Related papers: FlakyFix: Using Large Language Models for Predicti…

Automatically detecting software failures is an important task and a longstanding challenge. It requires finding failure-inducing test cases whose test input can trigger the software's fault, and constructing an automated oracle to detect…

As Large Language Models shift the programming toward human-guided ''vibe coding'', agentic coding tools increasingly rely on models to self-diagnose and repair their own subtle faults -- a capability central to autonomous software…

Converting deep learning models between frameworks is a common step to maximize model compatibility across devices and leverage optimization features that may be exclusively provided in one deep learning framework. However, this conversion…

Test flakiness is a common problem in industry, which hinders the reliability of automated build and testing workflows. Most existing research on test flakiness has primarily focused on unit and small-scale integration tests. In contrast,…

While language models are increasingly more proficient at code generation, they still frequently generate incorrect programs. Many of these programs are obviously wrong, but others are more subtle and pass weaker correctness checks such as…

This study explores the potential of Large Language Models (LLMs) in automating the repair of C programs. We present a framework that integrates spectrum-based fault localization (SBFL), runtime feedback, and Chain-of-Thought-structured…

Large language models are increasingly used for code generation and debugging, but their outputs can still contain bugs, that originate from training data. Distinguishing whether an LLM prefers correct code, or a familiar incorrect version…

Software defects are a major threat to the reliability of computer systems. The literature shows that more than 30% of bug reports submitted in large software projects are misclassified (i.e., are feature requests, or mistakes made by the…

The rapid advancement of large language models (LLMs) such as GPT-4 has revolutionized the landscape of software engineering, positioning these models at the core of modern development practices. As we anticipate these models to evolve into…

Fallacies are used as seemingly valid arguments to support a position and persuade the audience about its validity. Recognizing fallacies is an intrinsically difficult task both for humans and machines. Moreover, a big challenge for…

Continuous Integration (CI) is widely used to provide rapid feedback on code changes; however, CI build outcomes are not always reliable. Builds may fail intermittently due to non-deterministic factors, leading to flaky builds that…

Context: Learning-based automatic program repair techniques are showing promise to provide quality fix suggestions for detected bugs in the source code of the software. These tools mostly exploit historical data of buggy and fixed code…

Context: When an application evolves, some of the developed test cases break. Discarding broken test cases causes a significant waste of effort and leads to test suites that are less effective and have lower coverage. Test repair approaches…

White-box test generator tools rely only on the code under test to select test inputs, and capture the implementation's output as assertions. If there is a fault in the implementation, it could get encoded in the generated tests. Tool…

Large language models such as Codex, have shown the capability to produce code for many programming tasks. However, the success rate of existing models is low, especially for complex programming tasks. One of the reasons is that language…

Current Large Language Models (LLMs) have advanced automated unit test generation but face a critical limitation: they often neglect to construct the necessary test fixtures, which are the environmental setups required for a test to run. To…

In this paper, we present a challenging code reasoning task: vulnerability detection. Large Language Models (LLMs) have shown promising results in natural-language and math reasoning, but state-of-the-art (SOTA) models reported only 54.5%…

Flaky tests pose a significant issue for software testing. A test with a non-deterministic outcome may undermine the reliability of the testing process, making tests untrustworthy. Previous research has identified test order dependency as…

Identifying and resolving logic errors can be one of the most frustrating challenges for novices programmers. Unlike syntax errors, for which a compiler or interpreter can issue a message, logic errors can be subtle. In certain conditions,…

Large Language Models (LLMs) are of great interest in vulnerability detection and repair. The effectiveness of these models hinges on the quality of the datasets used for both training and evaluation. Our investigation reveals that a number…