Execution-based Code Generation using Deep Reinforcement Learning (2301.13816v4)

Abstract: The utilization of programming language (PL) models, pre-trained on large-scale code corpora, as a means of automating software engineering processes has demonstrated considerable potential in streamlining various code generation tasks such as code completion, code translation, and program synthesis. However, current approaches mainly rely on supervised fine-tuning objectives borrowed from text generation, neglecting unique sequence-level characteristics of code, including but not limited to compilability as well as syntactic and functional correctness. To address this limitation, we propose PPOCoder, a new framework for code generation that synergistically combines pre-trained PL models with Proximal Policy Optimization (PPO) which is a widely used deep reinforcement learning technique. By utilizing non-differentiable feedback from code execution and structure alignment, PPOCoder seamlessly integrates external code-specific knowledge into the model optimization process. It's important to note that PPOCoder is a task-agnostic and model-agnostic framework that can be used across different code generation tasks and PLs. Extensive experiments on three code generation tasks demonstrate the effectiveness of our proposed approach compared to SOTA methods, achieving significant improvements in compilation success rates and functional correctness across different PLs.

Overview of "Execution-based Code Generation using Deep Reinforcement Learning"

The research paper titled "Execution-based Code Generation using Deep Reinforcement Learning" introduces a novel framework aimed at enhancing code generation methodologies by leveraging deep reinforcement learning (RL). This framework, named \modelname, primarily integrates pre-trained programming language (PL) models with Proximal Policy Optimization (PPO), a widely acknowledged RL technique, to address the deficiencies in current code generation approaches. The central issue tackled by this paper is the reliance of existing models on supervised fine-tuning objectives tailored from text generation, which often overlook sequence-level characteristics unique to code, such as syntactic and functional correctness.

Core Contributions

The paper details several key contributions:

• Introduction of \modelname Framework: \modelname is both task-agnostic and model-agnostic, which implies it can flexibly extend across different code generation tasks and programming languages. This is a substantial improvement over existing methods which are frequently limited to specific programming languages or tasks.
• Incorporation of Non-Differentiable Feedback: The framework introduces the utilization of non-differentiable feedback rooted in code execution and structure alignment, providing a mechanism to embed external code-specific knowledge into the optimization process.
• New Reward Function Design: A novel reward function is developed, integrating feedback from code execution, syntactic and semantic matching scores, and KL-divergence to control explorations without deviating significantly from pre-trained models. This promotes higher-quality code generation in terms of both compilability and operational accuracy.

Empirical Validation

Through extensive empirical experimentation across several code generation tasks such as code completion, code translation, and program synthesis, \modelname demonstrated significant improvements:

• Code Completion: The framework achieved an impressive compilation success rate of 97.68% on Python code in the CodeSearchNet dataset, a marked improvement over existing baselines.
• Code Translation: On the XLCoST benchmark, \modelname increased the compilation rate across all evaluated language pairs, showcasing its capability to bridge syntactic and functional correctness gaps more effectively than state-of-the-art (SOTA) baselines.
• Program Synthesis: Especially notable in the APPS dataset, where \modelname improved pass rates for unseen problems, indicating an enhanced generalization capability.

Technical Innovation and Implications

The technical ingenuity of \modelname lies in its design which synergizes deep reinforcement learning with code-specific feedback mechanisms. By carefully considering non-differentiable code execution feedback and structuring objectives around execution-based correctness, this framework fosters an environment for models to learn representations more aligned with real-world software development requirements.

From a theoretical standpoint, \modelname paves the way towards bridging deep learning principles with software engineering needs, proposing a rich area of research and development. Practically, its model-agnostic approach offers a promising direction for deploying AI in a variety of programming environments without necessitating task or language-specific configurations.

Future Directions

Future innovations in AI-driven code generation could build upon this framework by exploring deeper integrations of semantic feedback and automated machine reasoning in synthesizing logically coherent programs. Investigations into scaling the approach across broader datasets and diverse development environments would be prudent, potentially enabling \modelname to generalize beyond curated datasets to field-level software applications.

Overall, the paper presents a compelling enhancement of the deep learning paradigms utilized for code generation and establishes a robust foundation for continuing advancements in automated programming.