Related papers: Mining Bug Repositories for Multi-Fault Programs

Defect prediction has been a popular research topic where machine learning (ML) and deep learning (DL) have found numerous applications. However, these ML/DL-based defect prediction models are often limited by the quality and size of their…

In large-scale software systems, there are often no fully-fledged bug reports with human-written descriptions when an error occurs. In this case, developers rely on stack traces, i.e., series of function calls that led to the error. Since…

Regression bugs occur whenever software functionality that previously worked as desired stops working, or no longer works as expected. Code changes, such as bug fixes or new feature work, may result in a regression bug. Regression bugs are…

Software development life cycle is profoundly influenced by bugs: their introduction, identification, and eventual resolution account for a significant portion of software cost. This has motivated software engineering researchers and…

Software testing helps developers to identify bugs. However, awareness of bugs is only the first step. Finding and correcting the faulty program components is equally hard and essential for high-quality software. Fault localization…

Bug detection and prevention is one of the most important goals of software quality assurance. Nowadays, many of the major problems faced by developers can be detected or even fixed fully or partially with automatic tools. However, recent…

During testing, developers can place oracles externally or internally with respect to a method. Given a faulty execution state, i.e., one that differs from the expected one, an oracle might be unable to expose the fault if it is placed at a…

Program repair is an important but difficult software engineering problem. One way to achieve acceptable performance is to focus on classes of simple bugs, such as bugs with single statement fixes, or that match a small set of bug…

Machine Learning approaches are good in solving problems that have less information. In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly. A…

Software development teams generally welcome any effort to expose bugs in their code base. In this work, we build on the hypothesis that mobile apps from the same category (e.g., two web browser apps) may be affected by similar bugs in…

Benchmarks of bugs are essential to empirically evaluate automatic program repair tools. In this paper, we present Bears, a project for collecting and storing bugs into an extensible bug benchmark for automatic repair studies in Java. The…

Bug fixing is a complex and time-consuming task in software development. Bug localization research tends to focus on the accuracy of automated tools that suggest source code files for developers to look at. However, little is known about…

Automated Program Repair (APR) improves developer productivity by saving debugging and bug-fixing time. While APR has been extensively explored for C/C++ and Java programs, there is little research on bugs in PHP programs due to the lack of…

Refactoring is a critical process in software development, aiming at improving the internal structure of code while preserving its external behavior. Refactoring engines are integral components of modern Integrated Development Environments…

Hardware complexity continues to strain verification resources, motivating the adoption of machine learning (ML) methods to improve debug efficiency. However, ML-assisted debugging critically depends on diverse and scalable bug datasets,…

Training machine learning approaches for vulnerability identification and producing reliable tools to assist developers in implementing quality software -- free of vulnerabilities -- is challenging due to the lack of large datasets and real…

Fixing bugs in large programs is a challenging task that demands substantial time and effort. Once a bug is found, it is reported to the project maintainers, who work with the reporter to fix it and eventually close the issue. However,…

Deep learning has gained substantial popularity in recent years. Developers mainly rely on libraries and tools to add deep learning capabilities to their software. What kinds of bugs are frequently found in such software? What are the root…

Variability models (e.g., feature models) are a common way for the representation of variabilities and commonalities of software artifacts. Such models can be translated to a logical representation and thus allow different operations for…

In this paper, we present ManyTypes4Py, a large Python dataset for machine learning (ML)-based type inference. The dataset contains a total of 5,382 Python projects with more than 869K type annotations. Duplicate source code files were…