2026-05-30T07:04:53Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/408162026-02-03T11:47:41Zcom_10630_2254col_10630_37959

Influence of External Dependency Retrieval and Prompt Engineering in Test Case Generation using LLMs Lenke, David Ferrer-Urbano, Francisco Javier Chicano-García, José-Francisco Aprendizaje automático (Inteligencia artificial) Lenguajes de programación Proceso en lenguaje natural (Informática) The recent rise of large language models (LLMs) has enabled the generation of higher-quality test cases by leveraging the semantics of the methods under test. However, existing LLM-based approaches still struggle to achieve high coverage levels. To mitigate this issue, we present two complementary techniques in this work: Prompt Engineering and External Dependency Retrieval for context enrichment. We evaluated our improvements through an ablation study on three open-source and four proprietary projects, encompassing 261 distinct methods. For each method, we generated test suites under four implementations and performed ten independent runs, yielding a total of 10,440 executions. Our combined approach yields an average coverage increase of 12% on industrial software, with statistically significant gains over all other variants studied in this paper. Although our enhancements increase the context (the number of input tokens rises by 66.3%), this is partially compensated by a reduction in output tokens due to fewer repair attempts, so that the overall cost overhead remains moderate at about 16%. As future work, we aim to identify the minimal necessary context that still yields significant improvements in test coverage, which could help to further reduce costs. 2025-11-19T09:49:38Z 2025-11-19T09:49:38Z 2025 conference output https://hdl.handle.net/10630/40816 eng IDEAL Jaen, España 14/11/2025 open access