Towards Autonomous Testing Agents via Conversational Large Language Models

Software testing is an important part of the development cycle, yet it requires specialized expertise and substantial developer effort to adequately test software. Recent discoveries of the capabilities of large language models (LLMs) suggest that they can be used as automated testing assistants, an...

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Vydáno v:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] s. 1688 - 1693
Hlavní autoři: Feldt, Robert, Kang, Sungmin, Yoon, Juyeon, Yoo, Shin
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 11.09.2023
Témata:
artificial intelligence
Automation
Drives
large language model
machine learning
Middleware
Oral communication
Software testing
Taxonomy
test automation
Testing
ISSN:2643-1572
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Abstract Software testing is an important part of the development cycle, yet it requires specialized expertise and substantial developer effort to adequately test software. Recent discoveries of the capabilities of large language models (LLMs) suggest that they can be used as automated testing assistants, and thus provide helpful information and even drive the testing process. To highlight the potential of this technology, we present a taxonomy of LLM-based testing agents based on their level of autonomy, and describe how a greater level of autonomy can benefit developers in practice. An example use of LLMs as a testing assistant is provided to demonstrate how a conversational framework for testing can help developers. This also highlights how the often criticized "hallucination" of LLMs can be beneficial for testing. We identify other tangible benefits that LLM-driven testing agents can bestow, and also discuss potential limitations.
AbstractList Software testing is an important part of the development cycle, yet it requires specialized expertise and substantial developer effort to adequately test software. Recent discoveries of the capabilities of large language models (LLMs) suggest that they can be used as automated testing assistants, and thus provide helpful information and even drive the testing process. To highlight the potential of this technology, we present a taxonomy of LLM-based testing agents based on their level of autonomy, and describe how a greater level of autonomy can benefit developers in practice. An example use of LLMs as a testing assistant is provided to demonstrate how a conversational framework for testing can help developers. This also highlights how the often criticized "hallucination" of LLMs can be beneficial for testing. We identify other tangible benefits that LLM-driven testing agents can bestow, and also discuss potential limitations.
Author Feldt, Robert
Yoon, Juyeon
Kang, Sungmin
Yoo, Shin
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  fullname: Yoo, Shin
  email: shin.yoo@kaist.ac.kr
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SubjectTerms artificial intelligence
Automation
Drives
large language model
machine learning
Middleware
Oral communication
Software testing
Taxonomy
test automation
Testing
Title Towards Autonomous Testing Agents via Conversational Large Language Models
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