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SOEN-101: Code Generation by Emulating Software Process Models Using Large Language Model Agents (2403.15852v2)

Published 23 Mar 2024 in cs.SE and cs.AI

Abstract: Software process models are essential to facilitate collaboration and communication among software teams to solve complex development tasks. Inspired by these software engineering practices, we present FlowGen - a code generation framework that emulates software process models based on multiple LLM agents. We emulate three process models, FlowGenWaterfall, FlowGenTDD, and FlowGenScrum, by assigning LLM agents to embody roles (i.e., requirement engineer, architect, developer, tester, and scrum master) that correspond to everyday development activities and organize their communication patterns. The agents work collaboratively using chain-of-thought and prompt composition with continuous self-refinement to improve the code quality. We use GPT3.5 as our underlying LLM and several baselines (RawGPT, CodeT, Reflexion) to evaluate code generation on four benchmarks: HumanEval, HumanEval-ET, MBPP, and MBPP-ET. Our findings show that FlowGenScrum excels compared to other process models, achieving a Pass@1 of 75.2, 65.5, 82.5, and 56.7 in HumanEval, HumanEval-ET, MBPP, and MBPP-ET, respectively (an average of 15% improvement over RawGPT). Compared with other state-of-the-art techniques, FlowGenScrum achieves a higher Pass@1 in MBPP compared to CodeT, with both outperforming Reflexion. Notably, integrating CodeT into FlowGenScrum resulted in statistically significant improvements, achieving the highest Pass@1 scores. Our analysis also reveals that the development activities impacted code smell and exception handling differently, with design and code review adding more exception handling and reducing code smells. Finally, FlowGen models maintain stable Pass@1 scores across GPT3.5 versions and temperature values, highlighting the effectiveness of software process models in enhancing the quality and stability of LLM-generated code.

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Summary

  • The paper demonstrates the LCG framework’s ability to integrate LLM agents with software process models to achieve up to a 31.5% improvement in Pass@1 scores.
  • It employs role-based assignment and techniques like chain-of-thought reasoning and self-refinement to enhance code quality and reduce code smells.
  • The study finds that the Scrum model outperforms Waterfall and TDD, emphasizing the practical benefits of structured process emulation in automated code generation.

Analyzing the Impact of Software Process Models on LLM-Based Code Generation

The integration of LLMs in code generation tasks represents a progression in automating software development activities. The paper "When LLM-based Code Generation Meets the Software Development Process" proposes a diversified approach in harnessing LLMs, specifically through the development of the LCG framework. This framework utilizes multiphasic agent-driven models to simulate traditional software development processes, namely Waterfall, Test-Driven Development (TDD), and Scrum. Each of these models enlists LLM agents in roles akin to real-world software engineering professions: requirement engineers, architects, developers, testers, and scrum masters, facilitating a simulated collaborative environment to improve code generation outputs.

Framework and Methodology Overview

The LCG framework extends beyond conventional prompting methods, assigning LLM agents distinct roles and tasks that accord with chosen development methodologies. In their paper, Lin et al. implemented a systematic role-implementation architecture, ensuring agents distinctly operate within their domains. Moreover, the framework employs advanced techniques such as chain-of-thought reasoning, prompt composition, and self-refinement to adaptively enhance code outputs iteratively. Notably, the work emphasizes zero-shot learning to eschew biases inherent in few-shot sample selections.

Evaluation benchmarks such as HumanEval and MBPP, including stronger evaluation tests (HumanEval-ET and MBPP-ET), were utilized to detect the robustness of these LLM configurations. Results demonstrated substantial improvements in Pass@1 scores over a baseline (GPT-3.5), with metrics showing up to 31.5% improvement in some tests. This underlines the efficacy of implementing structured software development paradigms within LLM code generation frameworks.

Numerical and Empirical Findings

Among the three process models tested, Scrum consistently outperformed others concerning not only Pass@1 scores but also favorable code smell and exception-handling metrics. The ability of these models to maintain stability amid fluctuating model versions establishes the pragmatic advantages of employing process models in LLM-driven code generation. Comparatively, the traditional usage of GPT without structured processes exhibits significant variability, particularly when different versions of the model are assessed.

Design and code review activities uniquely facilitated a decrease in code smells and incremented exception handling, signifying enhancements in the reliability of the generated code. Test execution, unsurprisingly, emerged as the most influential activity for improving code correctness, as its removal resulted in marked drops in Pass@1 scores. These insights suggest an intertwined effect of process-structure emulation and systematic testing, contributing synergistically to elevate code quality.

Implications and Future Directions

The implications of these findings extend toward new methodologies in automated code development, where the agent-based adaptation of traditional process models aligns with agile and iterative software practices. The theoretical underpinning implies a necessity to expand upon multi-agent collaborations within AI frameworks, potentially integrating similar methodologies in broader development tasks and life cycles.

In future studies, extending these frameworks to support diverse programming languages and task complexities could help streamline LLM applicability across varying software domains. Additionally, investigating the participatory role of LLMs in more intricate development settings, such as end-to-end system design and integration, holds merit. Thus, an evolved exploration into agents-as-collaborators can redefine existing paradigms in AI software engineering research.

Considering the paper's contribution to code generation techniques, it is evident that the authors provide a strong case for using process models to support stable and quality-driven implementations of LLMs in software development tasks. This research offers a promising foundation for continuous advancements in the domain of AI-driven software engineering.

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