An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models

Software analytics have empowered software organisations to support a wide range of improved decision-making and policy-making. However, such predictions made by software analytics to date have not been explained and justified. Specifically, current defect prediction models still fail to explain why...

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Vydáno v:	IEEE transactions on software engineering Ročník 48; číslo 1; s. 166 - 185
Hlavní autoři:	Jiarpakdee, Jirayus, Tantithamthavorn, Chakkrit Kla, Dam, Hoa Khanh, Grundy, John
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.01.2022 IEEE Computer Society
Témata:	Algorithms Analytical models Decision analysis Decision making defect prediction models Electric breakdown Empirical analysis Explainable software analytics Lime model-agnostic techniques Open source software Optimization Prediction algorithms Prediction models Predictive models Quality control Software Software algorithms Software engineering software quality assurance
ISSN:	0098-5589, 1939-3520
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Abstract	Software analytics have empowered software organisations to support a wide range of improved decision-making and policy-making. However, such predictions made by software analytics to date have not been explained and justified. Specifically, current defect prediction models still fail to explain why models make such a prediction and fail to uphold the privacy laws in terms of the requirement to explain any decision made by an algorithm. In this paper, we empirically evaluate three model-agnostic techniques, i.e., two state-of-the-art Local Interpretability Model-agnostic Explanations technique (LIME) and BreakDown techniques, and our improvement of LIME with Hyper Parameter Optimisation (LIME-HPO). Through a case study of 32 highly-curated defect datasets that span across 9 open-source software systems, we conclude that (1) model-agnostic techniques are needed to explain individual predictions of defect models; (2) instance explanations generated by model-agnostic techniques are mostly overlapping (but not exactly the same) with the global explanation of defect models and reliable when they are re-generated; (3) model-agnostic techniques take less than a minute to generate instance explanations; and (4) more than half of the practitioners perceive that the contrastive explanations are necessary and useful to understand the predictions of defect models. Since the implementation of the studied model-agnostic techniques is available in both Python and R, we recommend model-agnostic techniques be used in the future.
AbstractList	Software analytics have empowered software organisations to support a wide range of improved decision-making and policy-making. However, such predictions made by software analytics to date have not been explained and justified. Specifically, current defect prediction models still fail to explain why models make such a prediction and fail to uphold the privacy laws in terms of the requirement to explain any decision made by an algorithm. In this paper, we empirically evaluate three model-agnostic techniques, i.e., two state-of-the-art Local Interpretability Model-agnostic Explanations technique (LIME) and BreakDown techniques, and our improvement of LIME with Hyper Parameter Optimisation (LIME-HPO). Through a case study of 32 highly-curated defect datasets that span across 9 open-source software systems, we conclude that (1) model-agnostic techniques are needed to explain individual predictions of defect models; (2) instance explanations generated by model-agnostic techniques are mostly overlapping (but not exactly the same) with the global explanation of defect models and reliable when they are re-generated; (3) model-agnostic techniques take less than a minute to generate instance explanations; and (4) more than half of the practitioners perceive that the contrastive explanations are necessary and useful to understand the predictions of defect models. Since the implementation of the studied model-agnostic techniques is available in both Python and R, we recommend model-agnostic techniques be used in the future.
Author	Jiarpakdee, Jirayus Tantithamthavorn, Chakkrit Kla Dam, Hoa Khanh Grundy, John
Author_xml	– sequence: 1 givenname: Jirayus orcidid: 0000-0002-2907-915X surname: Jiarpakdee fullname: Jiarpakdee, Jirayus email: jirayus.jiarpakdee@monash.edu organization: Faculty of Information Technology, Monash University, Clayton, VIC, Australia – sequence: 2 givenname: Chakkrit Kla orcidid: 0000-0002-5516-9984 surname: Tantithamthavorn fullname: Tantithamthavorn, Chakkrit Kla email: chakkrit@monash.edu organization: Faculty of Information Technology, Monash University, Clayton, VIC, Australia – sequence: 3 givenname: Hoa Khanh orcidid: 0000-0003-4246-0526 surname: Dam fullname: Dam, Hoa Khanh email: hoa@uow.edu.au organization: Faculty of Engineering and Information Sciences, School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia – sequence: 4 givenname: John orcidid: 0000-0003-4928-7076 surname: Grundy fullname: Grundy, John email: john.grundy@monash.edu organization: Faculty of Information Technology, Monash University, Clayton, VIC, Australia
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Snippet	Software analytics have empowered software organisations to support a wide range of improved decision-making and policy-making. However, such predictions made...
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SubjectTerms	Algorithms Analytical models Decision analysis Decision making defect prediction models Electric breakdown Empirical analysis Explainable software analytics Lime model-agnostic techniques Open source software Optimization Prediction algorithms Prediction models Predictive models Quality control Software Software algorithms Software engineering software quality assurance
Title	An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models
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