arXiv subjects

Zongen Ren

Publications and source records attributed to Zongen Ren.

3 recordsLinked to original sources

arXiv2026

Assessing Language Models for Salient Class Identification

Code review requires reviewers to understand the core intent of code changes, which becomes difficult when a commit modifies multiple classes. In such commits, one or more primarily modified classes, referred to as salient classes, may induce modifications in other classes. Accurate identification of salient classes offers reviewers an effective entry point to navigate code changes and facilitates program comprehension. Existing state-of-the-art approaches rely on complex program-analysis procedures, including Abstract Syntax Tree (AST) parsing, class relation extraction, handcrafted feature engineering, or dependency graph construction. To this end, we study whether language models (LMs) can identify salient classes directly from commits without feature engineering, graph construction, or training. We first construct a new dataset ApacheJavaCM, derived from the ApacheCM dataset, containing 7,911 commits and 25,914 labeled classes. On this dataset, we systematically evaluate whether LMs can identify salient classes directly from commits and compare with the strongest reproducible state-of-the-art (SOTA) baseline. The evaluation covers two large language models (LLMs), GPT-5.4 and DeepSeek-V3.2, one small language model (SLM), Qwen3.5-9B, and three prompting strategies: zero-shot, few-shot, and chain-of-thought. The LMs substantially outperform the baseline while remaining stable across commit characteristics and selected LMs. We also found that, for salient class identification tasks, a 9B-parameter open-source SLM, Qwen3.5-9B, under few-shot prompting, achieves performance comparable to that of a much larger closed-source LLM, GPT-5.4. These results suggest that lightweight, locally deployable SLMs are feasible options for the salient class identification task and can reduce both cost and privacy barriers associated with relying on closed-source LLMs.

cs.SE↗

arXiv2026

LLM-Enhanced Commit Message Generation via Issue Information: An Exploratory Study

Commit messages help developers understand code changes, support collaboration, and improve long-term maintenance. However, the use of issue information alone as the external context for LLM-based CMG has not been systematically studied. We propose an ISsue-Augmented framework for Commit message generation (ISAC) by combining code diffs with issue information as LLM input. To support the evaluation, we construct ApacheCM-Issue, a commit-issue aligned dataset built upon ApacheCM by linking commits with issues from GitHub and Apache Jira. Using samples from Scala, Java, and C++ projects, we evaluate four input configurations using two representative LLMs, GPT-5.5 and DeepSeek-V4-Flash in different reasoning configurations. The results show that incorporating issue information consistently improves LLM-based CMG across all evaluated model configurations and metrics, with the largest gains observed for CIDEr. Incorporating a similar historical commit further improves automatic metric scores, while replacing full issue information with a structured issue summary decreases them. ISAC also outperforms the four reproduced state-of-the-art (SOTA) CMG baselines across all five automatic metrics on the experimental dataset. The human evaluation further shows that structured issue summaries may improve perceived completeness, although replacing the original issue information can sacrifice contextual details and lead to worse results on automatic metrics.

cs.SE↗

arXiv2026

CoRaCMG: Contextual Retrieval-Augmented Framework for Commit Message Generation

Commit messages play a key role in documenting the intent behind code changes. However, they are often low-quality, vague, or incomplete, limiting their usefulness. Commit Message Generation (CMG) aims to automatically generate descriptive commit messages from code diffs to reduce developers' effort and improve message quality. Although recent advances in LLMs have shown promise in automating CMG, their performance remains limited. This paper aims to enhance CMG performance by retrieving similar diff-message pairs to guide LLMs to generate commit messages that are more precise and informative. We proposed CoRaCMG, a Contextual Retrieval-augmented framework for Commit Message Generation, structured in three phases: (1) Retrieve: retrieving the similar diff-message pairs; (2) Augment: combining them with the query diff into a structured prompt; and (3) Generate: generating commit messages corresponding to the query diff via LLMs. CoRaCMG enables LLMs to learn project-specific terminologies and writing styles from the retrieved diff-message pairs. We evaluated CoRaCMG across multiple LLMs (e.g., GPT, DeepSeek, and Qwen) and compared its performance against SOTA baselines. Experimental results show that CoRaCMG significantly boosts LLM performance across four metrics (BLEU, Rouge-L, METEOR, and CIDEr). Specifically, DeepSeek-R1 achieves relative improvements of 76% in BLEU and 71% in CIDEr when augmented with a single retrieved example pair. After incorporating the single example pair, GPT-4o achieves the highest improvement rate, with BLEU increasing by 89%. Moreover, performance gains plateau after more than three examples are used, indicating diminishing returns. Further analysis shows that the improvements are attributed to the model's ability to capture the terminologies and writing styles of human-written commit messages from the retrieved example pairs.

cs.SE↗