference on Data Science and Institutional Research (DSIR 2021) 2021年12月

On the interplay between vocal production effect and learning content types in e-learning settings 査読

Kazuma Ohta, Zeynep Yücel, Parisa Supitayakul, Akito Monden, Pattara Leelaprute

International Conference on Learning Technologies and Learning Environments (LTLE 2021) 2021年12月

ソフトウェア開発工数予測におけるauto-sklearnの適用査読

田中和也, 門田暁人, Zeynep Yücel

コンピュータソフトウェア 38 ( 4 ) 4_46 - 4_52 2021年11月

Human Resource Analysis Based on Used Libraries in Eclipse Projects on GitHub 査読

Wilson Chukwu Emmanuel, Akito Monden

Proc. 22nd IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing ( WP1-07 ) 1 - 4 2021年11月

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