Aug 2026· International Conference on Automated Software Engineering· Vol 33· 0 citations· 33 references
Topic Modeling
TL;DR
A reproducible, human-validated evaluation framework applied to 13 strategies—four architectural families crossed with four reasoning variants crossed with four reasoning variants—across three SLMs spanning 3B–14B parameters, plus targeted ablations.
A large empirical study on using LLMs to generate CI configurations from natural language across services and model families suggests that similarity and validity are distinct objectives for CI generation and motivate schema-aware evaluation and tooling for LLM-based configuration generation.
Two novel contributions are introduced: CodeEval and CodeQual, an open-source execution framework that provides researchers with a ready-to-use evaluation pipeline for evaluating and improving LLMs in software engineering contexts, encompassing both functional correctness assessment and subjective code quality evaluation.
XREPOTEST is introduced, a multilingual repository-level benchmark for unit test generation spanning five underexplored languages: Rust, Go, Julia, PHP, and Ruby, and Invocation Rate is proposed to assess whether generated tests meaningfully exercise the intended functionality.
L. Dung, Dong Cao Van, Nam Le Hai et al.· 0 citations
An automated, multi-dimensional evaluation framework for C# code generation, applying it to four state-of-the-art LLMs: GPT, Gemini, Claude, and Grok is presented and a substantial gap between correctness and quality attributes is revealed.
Seyed Mohammad Mahdi Ghalandarian, Majid Bazargani, Masoumeh Taromirad· 0 citations
A systematic empirical study of multiple strategies for context enrichment and optimization in LLM‐based unit test generation, conducted on seven diverse projects (three open‐source and four proprietary industrial systems), encompassing 261 distinct methods establish this optimized context strategy as a cost‐effective solution for scalable, industrial‐grade automated test generation.
Javier Ferrer, Francisco Chicano· Expert systems· 0 citations
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
AI may appear weightless, but every model depends on physical infrastructure. To understand responsible AI, we need to look beyond algorithms and consider the entire lifecycle of the hardware behind them. The post Responsible AI Must Consider Its Afterlife appeared first on GPT-Lab.
