2026-10-05T21:32:55Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:28028 2026-10-05T06:44:05Z ddc:004 open_access

Parameterizing LLMs in Practice: An Empirical Study of LLMs Integrated into Software Systems Olmedo, Agustín Laval, Jannik Urtado, Christelle Vauttier, Sylvain Software Engineering for AI Large Language Model Hyperparameter LLM configuration LLM API Mining Software Repository Large Language Models (LLMs) are increasingly embedded in software projects, yet little is known about how developers configure LLM parameters in the wild. Characterizing which parameters are defined, how many per project, which groups are co-defined, and what values are preferred can inform academics and tool builders about practices. This article mines open-source Python repositories on GitHub that integrate LLMs and uses AST-based static analysis to extract parameter assignments. Project-level definitions are analyzed to address prevalence (RQ1), configuration complexity and its structure (RQ2), and value distributions (RQ3). The final corpus includes 363 projects and gathers 7892 parameter definitions, with both the dataset and the code available for reproducibility. We observe that temperature is most frequently defined (90.36%), followed by max_tokens (56.75%), top_p (49.86%), and top_k (42.70%); penalty parameters are comparatively rare (RQ1). Projects typically define few parameters (mean ≈ 3), and this limited set expands incrementally around a stable core (temperature + max_tokens) (RQ2). Distributions suggest "defaults-in-practice": temperature ≈ 0.0, 0.7 and 1.0, top_p ≈ 0.9, top_k ≈ 0-50, and max_tokens at 2^k values (e.g., 256/512/1024) (RQ3). In conclusion, developers favor minimalist, sampling-centric configurations with convergence around specific ranges. These descriptive findings support clearer configuration reporting, offer practical baselines for tools and education, and motivate future work on causes, longitudinal evolution and task/domain stratification. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Agustín Olmedo and Jannik Laval and Christelle Urtado and Sylvain Vauttier 2026 Is Part Of LIPIcs, Volume 394, 20th International Symposium on Empirical Software Engineering and Measurement (ESEM 2026) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.ESEM.2026.60 urn:nbn:de:0030-drops-280280 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESEM.2026.60 eng https://creativecommons.org/licenses/by/4.0/legalcode