Abstract Code Smell Detection (CSD) plays a crucial role in improving software quality and
maintainability. And Deep Learning (DL) techniques have emerged as a promising …

Background: Software defect prediction models aim at identifying the potential faulty
modules of a software project based on historical data collected from previous versions of …

Context: Most defect prediction methods consider a series of traditional manually designed
static code metrics. However, only using these hand-crafted features is impractical. Some …

With the increasing utilization of large language models such as ChatGPT during software
development, it has become crucial to verify the quality of code content it generates. Recent …

Context: Cross-project defect prediction (CPDP) models are being developed to optimise the
testing resources. Objectives: Proposing an ensemble classification framework for CPDP as …

Software defect prediction recommends the most defect-prone software modules for
optimization of the test resource allocation. The limitation of the extensively-studied …

Cross-project defect prediction (CPDP) refers to recognizing defective software modules in
one project (ie, target) using historical data collected from other projects (ie, source), which …

For construction enterprises, efficient knowledge sharing among projects not only effectively
improves enterprise technology, level of management and competitiveness, but also …

Abstract Context: Cross-Project Defect Prediction (CPDP) aims to leverage the knowledge
from label-rich source software projects to promote tasks in a label-poor target software …

Just-in-time (JIT) bug prediction is an effective quality assurance activity that identifies
whether a code commit will introduce bugs into the mobile app, aiming to provide prompt …