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In object-oriented software design, various metrics predict software systems' fault proneness. Fault predictions can considerably improve the quality of the development process and the software product. In this paper, we look at the…
Software engineering is continuously facing the challenges of growing complexity of software packages and increased level of data on defects and drawbacks from software production process. This makes a clarion call for inventions and…
Context: Automated software defect prediction (SDP) methods are increasingly applied, often with the use of machine learning (ML) techniques. Yet, the existing ML-based approaches require manually extracted features, which are cumbersome,…
Vulnerability analysis is crucial for software security. This work focuses on using pre-training techniques to enhance the understanding of vulnerable code and boost vulnerability analysis. The code understanding ability of a pre-trained…
Concurrent programs are difficult to test due to their inherent non-determinism. To address this problem, testing often requires the exploration of thread schedules of a program; this can be time-consuming when applied to real-world…
Context: Software engineering researchers have undertaken many experiments investigating the potential of software defect prediction algorithms. Unfortunately, some widely used performance metrics are known to be problematic, most notably…
Background: Software Process Simulation (SPS) has become an effective tool for software process management and improvement. However, its adoption in industry is less than what the research community expected due to the burden of measurement…
Traditional defect prediction approaches often use metrics that measure the complexity of the design or implementing code of a software system, such as the number of lines of code in a source file. In this paper, we explore a different…
Neural Networks are being integrated into safety critical systems, e.g., perception systems for autonomous vehicles, which require trained networks to perform safely in novel scenarios. It is challenging to verify neural networks because…
Code smells represent sub-optimal implementation choices applied by developers when evolving software systems. The negative impact of code smells has been widely investigated in the past: besides developers' productivity and ability to…
Background. Effort-aware metrics (EAMs) are widely used to evaluate the effectiveness of software defect prediction models, while accounting for the effort needed to analyze the software modules that are estimated defective. The usual…
Context. Software reusability mechanisms, like inheritance and delegation in Object-Oriented programming, are widely recognized as key instruments of software design. These are used to reduce the risks of source code being affected by…
Software metrics offer a quantitative basis for predicting the software development process. In this way, software quality can be improved very easily. Software quality should be achieved to satisfy the customer with decreasing the software…
Software defect prediction is an essential task during the software development Lifecycle as it can help managers to identify the most defect-proneness modules. Thus, it can reduce the test cost and assign testing resources efficiently.…
Change-prone classes or modules are defined as software components in the source code which are likely to change in the future. Change-proneness prediction is useful to the maintenance team as they can optimize and focus their testing…
Defect models are analytical models that are used to build empirical theories that are related to software quality. Prior studies often derive knowledge from such models using interpretation techniques, such as ANOVA Type-I. Recent work…
File-level defect prediction models traditionally rely on product and process metrics. While process metrics effectively complement product metrics, they often overlook commit size the number of files changed per commit despite its strong…
Due to the growing number of cyber attacks against computer systems, we need to pay special attention to the security of our software systems. In order to maximize the effectiveness, excluding the human component from this process would be…
Context: There is considerable diversity in the range and design of computational experiments to assess classifiers for software defect prediction. This is particularly so, regarding the choice of classifier performance metrics.…
Systematic application of software metric techniques can lead to significant improvements of the quality of a final software product. However, there is still the evident lack of wider utilization of software metrics techniques and tools due…