activity
20182022
most citedPredicting Defective Lines Using a Model-Agnostic Technique

138 citations · 233 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CR20224 cited

Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle

Van Nguyen, Trung Le, Chakkrit Tantithamthavorn +3

Software vulnerabilities (SVs) have become a common, serious and crucial concern due to the ubiquity of computer software. Many machine learning-based approaches have been proposed…

cs.SE20213 cited

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…

cs.SE2021

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…

cs.SE2021

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…

cs.SE20202 cited

Explainable AI for Software Engineering

Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, John Grundy

Artificial Intelligence/Machine Learning techniques have been widely used in software engineering to improve developer productivity, the quality of software systems, and decision-m…

cs.SE2020138 cited

Predicting Defective Lines Using a Model-Agnostic Technique

Supatsara Wattanakriengkrai, Patanamon Thongtanunam, Chakkrit Tantithamthavorn +2

Defect prediction models are proposed to help a team prioritize source code areas files that need Software QualityAssurance (SQA) based on the likelihood of having defects. However…