Jinqiang Yu, Michael Fu, Alexey Ignatiev, Chakkrit Tantithamthavorn, Peter Stuckey
{"title":"及时缺陷预测的形式解释器","authors":"Jinqiang Yu, Michael Fu, Alexey Ignatiev, Chakkrit Tantithamthavorn, Peter Stuckey","doi":"10.1145/3664809","DOIUrl":null,"url":null,"abstract":"<p>Just-In-Time (JIT) defect prediction has been proposed to help teams to prioritize the limited resources on the most risky commits (or pull requests), yet it remains largely a black-box, whose predictions are not explainable nor actionable to practitioners. Thus, prior studies have applied various model-agnostic techniques to explain the predictions of JIT models. Yet, explanations generated from existing model-agnostic techniques are still not formally sound, robust, and actionable. In this paper, we propose <span>FoX</span>, a <underline>Fo</underline>rmal e<underline>X</underline>plainer for JIT Defect Prediction, which builds on formal reasoning about the behaviour of JIT defect prediction models and hence is able to provide provably correct explanations, which are additionally guaranteed to be minimal. Our experimental results show that <span>FoX</span> is able to efficiently generate provably-correct, robust, and actionable explanations while existing model-agnostic techniques cannot. Our survey study with 54 software practitioners provides valuable insights into the usefulness and trustworthiness of our <span>FoX</span> approach. 86% of participants agreed that our approach is useful, while 74% of participants found it trustworthy. Thus, this paper serves as an important stepping stone towards trustable explanations for JIT models to help domain experts and practitioners better understand why a commit is predicted as defective and what to do to mitigate the risk.</p>","PeriodicalId":50933,"journal":{"name":"ACM Transactions on Software Engineering and Methodology","volume":"11 1","pages":""},"PeriodicalIF":6.6000,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Formal Explainer for Just-In-Time Defect Predictions\",\"authors\":\"Jinqiang Yu, Michael Fu, Alexey Ignatiev, Chakkrit Tantithamthavorn, Peter Stuckey\",\"doi\":\"10.1145/3664809\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Just-In-Time (JIT) defect prediction has been proposed to help teams to prioritize the limited resources on the most risky commits (or pull requests), yet it remains largely a black-box, whose predictions are not explainable nor actionable to practitioners. Thus, prior studies have applied various model-agnostic techniques to explain the predictions of JIT models. Yet, explanations generated from existing model-agnostic techniques are still not formally sound, robust, and actionable. In this paper, we propose <span>FoX</span>, a <underline>Fo</underline>rmal e<underline>X</underline>plainer for JIT Defect Prediction, which builds on formal reasoning about the behaviour of JIT defect prediction models and hence is able to provide provably correct explanations, which are additionally guaranteed to be minimal. Our experimental results show that <span>FoX</span> is able to efficiently generate provably-correct, robust, and actionable explanations while existing model-agnostic techniques cannot. Our survey study with 54 software practitioners provides valuable insights into the usefulness and trustworthiness of our <span>FoX</span> approach. 86% of participants agreed that our approach is useful, while 74% of participants found it trustworthy. Thus, this paper serves as an important stepping stone towards trustable explanations for JIT models to help domain experts and practitioners better understand why a commit is predicted as defective and what to do to mitigate the risk.</p>\",\"PeriodicalId\":50933,\"journal\":{\"name\":\"ACM Transactions on Software Engineering and Methodology\",\"volume\":\"11 1\",\"pages\":\"\"},\"PeriodicalIF\":6.6000,\"publicationDate\":\"2024-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Software Engineering and Methodology\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1145/3664809\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, SOFTWARE ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Software Engineering and Methodology","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3664809","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
A Formal Explainer for Just-In-Time Defect Predictions
Just-In-Time (JIT) defect prediction has been proposed to help teams to prioritize the limited resources on the most risky commits (or pull requests), yet it remains largely a black-box, whose predictions are not explainable nor actionable to practitioners. Thus, prior studies have applied various model-agnostic techniques to explain the predictions of JIT models. Yet, explanations generated from existing model-agnostic techniques are still not formally sound, robust, and actionable. In this paper, we propose FoX, a Formal eXplainer for JIT Defect Prediction, which builds on formal reasoning about the behaviour of JIT defect prediction models and hence is able to provide provably correct explanations, which are additionally guaranteed to be minimal. Our experimental results show that FoX is able to efficiently generate provably-correct, robust, and actionable explanations while existing model-agnostic techniques cannot. Our survey study with 54 software practitioners provides valuable insights into the usefulness and trustworthiness of our FoX approach. 86% of participants agreed that our approach is useful, while 74% of participants found it trustworthy. Thus, this paper serves as an important stepping stone towards trustable explanations for JIT models to help domain experts and practitioners better understand why a commit is predicted as defective and what to do to mitigate the risk.
期刊介绍:
Designing and building a large, complex software system is a tremendous challenge. ACM Transactions on Software Engineering and Methodology (TOSEM) publishes papers on all aspects of that challenge: specification, design, development and maintenance. It covers tools and methodologies, languages, data structures, and algorithms. TOSEM also reports on successful efforts, noting practical lessons that can be scaled and transferred to other projects, and often looks at applications of innovative technologies. The tone is scholarly but readable; the content is worthy of study; the presentation is effective.