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.
Abstract
While Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, their effectiveness in unit testing is often constrained by insufficient context regarding external dependencies. This limitation is particularly pronounced in industrial settings, where proprietary code remains opaque to the model. To address this challenge, we present 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. By evaluating seven implementations (ranging from basic prompts to optimized context reduction strategies) across 10 independent runs, we analysed a total of 28,710 test suites. Our results demonstrate that combining prompt engineering with external dependency retrieval achieves an average branch coverage increase of 11.52 percentage points on industrial software over the baseline, with statistically significant improvements across all competing implementations. Beyond coverage, richer context substantially reduces generation‐repair iterations, cutting median execution time by 51.3% in industrial projects. We further show that reducing external dependencies to method signatures alone decreases input token consumption by up to 46.6% (25.4% in industrial projects) while fully preserving the coverage and efficiency gains of the complete retrieval approach. To confirm that these benefits are not tied to a specific model, we replicate the core comparison across three LLM backends from different families, obtaining a consistent, statistically significant coverage improvement on industrial code in every case. These findings establish this optimized context strategy as a cost‐effective solution for scalable, industrial‐grade automated test generation.
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
This paper addresses automated unit test generation with large language models (LLMs). LLM-based test generation has not yet attained a quality level sufficient for practical use in industry. Although LLMs often reproduce API syntax faithfully, they frequently disregard semantic usage constraints and execution-environment dependencies, leading to assertion failures, mock-related errors, and reference/resolution errors. A prior failure analysis of Java unit test generation using GPT-4o classified 2980 trials into eight failure patterns and identified three root-cause mechanisms: external context ignorance, internal context ignorance, and a syntax–semantics gap. Building on that analysis, this paper proposes a prompt design comprising three strategies: (1) making the execution state explicit in the generated test, (2) stating semantic constraints explicitly, and (3) injecting environment constraints prior to generation. In contrast to generic techniques such as few-shot learning or chain-of-thought prompting, each proposed strategy is tied to a specific root-cause mechanism, yielding a systematic design in which each rule is explicitly justified by its correspondence to a specific root-cause mechanism. Experiments on 298 methods with five models (GPT-4o, GPT-5, GPT-5.1-Codex, Claude Sonnet 4.5, and Gemini 2.5 Pro) show improved test execution success rates for every model, with absolute gains ranging from 1.1 to 21.1 percentage points (pp). Mock-related errors were reduced by 61.9%–99.2% relative to the baseline prompt, demonstrating effectiveness against the targeted failure patterns. Finally, conditions under which the strategies transfer to other code-generation tasks are discussed, along with limitations on their scope.
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.
Large language models (LLMs) are increasingly used in software engineering pipelines for code generation, where production prompts often combine multiple constraints. This paper presents a full-factorial empirical study of how output formatting, persona assignment, and urgency framing jointly affect LLM code-generation reliability. We evaluate all 27 combinations in a controlled 3x3x3 design and decompose each compound condition into an additive prediction and a residual interaction term that captures super-additive degradation. The study uses all 164 HumanEval+ problems across five OpenAI models from the GPT-4o family, GPT-4.1 family, and o3-mini, yielding 22,140 greedy-decoding evaluations. A format-aware extraction pipeline separates formatting failures from reasoning failures, and significance is assessed with McNemar's test, odds ratios, and 95% confidence intervals. Results show that compound constraints can produce architecture-dependent degradation not predictable from single-factor experiments. The GPT-4o family exhibits consistent super-additive effects, with pass@1 reductions 3-12 percentage points beyond additive predictions; the largest interaction is -12.2 pp on GPT-4o-mini for JSON + expert persona + moderate urgency. JSON combinations generally produce larger interactions than XML. In contrast, the GPT-4.1 family is largely resistant, while o3-mini shows a qualitatively different pattern in which structured output constraints can improve performance. These findings show that vulnerability is architecture-dependent rather than size-dependent, that individually neutral or beneficial constraints can combine to cause substantial degradation, and that compound-prompt testing should be standard in reliability assessment for LLM-assisted engineering pipelines.
Shrenik Jadhav, Nickalsa LaPlaca, Caleb Stone et al.· 0 citations
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.
Balaji Venktesh, Amsaprabhaa M, G. Sundaram· International Conference on...· 0 citations
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