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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.22141 |
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| _version_ | 1866909555918635008 |
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| author | Zhang, Yifan Towey, Dave Pike, Matthew Luu, Quang-Hung Liu, Huai Chen, Tsong Yueh |
| author_facet | Zhang, Yifan Towey, Dave Pike, Matthew Luu, Quang-Hung Liu, Huai Chen, Tsong Yueh |
| contents | Context: This paper provides an in-depth examination of the generation and evaluation of Metamorphic Relations (MRs) using GPT models developed by OpenAI, with a particular focus on the capabilities of GPT-4 in software testing environments.
Objective: The aim is to examine the quality of MRs produced by GPT-3.5 and GPT-4 for a specific System Under Test (SUT) adopted from an earlier study, and to introduce and apply an improved set of evaluation criteria for a diverse range of SUTs.
Method: The initial phase evaluates MRs generated by GPT-3.5 and GPT-4 using criteria from a prior study, followed by an application of an enhanced evaluation framework on MRs created by GPT-4 for a diverse range of nine SUTs, varying from simple programs to complex systems incorporating AI/ML components. A custom-built GPT evaluator, alongside human evaluators, assessed the MRs, enabling a direct comparison between automated and human evaluation methods.
Results: The study finds that GPT-4 outperforms GPT-3.5 in generating accurate and useful MRs. With the advanced evaluation criteria, GPT-4 demonstrates a significant ability to produce high-quality MRs across a wide range of SUTs, including complex systems incorporating AI/ML components.
Conclusions: GPT-4 exhibits advanced capabilities in generating MRs suitable for various applications. The research underscores the growing potential of AI in software testing, particularly in the generation and evaluation of MRs, and points towards the complementarity of human and AI skills in this domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integrating Artificial Intelligence with Human Expertise: An In-depth Analysis of ChatGPT's Capabilities in Generating Metamorphic Relations Zhang, Yifan Towey, Dave Pike, Matthew Luu, Quang-Hung Liu, Huai Chen, Tsong Yueh Software Engineering Artificial Intelligence Context: This paper provides an in-depth examination of the generation and evaluation of Metamorphic Relations (MRs) using GPT models developed by OpenAI, with a particular focus on the capabilities of GPT-4 in software testing environments. Objective: The aim is to examine the quality of MRs produced by GPT-3.5 and GPT-4 for a specific System Under Test (SUT) adopted from an earlier study, and to introduce and apply an improved set of evaluation criteria for a diverse range of SUTs. Method: The initial phase evaluates MRs generated by GPT-3.5 and GPT-4 using criteria from a prior study, followed by an application of an enhanced evaluation framework on MRs created by GPT-4 for a diverse range of nine SUTs, varying from simple programs to complex systems incorporating AI/ML components. A custom-built GPT evaluator, alongside human evaluators, assessed the MRs, enabling a direct comparison between automated and human evaluation methods. Results: The study finds that GPT-4 outperforms GPT-3.5 in generating accurate and useful MRs. With the advanced evaluation criteria, GPT-4 demonstrates a significant ability to produce high-quality MRs across a wide range of SUTs, including complex systems incorporating AI/ML components. Conclusions: GPT-4 exhibits advanced capabilities in generating MRs suitable for various applications. The research underscores the growing potential of AI in software testing, particularly in the generation and evaluation of MRs, and points towards the complementarity of human and AI skills in this domain. |
| title | Integrating Artificial Intelligence with Human Expertise: An In-depth Analysis of ChatGPT's Capabilities in Generating Metamorphic Relations |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2503.22141 |