Assertion-Aware Test Code Summarization with Large Language Models

• URL: http://arxiv.org/abs/2511.06227v1
• Date: Sun, 09 Nov 2025 04:58:32 GMT
• Title: Assertion-Aware Test Code Summarization with Large Language Models
• Authors: Anamul Haque Mollah, Ahmed Aljohani, Hyunsook Do,
• Abstract summary: Unit tests often lack concise summaries that convey test intent.<n>This paper presents a new benchmark of 91 real-world Java test cases paired with developer-written summaries.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unit tests often lack concise summaries that convey test intent, especially in auto-generated or poorly documented codebases. Large Language Models (LLMs) offer a promising solution, but their effectiveness depends heavily on how they are prompted. Unlike generic code summarization, test-code summarization poses distinct challenges because test methods validate expected behavior through assertions rather than im- plementing functionality. This paper presents a new benchmark of 91 real-world Java test cases paired with developer-written summaries and conducts a controlled ablation study to investigate how test code-related components-such as the method under test (MUT), assertion messages, and assertion semantics-affect the performance of LLM-generated test summaries. We evaluate four code LLMs (Codex, Codestral, DeepSeek, and Qwen-Coder) across seven prompt configurations using n-gram metrics (BLEU, ROUGE-L, METEOR), semantic similarity (BERTScore), and LLM-based evaluation. Results show that prompting with as- sertion semantics improves summary quality by an average of 0.10 points (2.3%) over full MUT context (4.45 vs. 4.35) while requiring fewer input tokens. Codex and Qwen-Coder achieve the highest alignment with human-written summaries, while DeepSeek underperforms despite high lexical overlap. The replication package is publicly available at https://doi.org/10. 5281/zenodo.17067550

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