Large language models for code (LLM4Code), which demonstrate strong performance (eg,
high accuracy) in processing source code, have significantly transformed software …

Context: Software defect prediction is crucial for prioritising quality assurance tasks,
however, there are still limitations to the use of defect models. For example, the outputs often …

Machine Learning (ML) based defect prediction models can be used to improve the
reliability and overall quality of software systems. However, such defect predictors might not …

Abstract Just-In-Time (JIT) defect prediction aims to identify defects early, at commit time.
Hence, developers can take precautions to avoid defects when the code changes are still …

Defect reduction planning plays a vital role in enhancing software quality and minimizing
software maintenance costs. By training a black box machine learning model and …

Explainability is one of the most investigated quality attributes and nowadays, it has an
increasing interest of the stakeholders using Artificial Intelligence (AI), especially Machine …

Software defect prediction is the process of identifying defective files and modules that need
rigorous testing. In the literature, several secondary studies including systematic reviews …

Predicting the number of defects in a project is critical for project test managers to allocate
budget, resources, and schedule for testing, support and maintenance efforts. Software …

Just-in-Tim e (JIT) defect prediction has been proposed to help teams prioritize the limited
resources on the most risky commits (or pull requests), yet it remains largely a black box …

Explainable software defect prediction is practical for software quality assurance. However, it
is hard to explain the predictions made by obscure machine learning models because of the …