138 citations · 339 across the 56 of their papers we have counts for
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SQAPlanner: Generating Data-Informed Software Quality Improvement Plans
Dilini Rajapaksha, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee +3
Software Quality Assurance (SQA) planning aims to define proactive plans, such as defining maximum file size, to prevent the occurrence of software defects in future releases. To a…
JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction
Chanathip Pornprasit, Chakkrit Tantithamthavorn
A Just-In-Time (JIT) defect prediction model is a classifier to predict if a commit is defect-introducing. Recently, CC2Vec -- a deep learning approach for Just-In-Time defect pred…
Deep Learning for Android Malware Defenses: a Systematic Literature Review
Yue Liu, Chakkrit Tantithamthavorn, Li Li +1
Malicious applications (particularly those targeting the Android platform) pose a serious threat to developers and end-users. Numerous research efforts have been devoted to develop…
Practitioners' Perceptions of the Goals and Visual Explanations of Defect Prediction Models
Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, John Grundy
Software defect prediction models are classifiers that are constructed from historical software data. Such software defect prediction models have been proposed to help developers o…
Assessing the Students' Understanding and their Mistakes in Code Review Checklists -- An Experience Report of 1,791 Code Review Checklist Questions from 394 Students
Chun Yong Chong, Patanamon Thongtanunam, Chakkrit Tantithamthavorn
Code review is a widely-used practice in software development companies to identify defects. Hence, code review has been included in many software engineering curricula at universi…