| Challenge: | Existing methods for code comments generate comments manually, but they suffer from poor scalability and high maintenance cost due to the expensive overhead of writing comment templates. |
| Approach: | They propose a method to automatically generate code comments at a function level by targeting object-oriented programming languages. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods and is comparable with existing methods. |
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An Intra-Class Relation Guided Approach for Code Comment Generation (2023.findings-eacl)
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| Challenge: | Recent work in code comment generation assumes that all information required to generate comments is encoded in the target function itself, yet in most realistic situations, it is hard to understand a function in isolation from the surrounding context. |
| Approach: | They propose a graph-based learning framework to capture various relations among functions in a class file. |
| Outcome: | The proposed method outperforms baseline models on automatic and human evaluation metrics on a Java dataset collected from real-world projects. |
Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective (2024.findings-acl)
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| Challenge: | Existing studies decompose complex tasks into intermediate steps by prompting strategies, such as Chain-of-Thought and its variants. |
| Approach: | They propose to use code comments as natural logic pivot between natural language and code language to boost the code generation ability of code LLMs. |
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Leveraging Context-Aware Prompting for Commit Message Generation (2024.emnlp-main)
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| Challenge: | Existing methods for writing comprehensive commit messages focus on the changed lines or nearest context lines, but excessive contexts can lead to noise. |
| Approach: | They propose a code model COMMIT that can generate automatic commit messages by combining a dataset with a context-aware prompt. |
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Learning to Update Natural Language Comments Based on Code Changes (2020.acl-main)
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| Challenge: | a novel approach to update comments based on code changes is proposed . a dataset of open-source software projects is used to train and evaluate the model . |
| Approach: | They propose an approach that learns to correlate changes across two distinct language representations to generate a sequence of edits that are applied to the existing comment to reflect the source code modifications. |
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Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback (2024.findings-acl)
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Zhangqian Bi, Yao Wan, Zheng Wang, Hongyu Zhang, Batu Guan, Fangxin Lu, Zili Zhang, Yulei Sui, Hai Jin, Xuanhua Shi
| Challenge: | Large Language Models (LLMs) generate code for given contexts, such as incomplete code, class, data structure, or project-specific information. |
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| Outcome: | The proposed model outperforms retrieval-based code generation baselines and significantly outperfies the existing large language models. |
TAG : Type Auxiliary Guiding for Code Comment Generation (2020.acl-main)
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| Challenge: | Existing code comment generation approaches ignore type information of interpretation of the code, e.g., operator, string, etc. Existing approaches ignore the type information due to the hierarchical dependence among the type. |
| Approach: | They propose an encoder-decoder framework which considers the source code as an N-ary tree with type information associated with each node. |
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CodeRetriever: A Large Scale Contrastive Pre-Training Method for Code Search (2022.emnlp-main)
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Xiaonan Li, Yeyun Gong, Yelong Shen, Xipeng Qiu, Hang Zhang, Bolun Yao, Weizhen Qi, Daxin Jiang, Weizhu Chen, Nan Duan
| Challenge: | Existing code pre-training approaches often adopt (masked) language modeling as the training objective which targets on learning to predict (macked) tokens in a given code context. |
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Turning the Tide: Repository-based Code Reflection (2025.findings-emnlp)
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| Challenge: | Code large language models (LLMs) enhance programming by understanding and generating code across languages. |
| Approach: | a new benchmark evaluates code understanding and generation in repositories using code large language models. |
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Unified Pre-training for Program Understanding and Generation (2021.naacl-main)
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| Challenge: | PLUG is a programming language that is used for programming and language understanding and generation tasks. |
| Approach: | They propose a sequence-to-sequence model that performs a broad spectrum of program and language understanding and generation tasks. |
| Outcome: | The proposed model outperforms or rivals state-of-the-art models on code summarization, code generation, and code translation tasks in seven programming languages. |
ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation (2025.acl-long)
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| Challenge: | Existing methods to enhance code generation performance include integrating compiler feedback. |
| Approach: | They propose a method that integrates compiler feedback to improve one-off code generation performance. |
| Outcome: | The proposed method improves one-off code generation performance on three benchmarks and can be applied to other domains that focus on final results and require long reasoning paths. |