Technique to Baseline QE Artefact Generation Aligned to Quality Metrics
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866914164772962304 |
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| author | Farchi, Eitan Nayak, Kiran Majumdar, Papia Ghosh Route, Saritha |
| author_facet | Farchi, Eitan Nayak, Kiran Majumdar, Papia Ghosh Route, Saritha |
| contents | Large Language Models (LLMs) are transforming Quality Engineering (QE) by automating the generation of artefacts such as requirements, test cases, and Behavior Driven Development (BDD) scenarios. However, ensuring the quality of these outputs remains a challenge. This paper presents a systematic technique to baseline and evaluate QE artefacts using quantifiable metrics. The approach combines LLM-driven generation, reverse generation , and iterative refinement guided by rubrics technique for clarity, completeness, consistency, and testability. Experimental results across 12 projects show that reverse-generated artefacts can outperform low-quality inputs and maintain high standards when inputs are strong. The framework enables scalable, reliable QE artefact validation, bridging automation with accountability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15733 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Technique to Baseline QE Artefact Generation Aligned to Quality Metrics Farchi, Eitan Nayak, Kiran Majumdar, Papia Ghosh Route, Saritha Software Engineering Artificial Intelligence Large Language Models (LLMs) are transforming Quality Engineering (QE) by automating the generation of artefacts such as requirements, test cases, and Behavior Driven Development (BDD) scenarios. However, ensuring the quality of these outputs remains a challenge. This paper presents a systematic technique to baseline and evaluate QE artefacts using quantifiable metrics. The approach combines LLM-driven generation, reverse generation , and iterative refinement guided by rubrics technique for clarity, completeness, consistency, and testability. Experimental results across 12 projects show that reverse-generated artefacts can outperform low-quality inputs and maintain high standards when inputs are strong. The framework enables scalable, reliable QE artefact validation, bridging automation with accountability. |
| title | Technique to Baseline QE Artefact Generation Aligned to Quality Metrics |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2511.15733 |