Recent years have experienced sustained focus in research on software defect prediction
that aims to predict the likelihood of software defects. Moreover, with the increased interest …

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

Wind turbines play a crucial role in renewable energy generation systems and are frequently
exposed to challenging operational environments. Monitoring and diagnosing potential …

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

Context: Static Application Security Testing Tools (SASTTs) identify software vulnerabilities
to support the security and reliability of software applications. Interestingly, several studies …

Abstract Unsupervised Effort-Aware Defect P rediction (EADP) uses unlabeled data to
construct a model and ranks software modules according to the software feature values. Xu …

Software defect prediction (SDP) plays a significant role in detecting the most likely defective
software modules and optimizing the allocation of testing resources. In practice, though …

Software reuse is a popular way to utilize existing software components to ensure the quality
of newly developed software in service-oriented architecture. However, how to find a …

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 …

Software crashes occur when the software program is executed wrongly or interrupted
compulsively, which negatively impacts on user experience. Since the stack traces offer the …