Why you shouldn't fully trust ChatGPT: A synthesis of this AI tool's error rates across disciplines and the software engineering lifecycle

Context: ChatGPT and other large language models (LLMs) are widely used across healthcare, business, economics, engineering, and software engineering (SE). Despite their popularity, concerns persist about their reliability, especially their error rates across domains and the software development lifecycle (SDLC). Objective: This study synthesizes and quantifies ChatGPT's reported error rates across major domains and SE tasks aligned with SDLC phases. It provides an evidence-based view of where ChatGPT excels, where it fails, and how reliability varies by task, domain, and model version (GPT-3.5, GPT-4, GPT-4-turbo, GPT-4o). Method: A Multivocal Literature Review (MLR) was conducted, gathering data from academic studies, reports, benchmarks, and grey literature up to 2025. Factual, reasoning, coding, and interpretive errors were considered. Data were grouped by domain and SE phase and visualized using boxplots to show error distributions. Results: Error rates vary across domains and versions. In healthcare, rates ranged from 8% to 83%. Business and economics saw error rates drop from ~50% with GPT-3.5 to 15-20% with GPT-4. Engineering tasks averaged 20-30%. Programming success reached 87.5%, though complex debugging still showed over 50% errors. In SE, requirements and design phases showed lower error rates (~5-20%), while coding, testing, and maintenance phases had higher variability (10-50%). Upgrades from GPT-3.5 to GPT-4 improved reliability. Conclusion: Despite improvements, ChatGPT still exhibits non-negligible error rates varying by domain, task, and SDLC phase. Full reliance without human oversight remains risky, especially in critical settings. Continuous evaluation and critical validation are essential to ensure reliability and trustworthiness.

NLP-assisted software testing: a systematic review

Context: To reduce manual effort of extracting test cases from natural-language requirements, many approaches based on Natural Language Processing (NLP) have been proposed in the literature. Given the large number of approaches in this area, and since many practitioners are eager to utilize such techniques, it is important to synthesize and provide an overview of the state-of-the-art in this area. Objective: Our objective is to summarize the state-of-the-art in NLP-assisted software testing which could benefit practitioners to potentially utilize those NLP-based techniques, benefit researchers in providing an overview of the research landscape. Method: To address the above need, we conducted a survey in the form of a systematic literature mapping (classification) and systematic literature review. After compiling an initial pool of 57 papers, we conducted a systematic voting, and our final pool included 50 technical papers. Results: This review paper provides an overview of contribution types in the papers, types of NLP approaches used to assist software testing, types of required input requirements, and a review of tool support in this area. Among our results are the followings: (1) only 2 of the 28 tools (7%) presented in the papers are available for download; (2) a larger ratio of the papers (23 of 50) provided a shallow exposure to the NLP aspects (almost no details). Conclusion: We believe that this paper would benefit both practitioners and researchers by serving as an "index" to the body of knowledge in this area. The results could help practitioners by enabling them to utilize any of the existing NLP-based techniques to reduce cost of test-case design and decrease the amount of human resources spent on test activities. Initial insights, after sharing this review with some of our industrial collaborators, show that this review can indeed be useful and beneficial to practitioners.