External validity is a central concern in empirical software engineering, yet the debate has largely focused on whether study participants are representative. How well the study objects they reason about - code, models, or requirements - are representative of each participant's own work is often overlooked. Practitioners' own artifacts would be ideal, but are often out of reach, so researchers sample from public datasets or construct objects artificially. Whether this lack of subject-object fit affects participants' ability to act on the material remains unknown. We take a first step toward answering this question for requirements. We propose an approach that uses an LLM to generate context-adapted synthetic requirements just-in-time during an online survey from a short, participant-supplied context description, optionally injecting requirements smells as controlled stimuli. To validate the approach, we conducted a controlled experiment with 31 industry practitioners in a 2x2 design that crosses stimulus generation (static vs. synthetic) with quality (clean vs. smelly). Each participant rated the perceived representativeness and assessability of 20 requirements. Using Bayesian data analysis within a statistical causal-inference framework, we find that perceived representativeness has a strong positive effect on assessability. Synthetic requirements are rated as more representative and, primarily due to this gain, more assessable than static ones sourced from public datasets; smell injection leaves both perceptions unchanged. Our results support treating a lack of study-object representativeness as a first-class validity threat and position context-adapted synthetic study objects as a practical addition to the empirical methodology toolbox.

翻译：暂无翻译