SQAPlanner: Generating Data-Informed Software Quality Improvement Plans

1 code implementation • 19 Feb 2021 • Dilini Rajapaksha, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Christoph Bergmeir, John Grundy, Wray Buntine

Thus, our SQAPlanner paves a way for novel research in actionable software analytics-i. e., generating actionable guidance on what should practitioners do and not do to decrease the risk of having defects to support SQA planning.

Explainable AI for Software Engineering

no code implementations • 3 Dec 2020 • 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-making.

BIG-bench Machine Learning Decision Making

AutoSpearman: Automatically Mitigating Correlated Metrics for Interpreting Defect Models

no code implementations • 26 Jun 2018 • Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Christoph Treude

Through a case study of 13 publicly-available defect datasets, we find that feature selection techniques produce inconsistent subsets of metrics and do not mitigate correlated metrics, suggesting that feature selection techniques should not be used and correlation analyses must be applied when the goal is model interpretation.

feature selection