Abstract CONTEXT. Software Product Lines (SPLs) aim to support the development of a
whole family of software products through systematic reuse of shared assets. As SPLs …

Research in the area of Cyber-Physical Systems (CPS) is hampered by the lack of available
project environments in which to explore open challenges and to propose and rigorously …

The label quality of defect data sets has a direct influence on the reliability of defect
prediction models. In this paper, we conduct a systematic study of inconsistent defect labels …

Predicting software fault proneness is very important as the process of fixing these faults
after the release is very costly and time-consuming. In order to predict software fault …

This paper aims to improve software fault-proneness prediction by investigating the
unexplored effects on classification performance of the temporal decisions made by …

An transfer learning method, called Kernel Canonical Correlation Analysis plus (KCCA+), is
proposed for heterogeneous Cross-company defect prediction. Combining the kernel …

Machine learning algorithms have been used extensively for software fault proneness
prediction. This paper presents the first application of Group Lasso Regression (G-Lasso) for …

Problems with the correctness and completeness of environmental assumptions contribute
to many accidents in safety-critical systems. The problem is exacerbated when products are …

Software fault-proneness prediction is an active research area, with many factors affecting
prediction performance extensively studied. However, the impact of the learning approach …

The label quality of defect data sets has a direct influence on the reliability of defect
prediction models. In this study, for multi-version-project defect data sets, we propose an …