Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in
industry. However, based on well over a dozen case studies, we have learned that …

Predictive models are one of the most important techniques that are widely applied in many
areas of software engineering. There have been a large number of primary studies that …

Technical debt is a metaphor to reflect the tradeoff software engineers make between short-
term benefits and long-term stability. Self-admitted technical debt (SATD), a variant of …

Software maintainability predicts changes or failures that may occur in software after it has
been deployed. Since it deals with the degree to which an application may be understood …

Effort-aware just-in-time (JIT) defect prediction aims at finding more defective software
changes with limited code inspection cost. Traditionally, supervised models have been …

Code comments are a primary means to document source code. Keeping comments up-to-
date during code change activities requires substantial time and attention. For this reason …

Developers use bug reports to triage and fix bugs. When triaging a bug report, developers
must decide whether the bug report is valid (ie, a real bug). A large amount of bug reports …

Machine Learning (ML), including Deep Learning (DL), systems, ie, those with ML
capabilities, are pervasive in today's data-driven society. Such systems are complex; they …

Technical debt is a metaphor indicating sub-optimal solutions implemented for short-term
benefits by sacrificing the long-term maintainability and evolvability of software. A special …

Background. Self-admitted technical debt (SATD) is a special kind of technical debt that is
intentionally introduced and remarked by code comments. Those technical debts reduce the …