Mengzhuo Chen, Junjie Wang, Fangwen Mu, Yawen Wang, Zhe Liu, Huanxiang Feng, Qing Wang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

The automotive industry is undergoing rapid growth driven by advancements such as electrification, necessitating efficient software development methodologies. While model-based system engineering is prevalent in automotive software development, its application is typically confined to the requirements and design phases, with manual coding still dominating the development process, resulting in extended development efforts and cycles. To address these challenges, we propose Vehicle Domain-Specific Language (VDSL), a low-code development tool that integrates modeling and automatic code generation. With VDSL, users can simply specify their requirements and utilize graphical drag-and-drop along with parameter configuration to efficiently implement vehicle functionality with auto-generated code. Tested and evaluated on 3 vehicle models across 18 months at GAC Group, VDSL has demonstrated its effectiveness in significantly reducing development effort and costs, improving resource efficiency, and providing greater flexibility for system updates via Over-The-Air (OTA) Technology. This work further enhances the practicality of model-based development in the automotive domain, boosting development efficiency and potentially enhancing quality.

Detecting and refactoring code smells is challenging, laborious, and sustaining. Although large language models have demonstrated potential in identifying various types of code smells, they also have limitations such as input-output token restrictions, difficulty in accessing repository-level knowledge, and performing dynamic source code analysis. Existing learning-based methods or commercial expert toolsets have advantages in handling complex smells. They can analyze project structures and contextual information in-depth, access global code repositories, and utilize advanced code analysis techniques. However, these toolsets are often designed for specific types and patterns of code smells and can only address fixed smells, lacking flexibility and scalability. To resolve that problem, we propose iSMELL, an ensemble approach that employs various code smell detection toolsets via Mixture of Experts (MoE) architecture for comprehensive code smell detection, and enhances the LLMs with the detection results from expert toolsets for refactoring those identified code smells. First, we train a MoE model that, based on input code vectors, outputs the most suitable expert tool for identifying each type of smell. Then, we select the recommended toolsets for code smell detection and obtain their results. Finally, we equip the prompts with the detection results from the expert toolsets, thereby enhancing the refactoring capability of LLMs for code with existing smells, enabling them to provide different solutions based on the type of smell. We evaluate our approach on detecting and refactoring three classical and complex code smells, i.e., Refused Bequest, God Class, and Feature Envy. The results show that, by adopting seven expert code smell toolsets, iSMELL achieved an average F1 score of 75.17% on code smell detection, outperforming LLMs baselines by an increase of 35.05% in F1 score. We further evaluate the code refactored by the enhanced LLM. The quantitative and human evaluation results show that iSMELL could improve code quality metrics and conduct satisfactory refactoring toward the identified code smells. We believe that our proposed solution could provide new insights into better leveraging LLMs and existing approaches to resolving complex software tasks.

Existing automatic code comment generators mainly focus on producing a general description of functionality for a given code snippet without considering developer intentions. However, in real-world practice, comments are complicated, which often contain information reflecting various intentions of developers, e.g., functionality summarization, design rationale, implementation details, code properties, etc. To bridge the gap between automatic code comment generation and real-world comment practice, we define Developer-Intent Driven Code Comment Generation, which can generate intent-aware comments for the same source code with different intents. To tackle this challenging task, we propose DOME, an approach that utilizes Intent-guided Selective Attention to explicitly select intent-relevant information from the source code, and produces various comments reflecting different intents. Our approach is evaluated on two real-world Java datasets, and the experimental results show that our approach outperforms the state-of-the-art baselines. A human evaluation also confirms the significant potential of applying DOME in practical usage, enabling developers to comment code effectively according to their own needs.

In community-based software development, developers frequently rely on live-chatting to discuss emergent bugs/errors they encounter in daily development tasks. However, it remains a challenging task to accurately record such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the task of identifying and synthesizing bug reports from community live chats, and propose a novel approach, named BugListener, to address the challenges. Specifically, BugListener automates three sub-tasks: 1) Disentangle the dialogs from massive chat logs by using a Feed-Forward neural network; 2) Identify the bug-report dialogs from separated dialogs by leveraging the Graph neural network to learn the contextual information; 3) Synthesize the bug reports by utilizing Transfer Learning techniques to classify the sentences into: observed behaviors (OB), expected behaviors (EB), and steps to reproduce the bug (SR). BugListener is evaluated on six open source projects. The results show that: for bug report identification, BugListener achieves the average F1 of 77.74%, improving the best baseline by 12.96%; and for bug report synthesis task, BugListener could classify the OB, EB, and SR sentences with the F1 of 84.62%, 71.46%, and 73.13%, improving the best baselines by 9.32%, 12.21%, 10.91%, respectively. A human evaluation study also confirms the effectiveness of BugListener in generating relevant and accurate bug reports. These demonstrate the significant potential of applying BugListener in community-based software development, for promoting bug discovery and quality improvement.

