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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Veröffentlicht in:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] S. 1688 - 1693
Hauptverfasser: Feldt, Robert, Kang, Sungmin, Yoon, Juyeon, Yoo, Shin
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 11.09.2023
Schlagworte:
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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Zusammenfassung: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.
ISSN:2643-1572
DOI:10.1109/ASE56229.2023.00148