Improving Retrieval-Augmented Code Comment Generation by Retrieving for Generation (2408.03623v1)

Abstract: Code comment generation aims to generate high-quality comments from source code automatically and has been studied for years. Recent studies proposed to integrate information retrieval techniques with neural generation models to tackle this problem, i.e., Retrieval-Augmented Comment Generation (RACG) approaches, and achieved state-of-the-art results. However, the retrievers in previous work are built independently of their generators. This results in that the retrieved exemplars are not necessarily the most useful ones for generating comments, limiting the performance of existing approaches. To address this limitation, we propose a novel training strategy to enable the retriever to learn from the feedback of the generator and retrieve exemplars for generation. Specifically, during training, we use the retriever to retrieve the top-k exemplars and calculate their retrieval scores, and use the generator to calculate a generation loss for the sample based on each exemplar. By aligning high-score exemplars retrieved by the retriever with low-loss exemplars observed by the generator, the retriever can learn to retrieve exemplars that can best improve the quality of the generated comments. Based on this strategy, we propose a novel RACG approach named JOINTCOM and evaluate it on two real-world datasets, JCSD and PCSD. The experimental results demonstrate that our approach surpasses the state-of-the-art baselines by 7.3% to 30.0% in terms of five metrics on the two datasets. We also conduct a human evaluation to compare JOINTCOM with the best-performing baselines. The results indicate that JOINTCOM outperforms the baselines, producing comments that are more natural, informative, and useful.

• The paper introduces a joint training strategy that synchronizes retrievers with generators to enhance code comment generation.
• The approach uses weighted loss optimization with CodeT5 for exemplar retrieval, improving the generation process.
• Experiments on JCSD and PCSD datasets show improvements of up to 30% across key metrics, endorsing its practical impact.

Improving Retrieval-Augmented Code Comment Generation by Retrieving for Generation

The paper "Improving Retrieval-Augmented Code Comment Generation by Retrieving for Generation" discusses a novel approach for enhancing the generation of code comments, an essential aspect of easing code comprehension and maintenance. The integrative strategy proposed by the authors leverages both information retrieval techniques and neural generation models to improve upon current state-of-the-art results in Retrieval-Augmented Comment Generation (RACG).

The core proposal of the paper is a joint training strategy that synchronizes the retriever and the generator in RACG approaches. Traditional RACG methods rely on independently trained retrievers and generators, which often results in the retrieval of suboptimal exemplars for the generation task. The authors hypothesize that coupling the training processes of these components can lead to the retrieval of more useful exemplars, thereby improving the overall quality of the generated comments.

Methodology

Joint Training Strategy

The authors propose a novel training strategy to align the retriever with the generator:

• Exemplar Retrieval and Loss Calculation: In the joint training scheme, the retriever fetches the top-k code-comment pairs (exemplars) from the retrieval base, and then the generator calculates a generation loss for each exemplar-comment pair.
• Weighted Loss Optimization: A weighted loss is constructed, where the weights are derived from the retrieval scores of the exemplars. This loss is then optimized using backpropagation to update both the retriever and the generator.
• Implementation: The implemented system utilizes CodeT5 for initialization of both the retriever's encoder and the generator. The retriever uses a Transformer-based encoder to compute semantic embeddings of code snippets, and the generator is a sequence-to-sequence (seq2seq) model that generates comments based on the concatenated input of code snippets and retrieved exemplars.

Experiments and Results

The approach, named JointCom, was tested on two real-world datasets: JCSD and PCSD. Five metrics were used to evaluate performance: Corpus-level BLEU, Sentence-level BLEU, ROUGE-L, METEOR, and CIDEr. The results showed substantial improvements over existing state-of-the-art methods:

• On JCSD, JointCom outperformed the previous best methods by margins ranging from 7.6% to 28.4% across all metrics.
• On PCSD, improvements ranged from 9.6% to 30.0%, marking significant enhancements in comment generation quality.

Implications and Future Directions

The joint training of retrievers and generators for RACG represents a significant methodological shift, leading to exemplars that more effectively aid the comment generation process. Practically, this approach facilitates the creation of more accurate and informative code comments, which benefits software maintenance and comprehension tasks.

Theoretically, this research demonstrates the efficacy of integrating feedback loops between machine learning components to refine and enhance training protocols. JointCom's architecture underlines the potential for further advancements in code-related AI tasks by leveraging synchronized training strategies.

Future research could explore several expansions:

• Scalability to Other Tasks: Given the promising results in comment generation, the framework could be adapted to other tasks such as bug fixing, code synthesis, or code translation.
• Integration with Larger Models: Applying this joint training strategy to larger pre-trained models, such as CodeT5+ or even more advanced models, could further extend the boundaries of performance in code comprehension tasks.
• Cross-Lingual Capabilities: Extending the approach to support multiple programming languages beyond Java and Python could make it more universally applicable.

Conclusion

The paper provides a well-founded and empirically validated approach to improve retrieval-augmented comment generation. By jointly training retrievers and generators, the authors effectively address the limitations of independent training and significantly enhance the quality and usefulness of generated comments. This contribution not only sets a new benchmark in RACG but also opens up avenues for further research and application in the broader scope of AI-assisted code analysis and generation.