Context: Automated software defect prediction (SDP) methods are increasingly applied, often with the use of machine learning (ML) techniques. Yet, the existing ML-based …
S Feng, J Keung, X Yu, Y Xiao, M Zhang - Information and Software …, 2021 - Elsevier
Context: In practice, software datasets tend to have more non-defective instances than defective ones, which is referred to as the class imbalance problem in software defect …
C Ni, X Chen, F Wu, Y Shen, Q Gu - Journal of Systems and Software, 2019 - Elsevier
The performance of software defect prediction (SDP) models depend on the quality of considered software features. Redundant features and irrelevant features may reduce the …
Cross-project defect prediction (CPDP), aiming to apply defect prediction models built on source projects to a target project, has been an active research topic. A variety of supervised …
Defect prediction assists the rational allocation of testing resources by detecting the potentially defective software modules before releasing products. When a project has no …
X Chen, D Zhang, Y Zhao, Z Cui, C Ni - Information and Software …, 2019 - Elsevier
Context: Software defect number prediction (SDNP) can rank the program modules according to the prediction results and is helpful for the optimization of testing resource …
U Sharma, R Sadam - Journal of Systems and Software, 2023 - Elsevier
Context: Cross-project defect prediction (CPDP) models are being developed to optimise the testing resources. Objectives: Proposing an ensemble classification framework for CPDP as …