How Software Testers Perceive Large Language Models: A Focus Group Study with Lexicometric Analysis

Context. Large Language Models (LLMs) are increasingly embedded in software-engineering toolchains, with potential to enhance quality-assurance tasks such as test-case generation, bug analysis, and traceability. Yet their industrial value remains unclear, partly because practicing testers’ perspectives are underexplored. Objective. We investigate how industry testers perceive the usefulness and limitations of LLMs across the Software Test Life Cycle (STLC), identifying (i) the most critical stages, (ii) operational bottlenecks, and (iii) perceived benefits, risks, and adoption conditions. Method. A focus group with five professional quality analysts (2–10 years’ experience) included hands-on interaction with an LLM-powered BDD notebook, followed by guided discussion and affinity-diagram voting. Forty-two statements were coded and prioritized. Results. Requirements Analysis and Test Planning were deemed most critical; 71% of votes linked rework to unclear or incomplete requirements. Benefits cited were automation of repetitive tasks, broader coverage, and faster learning; main concerns were prompt sensitivity, limited domain generalization, and data-privacy risks, with emphasis on human oversight and domain-adapted prompt libraries. Conclusion. While LLMs can improve efficiency and coverage, adoption depends on high-quality inputs, secure deployment, and early-phase integration. The findings offer empirically grounded guidance for aligning LLM solutions with the socio-technical realities of industrial testing teams.