ConAIR:Consistency-Augmented Iterative Interaction Framework to Enhance the Reliability of Code Generation (2411.15587v1)

Abstract: Code generation techniques generate code snippets automatically based on the problem requirements in natural language. Recently, LLMs achieve the SOTA performance on code generation. However, LLMs still struggle at times to generate accurate code, which diminishes their promised efficiency as developers must spend significant effort evaluating and debugging the generated code. To improve the reliability and quality of the generated codes, researchers propose to leverage Consistency to obtain a better code based on generating and ranking multiple candidates. The existing approach is problematic as Consistency thinks a code is better when (1) the code pass more tests (inter-consistency) (2) more codes share the same behavior (intra-consistency). However, because the tests are also generated by LLMs, they could be wrong as well. As a result, majority voting based on testing results is unreliable. Relying solely on consistency is insufficient to address this issue; integrating user feedback is essential for effectively guiding consistency. We show that with minimal human effort, performance can be significantly enhanced. We propose Consistency-Augmented Iterative Interaction Framework to Enhance the Reliability of Code Generation, ConAIR, which is an approach that aims to improve the performance of a code generator through two distinctive ingredients, i.e., (1) lightweight user effort for validating the correctness of selected tests; and (2) a dynamic strategy for ranking, localizing and correcting multiple tests and codes. Overall, we propose a lightweight interaction framework that incorporates user feedback to correct identified tests and guide the iterative process. The iteration rounds are only 4 in average with the help of consistency. With only lightweight human efforts, we can achieve an improvement of 33% towards the base model.

Analyzing ConAIR: A Consistency-Augmented Iterative Interaction Framework for Enhanced Code Generation

The paper introduces ConAIR, a novel framework designed to enhance the reliability of code generation outputs produced by LLMs. By addressing existing limitations in consistency-based techniques, the framework incorporates a lightweight interaction model paired with a co-evolution process, which iteratively refines the quality of both generated code and tests. This approach emerges as a significant endeavor in improving LLM efficiency and effectiveness, particularly in automatic code generation scenarios.

Problem Statement and Research Context

Recent advances in LLMs have led to considerable improvements in automated code generation. These models, especially those trained on code-specific corpora, exhibit state-of-the-art performance in generating functional code snippets from natural language descriptions. Despite these advancements, LLMs often produce unreliable and suboptimal code, necessitating substantial post-hoc verification and debugging efforts from developers.

Reliability in code generation hinges on consistency, which involves generating multiple code versions and selecting the most consistent one. Traditional methods rely on the presumption that the more times a code solution passes generated test cases, the better its quality. However, the paper identifies a critical oversight: the tests themselves, produced by similar LLM mechanisms, may contain significant errors. The authors emphasize that relying solely on majority voting, based on these flawed tests, is insufficient. Instead, user engagement to validate tests is proposed as a crucial and effective solution.

Core Contributions of ConAIR

ConAIR delivers a set of methodological innovations that distinguish it from previous approaches:

1. Lightweight Interaction Framework: The approach integrates minimal user involvement to verify the correctness of selected tests. This incorporates the pivotal role of developers or users as oracles in the validation process, thereby enhancing the quality of consistency indicators and guiding iterative improvements.
2. Dynamic Rank-Correct-Fix Co-evolution Process: ConAIR uses consistency measures to progressively rank, identify, and rectify incorrect tests and codes. Iterative processes improve code quality, and consistency measures (Con_𝑐→𝑡 and Con_𝑡→𝑐) become increasingly reliable. Each cycle involves ranking tests by their likelihood of being incorrect, obtaining user-corrected feedback, applying automated code fixes, and reassessing the consistency relationship.
3. Empirical Validation: Evaluation on three benchmarks, HumanEval, HumanEval+, and MBPP, shows that ConAIR considerably enhances code generation from LLM models. Notably, with a suboptimal model GPT-3.5, ConAIR improves performance by an average of 32.9% compared to the base model alone, and surpasses other post-processing techniques like MPSC.

Numerical Results

The framework delivers notable improvements with minimal user interaction, averaging only four interactions per session. This significant boost points to ConAIR's efficient integration into the LLM code generation pipeline. Compared to the cutting-edge LLM, GPT-4o, ConAIR achieves an impressive 12.32% improvement, highlighting the potential benefits of incorporating user feedback in automated environments.

Implications and Future Directions

ConAIR’s results imply substantial practical and theoretical implications. Practically, it offers a refined method to boost developers' productivity by reducing the manual effort required to debug and validate LLM-generated code. Theoretically, the research might pave the way for further explorations into human-AI collaborative paradigms focused on diverse consistency measures.

The authors express concerns about test and code verification being too burdensome or error-prone, suggesting potential areas for future research. Additionally, while ConAIR has been tested on Python benchmarks, its adaptability to other programming languages and more complex software development tasks remains an open question.

Conclusion

This research presents ConAIR as a sophisticated and efficient enhancement for LLM-based code generation, addressing key limitations of prior consistency-oriented approaches. By successfully incorporating human feedback into the interaction framework, automated code generation is not only improved quantitatively but also becomes more reliable and efficient for developers. As LLMs continue to evolve, frameworks like ConAIR are crucial in aligning AI capabilities with practical software development needs, promoting a more effective symbiosis between human and machine intelligence.