The software engineering community is rapidly adopting machine learning for transitioning
modern-day software towards highly intelligent and self-learning systems. However, the …

Researchers have recently achieved significant advances in deep learning techniques,
which in turn has substantially advanced other research disciplines, such as natural …

Context: Automated software defect prediction (SDP) methods are increasingly applied,
often with the use of machine learning (ML) techniques. Yet, the existing ML-based …

Defect prediction is proposed to assist practitioners effectively prioritize limited Software
Quality Assurance (SQA) resources on the most risky files that are likely to have post-release …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

Deep learning-based software defect prediction has been popular these days. Recently, the
publishing of the CodeBERT model has made it possible to perform many software …

Software defect prediction (SDP) plays a vital role in enhancing the quality of software
projects and reducing maintenance-based risks through the ability to detect defective …

Software defects are a critical issue in software development that can lead to system failures
and cause significant financial losses. Predicting software defects is a vital aspect of …

Software defect prediction aims to find a reliable method for predicting defects in a particular
software project and assisting software engineers in allocating limited resources to release …