DeCEAT: Decoding Carbon Emissions for AI-driven Software Testing

Abstract: The increasing use of LLMs in automated software testing raises concerns about their environmental impact, yet existing sustainability analyses focus almost exclusively on LLMs. As a result, the energy and carbon characteristics of small LLMs (SLMs) during test generation remain largely unexplored. To address this gap, this work introduces the DeCEAT framework, which systematically evaluates the environmental and performance trade-offs of SLMs using the HumanEval benchmark and adaptive prompt variants (based on the Anthropic template). The framework quantifies emission and time-aware behavior under controlled conditions, with CodeCarbon measuring energy consumption and carbon emissions, and unit test coverage assessing the quality of generated tests. Our results show that different SLMs exhibit distinct sustainability strengths: some prioritize lower energy use and faster execution, while others maintain higher stability or accuracy under carbon constraints. These findings demonstrate that sustainability in the generation of SLM-driven tests is multidimensional and strongly shaped by prompt design. This work provides a focused sustainability evaluation framework specifically tailored to automated SLM-based test generation, clarifying how prompt structure and model choice jointly influence environmental and performance outcomes.