Private GPTs for LLM-driven testing in software development and machine learning
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913969138040832 |
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| author | Jagielski, Jakub Rojas, Consuelo Abel, Markus |
| author_facet | Jagielski, Jakub Rojas, Consuelo Abel, Markus |
| contents | In this contribution, we examine the capability of private GPTs to automatically generate executable test code based on requirements. More specifically, we use acceptance criteria as input, formulated as part of epics, or stories, which are typically used in modern development processes. This gives product owners, or business intelligence, respectively, a way to directly produce testable criteria through the use of LLMs. We explore the quality of the so-produced tests in two ways: i) directly by letting the LLM generate code from requirements, ii) through an intermediate step using Gherkin syntax. As a result, it turns out that the two-step procedure yields better results -where we define better in terms of human readability and best coding practices, i.e. lines of code and use of additional libraries typically used in testing. Concretely, we evaluate prompt effectiveness across two scenarios: a simple "Hello World" program and a digit classification model, showing that structured prompts lead to higher-quality test outputs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06509 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Private GPTs for LLM-driven testing in software development and machine learning Jagielski, Jakub Rojas, Consuelo Abel, Markus Software Engineering Artificial Intelligence I.2.1 In this contribution, we examine the capability of private GPTs to automatically generate executable test code based on requirements. More specifically, we use acceptance criteria as input, formulated as part of epics, or stories, which are typically used in modern development processes. This gives product owners, or business intelligence, respectively, a way to directly produce testable criteria through the use of LLMs. We explore the quality of the so-produced tests in two ways: i) directly by letting the LLM generate code from requirements, ii) through an intermediate step using Gherkin syntax. As a result, it turns out that the two-step procedure yields better results -where we define better in terms of human readability and best coding practices, i.e. lines of code and use of additional libraries typically used in testing. Concretely, we evaluate prompt effectiveness across two scenarios: a simple "Hello World" program and a digit classification model, showing that structured prompts lead to higher-quality test outputs. |
| title | Private GPTs for LLM-driven testing in software development and machine learning |
| topic | Software Engineering Artificial Intelligence I.2.1 |
| url | https://arxiv.org/abs/2506.06509 |