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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…
A fundamental unit of work in programming is the code contribution ("commit") that a developer makes to the code base of the project in work. An author's commit frequency describes how often that author commits. Knowing the distribution of…
Two key contributions presented in this paper are: i) A method for building a dataset containing source code features extracted from source files taken from Open Source Software (OSS) and associated bug reports, ii) A predictive model for…
Context: Code refactoring is widely recognized as an essential software engineering practice that improves the understandability and maintainability of source code. Several studies attempted to detect refactoring activities through mining…
This paper illustrates an empirical study of the working efficiency of machine learning techniques in classifying code review text by semantic meaning. The code review comments from the source control repository in GitHub were extracted for…
Accurate classification of software bugs is essential for improving software quality. This paper presents a rule-based automated framework for classifying issues in quantum software repositories by bug type, category, severity, and impacted…
In an era shaped by Generative Artificial Intelligence for code generation and the rising adoption of Python-based Machine Learning systems (MLS), software quality has emerged as a major concern. As these systems grow in complexity and…
Code quality remains an abstract concept that fails to get traction at the business level. Consequently, software companies keep trading code quality for time-to-market and new features. The resulting technical debt is estimated to waste up…
Code comments are essential for clarifying code functionality, improving readability, and facilitating collaboration among developers. Despite their importance, comments often become outdated, leading to inconsistencies with the…
Context: GitHub hosts an impressive number of high-quality OSS projects. However, selecting "the right tool for the job" is a challenging task, because we do not have precise information about those high-quality projects. Objective: In this…
Peer code review locates common coding rule violations and simple logical errors in the early phases of software development, and thus reduces overall cost. However, in GitHub, identifying an appropriate code reviewer for a pull request is…
Just-in-Time software defect prediction (JIT-SDP) plays a critical role in prioritizing risky code changes during code review and continuous integration. However, existing datasets often suffer from noisy labels and low precision in…
Recent progress in automated repair of performance bugs demands realistic, executable benchmarks. However, existing C++ performance benchmarks are largely built from competitive programming submissions, and recent real-world benchmarks…
Version control relies on commit messages to convey the rationale for code changes, but these messages are often low quality and, more critically, inconsistent with their diffs-known as message-code inconsistency (MCI). MCIs mislead…
This work compares the overhead of quantum error correction with concatenated and topological quantum error-correcting codes. To perform a numerical analysis, we use the Quantum Resource Estimator Toolbox (QuRE) that we recently developed.…
Many researchers assume that, for software analytics, "more data is better." We write to show that, at least for learning defect predictors, this may not be true. To demonstrate this, we analyzed hundreds of popular GitHub projects. These…
Defect prediction can be a powerful tool to guide the use of quality assurance resources. In recent years, many researchers focused on the problem of Cross-Project Defect Prediction (CPDP), i.e., the creation of prediction models based on…
Regression test case prioritization (RTCP) aims to improve the rate of fault detection by executing more important test cases as early as possible. Various RTCP techniques have been proposed based on different coverage criteria. Among them,…
Prior literature has suggested that in many projects 80\% or more of the contributions are made by a small called group of around 20% of the development team. Most prior studies deprecate a reliance on such a small inner group of "heroes",…
The large language model (LLM) ChatGPT's quality scores for journal articles correlate more strongly with human judgements than some citation-based indicators in most fields. Averaging multiple ChatGPT scores improves the results,…