The main goal of dialogue disentanglement is to separate the mixed utterances from a chat slice into independent dialogues. Existing models often utilize either an utterance-to-utterance (U2U) prediction to determine whether two utterances that have the “reply-to” relationship belong to one dialogue, or an utterance-to-thread (U2T) prediction to determine which dialogue-thread a given utterance should belong to. Inspired by mutual leaning, we propose MuiDial, a novel dialogue disentanglement model, to exploit the intent of each utterance and feed the intent to a mutual learning U2U-U2T disentanglement model. Experimental results and in-depth analysis on several benchmark datasets demonstrate the effectiveness and generalizability of our approach.

Deliberation is a common and natural behavior in human daily life. For example, when writing papers or articles, we usually first write drafts, and then iteratively polish them until satisfied. In light of such a human cognitive process, we propose DECOM, which is a multi-pass deliberation framework for automatic comment generation. DECOM consists of multiple Deliberation Models and one Evaluation Model. Given a code snippet, we first extract keywords from the code and retrieve a similar code fragment from a pre-defined corpus. Then, we treat the comment of the retrieved code as the initial draft and input it with the code and keywords into DECOM to start the iterative deliberation process. At each deliberation, the deliberation model polishes the draft and generates a new comment. The evaluation model measures the quality of the newly generated comment to determine whether to end the iterative process or not. When the iterative process is terminated, the best-generated comment will be selected as the target comment. Our approach is evaluated on two real-world datasets in Java (87K) and Python (108K), and experiment results show that our approach outperforms the state-of-the-art baselines. A human evaluation study also confirms the comments generated by DECOM tend to be more readable, informative, and useful.

Code summarization, the task of generating useful comments given the code, has long been of interest. Most of the existing code summarization models are trained and validated on widely-used code comment benchmark datasets. However, little is known about the quality of the benchmark datasets built from real-world projects. Are the benchmark datasets as good as expected? To bridge the gap, we conduct a systematic research to assess and improve the quality of four benchmark datasets widely used for code summarization tasks. First, we propose an automated code-comment cleaning tool that can accurately detect noisy data caused by inappropriate data preprocessing operations from existing benchmark datasets. Then, we apply the tool to further assess the data quality of the four benchmark datasets, based on the detected noises. Finally, we conduct comparative experiments to investigate the impact of noisy data on the performance of code summarization models. The results show that these data preprocessing noises widely exist in all four benchmark datasets, and removing these noisy data leads to a significant improvement on the performance of code summarization. We believe that the findings and insights will enable a better understanding of data quality in code summarization tasks, and pave the way for relevant research and practice.

Collaborative live chats are gaining popularity as a development communication tool. In community live chatting, developers are likely to post issues they encountered (e.g., setup issues and compile issues), and other developers respond with possible solutions. Therefore, community live chats contain rich sets of information for reported issues and their corresponding solutions, which can be quite useful for knowledge sharing and future reuse if extracted and restored in time. However, it remains challenging to accurately mine such knowledge due to the noisy nature of interleaved dialogs in live chat data. In this paper, we first formulate the problem of issue-solution pair extraction from developer live chat data, and propose an automated approach, named ISPY, based on natural language processing and deep learning techniques with customized enhancements, to address the problem. Specifically, ISPY automates three tasks: 1) Disentangle live chat logs, employing a feedforward neural network to disentangle a conversation history into separate dialogs automatically; 2) Detect dialogs discussing issues, using a novel convolutional neural network (CNN), which consists of a BERT-based utterance embedding layer, a context-aware dialog embedding layer, and an output layer; 3) Extract appropriate utterances and combine them as corresponding solutions, based on the same CNN structure but with different feeding inputs. To evaluate ISPY, we compare it with six baselines, utilizing a dataset with 750 dialogs including 171 issue-solution pairs and evaluate ISPY from eight open source communities. The results show that, for issue-detection, our approach achieves the F1 of 76%, and outperforms all baselines by 30%. Our approach achieves the F1 of 63% for solution-extraction and outperforms the baselines by 20%. Furthermore, we apply ISPY on three new communities to extensively evaluate ISPY’s practical usage. Moreover, we publish over 30K issue-solution pairs extracted from 11 communities. We believe that ISPY can facilitate community-based software development by promoting knowledge sharing and shortening the issue-resolving process.

Utilizing massive user feedback, e.g. feature requests from Bugzilla, JIRA, or GitHub, to motivate software evolution has become a new trend in RE community. However, manually understanding and analyzing feature requests from issue tracking systems is a time-consuming and labor-intensive task. In this paper, we present NERO (coNtent annotation and smElly Feature Requests detection), an automated tool to support analysts to understand the semantic meaning of feature requests and detect the smells in feature requests. It can also provide an overall score based on the smell detection results to help analysts quickly judge the quality of feature requests.