Context: Software engineering researchers have undertaken many experiments
investigating the potential of software defect prediction algorithms. Unfortunately some …

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

Software defect prediction is the process of developing predictive models that helps in the
early identification of defect-prone modules based on software metrics and defect data. It …

This paper proposes a novel hybrid algorithm, named Multi-Strategy Hybrid Harris Hawks
Tunicate Swarm Optimization Algorithm (MSHHOTSA). The primary objective of MSHHOTSA …

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 …

In this information era, software usage is intertwined with daily routine work and business.
Defects in software can cause a severe economic crisis. It is a crucial task in the software …

Anticipating software defects prior to the testing phase proves advantageous for efficient
resource allocation to develop the high-quality software, a necessity for any organization …

In software engineering, defect prediction is significantly important and challenging. The
main task is to predict the defect proneness of the modules. It helps developers find bugs …

Software defect prediction is a way to improve quality by finding and tracking defective
modules in the software which helps reduce costs during the software testing process. The …

Predicting defect-prone software components can play a significant role in allocating
relevant testing resources to fault-prone modules and hence increasing the business value …