Related papers: Mining Fix Patterns for FindBugs Violations

The complexity of modern software has led to a drastic increase in the time and cost associated with detecting and rectifying software bugs. In response, researchers have explored various methods to automatically generate fixes for buggy…

Software defect datasets, which are collections of software bugs, are essential resources to facilitate empirical research and enable standardized benchmarking for a wide range of software engineering techniques, including emerging areas…

Lack of experience, inadequate documentation, and sub-optimal API design frequently cause developers to make mistakes when re-using third-party implementations. Such API misuses can result in unintended behavior, performance losses, or…

This paper is about understanding the nature of bug fixing by analyzing thousands of bug fix transactions of software repositories. It then places this learned knowledge in the context of automated program repair. We give extensive…

Conformance checking deals with collating modeled process behavior with observed process behavior recorded in event data. Alignments are a state-of-the-art technique to detect, localize, and quantify deviations in process executions, i.e.,…

For a given software bug report, identifying an appropriate developer who could potentially fix the bug is the primary task of a bug triaging process. A bug title (summary) and a detailed description is present in most of the bug tracking…

Deep learning had been used in program analysis for the prediction of hidden software defects using software defect datasets, security vulnerabilities using generative adversarial networks as well as identifying syntax errors by learning a…

Automatic program repair holds the potential of dramatically improving the productivity of programmers during the software development process and correctness of software in general. Recent advances in machine learning, deep learning, and…

The presence of missing entries in data often creates challenges for pattern recognition algorithms. Traditional algorithms for clustering data assume that all the feature values are known for every data point. We propose a method to…

Software developers attempt to reproduce software bugs to understand their erroneous behaviours and to fix them. Unfortunately, they often fail to reproduce (or fix) them, which leads to faulty, unreliable software systems. However, to…

Fixing software faults contributes significantly to the cost of software maintenance and evolution. Techniques for reducing these costs require datasets of software faults, as well as an understanding of the faults, for optimal testing and…

More and more users and developers are using Issue Tracking Systems (ITSs) to report issues, including bugs, feature requests, enhancement suggestions, etc. Different information, however, is gathered from users when issues are reported on…

Machine learning models commonly exhibit unexpected failures post-deployment due to either data shifts or uncommon situations in the training environment. Domain experts typically go through the tedious process of inspecting the failure…

Bug reports provide critical insights into software quality, yet existing datasets often suffer from limited scope, outdated content, or insufficient metadata for machine learning. To address these limitations, we present GitBugs-a…

Refactoring is a common practice in software development, aimed at improving the internal code structure in order to make it easier to understand and modify. Consequently, it is often assumed that refactoring makes the code less prone to…

Bug prediction is the process of training a machine learning model on software metrics and fault information to predict bugs in software entities. While feature selection is an important step in building a robust prediction model, there is…

Software bugs in a production environment have an undesirable impact on quality of service, unplanned system downtime, and disruption in good customer experience, resulting in loss of revenue and reputation. Existing approaches to automated…

A long-standing open challenge for automated program repair is the overfitting problem, which is caused by having insufficient or incomplete specifications to validate whether a generated patch is correct or not. Most available repair…

For software testing research, Defects4J stands out as the primary benchmark dataset, offering a controlled environment to study real bugs from prominent open-source systems. However, prior research indicates that Defects4J might include…

This paper introduces an automatic debugging framework that relies on model-based reasoning techniques to locate faults in programs. In particular, model-based diagnosis, together with an abstract interpretation based conflict detection…