Year: 2025 | Month: September | Volume: 12 | Issue: 9 | Pages: 393-401
DOI: https://doi.org/10.52403/ijrr.20250941
Human-AI Collaboration in Validating and Refining LLM Summaries of Test Automation Results
Alex Thomas Thomas
Saransh Inc, New Jersey, USA
Corresponding Author: Alex Thomas Thomas
ABSTRACT
With Large Language Models (LLMs) persistently demonstrating the capabilities of automating software test procedures, the need for effective human monitoring and cooperation in confirming AI-produced test summaries has grown to become a top priority in software quality assurance. This integrative review synthesizes current evidence for human-AI collaborative frameworks for verifying and enhancing LLM-generated summaries of test automation reports, investigating the intersection of artificial intelligence strengths and human competence in ensuring reliable software testing results. The review systematically reviews existing methodological paradigms for human-in-the-loop testing protocols, with particular emphasis on quality engineering techniques instilling confidence and safety in software systems that utilize LLMs, and empirical research comparing LLM and human judge performance in software engineering contexts to investigate the potential and potential constraints of AI systems as standalone judges of test quality. Underlying the analysis here is a look at collaborative intelligence frameworks that integrate human knowledge and AI potential smoothly within software testing scenarios, evaluating structured interaction designs that enable significant human-LLM collaboration and examining functionality-aware decision-making that can maximize the dependability of AI-generated test summaries through knowledge-validation mechanisms that integrate LLMs with human overseeing mechanisms. Discoveries emphasize that strong human-AI collaboration in testing result validation requires well-designed interaction paradigms leveraging human domain expertise while exploiting AI processing abilities, with trust and transparency being key determinants of establishing including robust evaluation metrics, mixed-methods validation schemes, and human-centric approaches to AI-generated technical documentation. The review finishes by proposing a harmonized methodological approach for measuring human-AI collaboration effectiveness in test automation cases, highlighting the necessity of systematic validation processes, interpretable AI decision-making algorithms, and continuous human oversight in guaranteeing software quality requirements, with significant implications for software engineering practitioners seeking to integrate LLM functionality into existing testing procedures while maintaining high-quality assurance standards.
Keywords: Human-AI collaboration, Large Language Models, test automation, software testing, validation frameworks, quality assurance, human-in-the-loop systems, collaborative intelligence
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