DeCEAT：为AI驱动的软件测试解码碳排放
DeCEAT: Decoding Carbon Emissions for AI-driven Software Testing
AI总结：
DeCEAT框架通过评估SLMs在测试生成中的环境与性能权衡，揭示了提示设计对可持续性的影响。
摘要：
随着语言模型在自动化软件测试中的使用增加，其环境影响引发了担忧，但现有的可持续性分析几乎只关注大型语言模型。因此，小型语言模型（SLMs）在测试生成过程中的能耗和碳特征仍鲜为人知。为填补这一空白，本工作引入了DeCEAT框架，该框架系统地利用HumanEval基准和自适应提示变体（基于Anthropic模板）评估SLMs的环境和性能权衡。该框架在受控条件下量化了排放和时间感知行为，CodeCarbon测量能耗和碳排放，单元测试覆盖率评估生成测试的质量。我们的结果表明，不同SLMs表现出不同的可持续性优势：一些优先考虑更低的能耗和更快的执行速度，而另一些则在碳约束下保持更高的稳定性或准确性。这些发现表明，SLMs驱动的测试生成的可持续性是多维的，并且强烈受提示设计影响。本工作提供了一个专门针对自动化SLMs测试生成的可持续性评估框架，阐明了提示结构和模型选择如何共同影响环境和性能结果。
英文摘要：
The increasing use of language models in automated software testing raises concerns about their environmental impact, yet existing sustainability analyses focus almost exclusively on large language models. As a result, the energy and carbon characteristics of small language models (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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
