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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/2503.17998 |
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| _version_ | 1866917966517370880 |
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| author | Hasan, Navid Bin Islam, Md. Ashraful Khan, Junaed Younus Senjik, Sanjida Iqbal, Anindya |
| author_facet | Hasan, Navid Bin Islam, Md. Ashraful Khan, Junaed Younus Senjik, Sanjida Iqbal, Anindya |
| contents | We explored the challenges practitioners face in software testing and proposed automated solutions to address these obstacles. We began with a survey of local software companies and 26 practitioners, revealing that the primary challenge is not writing test scripts but aligning testing efforts with business requirements. Based on these insights, we constructed a use-case $\rightarrow$ (high-level) test-cases dataset to train/fine-tune models for generating high-level test cases. High-level test cases specify what aspects of the software's functionality need to be tested, along with the expected outcomes. We evaluated large language models, such as GPT-4o, Gemini, LLaMA 3.1 8B, and Mistral 7B, where fine-tuning (the latter two) yields improved performance. A final (human evaluation) survey confirmed the effectiveness of these generated test cases. Our proactive approach strengthens requirement-testing alignment and facilitates early test case generation to streamline development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_17998 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automatic High-Level Test Case Generation using Large Language Models Hasan, Navid Bin Islam, Md. Ashraful Khan, Junaed Younus Senjik, Sanjida Iqbal, Anindya Software Engineering We explored the challenges practitioners face in software testing and proposed automated solutions to address these obstacles. We began with a survey of local software companies and 26 practitioners, revealing that the primary challenge is not writing test scripts but aligning testing efforts with business requirements. Based on these insights, we constructed a use-case $\rightarrow$ (high-level) test-cases dataset to train/fine-tune models for generating high-level test cases. High-level test cases specify what aspects of the software's functionality need to be tested, along with the expected outcomes. We evaluated large language models, such as GPT-4o, Gemini, LLaMA 3.1 8B, and Mistral 7B, where fine-tuning (the latter two) yields improved performance. A final (human evaluation) survey confirmed the effectiveness of these generated test cases. Our proactive approach strengthens requirement-testing alignment and facilitates early test case generation to streamline development. |
| title | Automatic High-Level Test Case Generation using Large Language Models |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2503.17998 |