Design choices made by LLM-based test generators prevent them from finding bugs

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Detaylı Bibliyografya
Yayımlandı:arXiv.org (Dec 18, 2024), p. n/a
Yazar: Noble Saji Mathews
Diğer Yazarlar: Nagappan, Meiyappan
Baskı/Yayın Bilgisi:
Cornell University Library, arXiv.org
Konular:
Online Erişim:Citation/Abstract
Full text outside of ProQuest
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022 |a 2331-8422 
035 |a 3147264606 
045 0 |b d20241218 
100 1 |a Noble Saji Mathews 
245 1 |a Design choices made by LLM-based test generators prevent them from finding bugs 
260 |b Cornell University Library, arXiv.org  |c Dec 18, 2024 
513 |a Working Paper 
520 3 |a There is an increasing amount of research and commercial tools for automated test case generation using Large Language Models (LLMs). This paper critically examines whether recent LLM-based test generation tools, such as Codium CoverAgent and CoverUp, can effectively find bugs or unintentionally validate faulty code. Considering bugs are only exposed by failing test cases, we explore the question: can these tools truly achieve the intended objectives of software testing when their test oracles are designed to pass? Using real human-written buggy code as input, we evaluate these tools, showing how LLM-generated tests can fail to detect bugs and, more alarmingly, how their design can worsen the situation by validating bugs in the generated test suite and rejecting bug-revealing tests. These findings raise important questions about the validity of the design behind LLM-based test generation tools and their impact on software quality and test suite reliability. 
653 |a Software reliability 
653 |a Debugging 
653 |a Large language models 
653 |a Software testing 
700 1 |a Nagappan, Meiyappan 
773 0 |t arXiv.org  |g (Dec 18, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3147264606/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2412.14137