Related papers: Identifying non-natural language artifacts in bug …

Machine learning models can reach high performance on benchmark natural language processing (NLP) datasets but fail in more challenging settings. We study this issue when a pre-trained model learns dataset artifacts in natural language…

We show that large pre-trained language models are inherently highly capable of identifying label errors in natural language datasets: simply examining out-of-sample data points in descending order of fine-tuned task loss significantly…

Automatically locating a bug within a large codebase remains a significant challenge for developers. Existing techniques often struggle with generalizability and deployment due to their reliance on application-specific data and large model…

Effective prioritization of issue reports is crucial in software engineering to optimize resource allocation and address critical problems promptly. However, the manual classification of issue reports for prioritization is laborious and…

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…

Much recent work in NLP has documented dataset artifacts, bias, and spurious correlations between input features and output labels. However, how to tell which features have "spurious" instead of legitimate correlations is typically left…

The recent advancement of artificial intelligence, especially machine learning (ML), has significantly impacted software engineering research, including bug report analysis. ML aims to automate the understanding, extraction, and correlation…

Developers are increasingly overwhelmed by AI-generated issue reports that lack actionability and reproducibility, eroding trust in automated bug detection tools. In this paper, we present IssueSpecter, an automated tool that finds bugs in…

Tracking user reported bugs requires considerable engineering effort in going through many repetitive reports and assigning them to the correct teams. This paper proposes a neural architecture that can jointly (1) detect if two bug reports…

Large Language Models (LLMs) have demonstrated strong natural language processing and code synthesis capabilities, which has led to their rapid adoption in software engineering applications. However, details about LLM training data are…

Bugs are inescapable during software development due to frequent code changes, tight deadlines, etc.; therefore, it is important to have tools to find these errors. One way of performing bug identification is to analyze the characteristics…

Software quality assurance remains a major challenge in industrial environments, where large-scale and long-lived systems inevitably accumulate defects. Identifying the location of a fault is often time-consuming and costly, particularly…

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…

Static analysis is one of the most widely adopted techniques to find software bugs before code is put in production. Designing and implementing effective and efficient static analyses is difficult and requires high expertise, which results…

Many users and contributors of large open-source projects report software defects or enhancement requests (known as bug reports) to the issue-tracking systems. However, they sometimes report issues that have already been reported. First,…

As software systems evolve, patches may unintentionally alter program behavior. Validating patches against their intended semantics is difficult due to incomplete regression tests and informal, non-executable natural language (NL)…

The volume of scientific publications in organizational research becomes exceedingly overwhelming for human researchers who seek to timely extract and review knowledge. This paper introduces natural language processing (NLP) models to…

In NLP, models are usually evaluated by reporting single-number performance scores on a number of readily available benchmarks, without much deeper analysis. Here, we argue that - especially given the well-known fact that benchmarks often…

Consumers often face inconsistent product quality, particularly when identical products vary between markets, a situation known as the dual quality problem. To identify and address this issue, automated techniques are needed. This paper…

Software maintenance and evolution involves critical activities for the success of software projects. To support such activities and keep code up-to-date and error-free, software communities make use of issue trackers, i.e., tools for…