On the Energy Footprint of Using a Small Language Model for Unit Test Generation

Context. Manual unit test creation is a cognitively intensive and time-consuming activity, prompting researchers and practitioners to increasingly adopt automated testing tools. Recent advancements in language models have expanded automation possibilities, including unit test generation, yet these models raise substantial sustainability concerns due to their energy consumption compared to conventional, specialized tools. Goal. Our research investigates whether the energy overhead associated with employing a small language model (SLM) for unit test generation is justified compared to a conventional, lightweight testing tool. We compare and analyze the energy consumption incurred during test suite generation, as well as the fault-finding effectiveness of the resulting test suites, for an SLM (Phi-3.1 Mini 128k) and Pynguin, a purpose-built tool for unit test generation. Method. We posed two research questions: (i) What is the difference in energy usage between Phi and Pynguin during the generation of unit test suites for Python programs?; and (ii) To what extent do unit test suites generated by Phi and Pynguin differ in their fault-finding effectiveness? To rigorously address the first research question, we employed Bayesian Data Analysis (BDA). For the second research question, we conducted a complementary empirical analysis using descriptive statistics. Results. Our Bayesian analysis provides robust evidence indicating that Phi consistently consumes significantly more energy than Pynguin during test suite generation. Conclusions. These findings underscore significant sustainability concerns associated with employing even SLMs for routine Software Engineering tasks such as unit test generation. The results challenge the assumption of universal energy efficiency benefits from smaller-scale models and emphasize the necessity for careful energy consumption evaluations in the adoption of automated software testing approaches.