Improving prompt tuning-based software vulnerability assessment by fusing source code and vulnerability description

Automated Software Engineering, Journal Year: 2025, Volume and Issue: 32(2)

Published: May 3, 2025

Language: Английский

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Evaluating Large Language Models in Detecting Test Smells

Published: Sept. 30, 2024

Test smells are coding issues that typically arise from inadequate practices, a lack of knowledge about effective testing, or deadline pressures to complete projects. The presence test can negatively impact the maintainability and reliability software. While there tools use advanced static analysis machine learning techniques detect smells, these often require effort be used. This study aims evaluate capability Large Language Models (LLMs) in automatically detecting smells. We evaluated ChatGPT-4, Mistral Large, Gemini Advanced using 30 types across codebases seven different programming languages collected literature. ChatGPT-4 identified 21 17 types, while detected 15 LLMs demonstrated potential as valuable tool identifying

Language: Английский

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Towards prompt tuning-based software vulnerability assessment with continual learning

Computers & Security, Journal Year: 2024, Volume and Issue: unknown, P. 104184 - 104184

Published: Nov. 1, 2024

Language: Английский

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An empirical investigation into the capabilities of anomaly detection approaches for test smell detection

Journal of Systems and Software, Journal Year: 2024, Volume and Issue: unknown, P. 112320 - 112320

Published: Dec. 1, 2024

Language: Английский

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What’s in a Display Name? An Empirical Study on the Use of Display Names in Open-Source JUnit Tests

Published: April 20, 2024

Language: Английский

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