Related papers: Evaluation of Cross-Lingual Bug Localization: Two …
Computational notebooks became indispensable tools for research-related development, offering unprecedented interactivity and flexibility in the development process. However, these benefits come at the cost of reproducibility and an…
Random testing has proven to be an effective technique for compiler validation. However, the debugging of bugs identified through random testing presents a significant challenge due to the frequent occurrence of duplicate test programs that…
Large language models (LLMs) have shown impressive effectiveness in various software engineering tasks, including automated program repair (APR). In this study, we take a deep dive into automated bug fixing utilizing LLMs. In contrast to…
Repositories of large software systems have become commonplace. This massive expansion has resulted in the emergence of various problems in these software platforms including identification of (i) bug-prone packages, (ii) critical bugs, and…
Learning-based bug detectors promise to find bugs in large code bases by exploiting natural hints such as names of variables and functions or comments. Still, existing techniques tend to underperform when presented with realistic bugs. We…
Software debugging is a very time-consuming process, which is even worse for multi-threaded programs, due to the non-deterministic behavior of thread-scheduling algorithms. However, the debugging time may be greatly reduced, if automatic…
Bugs are essential in software engineering; many research studies in the past decades have been proposed to detect, localize, and repair bugs in software systems. Effectiveness evaluation of such techniques requires complex bugs, i.e.,…
Managing large numbers of incoming bug reports and finding the most critical issues in hardware development is time consuming, but crucial in order to reduce development costs. In this paper, we present an approach to predict the time to…
Summarizing software artifacts is an important task that has been thoroughly researched. For evaluating software summarization approaches, human judgment is still the most trusted evaluation. However, it is time-consuming and fatiguing for…
Software bugs pose a significant challenge during development and maintenance, and practitioners spend nearly 50% of their time dealing with bugs. Many existing techniques adopt Information Retrieval (IR) to localize a reported bug using…
Our method for multi-lingual geoparsing uses monolingual tools and resources along with machine translation and alignment to return location words in many languages. Not only does our method save the time and cost of developing geoparsers…
Bug reports are often unstructured and verbose, making it challenging for developers to efficiently comprehend software issues. Existing summarization approaches typically rely on surface-level textual cues, resulting in incomplete or…
Due to its potential to improve programmer productivity and software quality, automated program repair has been an active topic of research. Newer techniques harness neural networks to learn directly from examples of buggy programs and…
Cross-lingual Summarization (CLS) aims at producing a summary in the target language for an article in the source language. Traditional solutions employ a two-step approach, i.e. translate then summarize or summarize then translate.…
Bug severity prediction is important in software maintenance, because it helps the development teams to prioritize bugs that have a significant impact on the operation, stability and security of the system. In large software projects bug…
We explore the application of Information Retrieval (IR) based bug localization methods at a large industrial setting, Facebook. Facebook's code base evolves rapidly, with thousands of code changes being committed to a monolithic repository…
Software bugs cost the global economy billions of dollars each year and take up ~50% of the development time. Once a bug is reported, the assigned developer attempts to identify and understand the source code responsible for the bug and…
Many automated tasks in software maintenance rely on information retrieval techniques to identify specific information within unstructured data. Bug localization is such a typical task, where text in a bug report is analyzed to identify…
Information Retrieval-based Bug Localization (IRBL) aims to identify buggy source files for a given bug report. Traditional and deep learning-based IRBL techniques often suffer from vocabulary mismatch and dependence on project-specific…
When a software bug is reported, developers engage in a discussion to collaboratively resolve it. While the solution is likely formulated within the discussion, it is often buried in a large amount of text, making it difficult to comprehend…