RepairAgent: An Autonomous, Large Language Model-Based Agent for Automated Program Repair

Core Concepts

RepairAgent, an autonomous agent powered by a large language model, can effectively fix real-world software bugs by dynamically interleaving information gathering, repair ingredient search, and fix validation.

Abstract

The paper introduces RepairAgent, an autonomous, large language model (LLM)-based agent for automated program repair. Unlike existing deep learning-based approaches that use a fixed prompt or feedback loop, RepairAgent treats the LLM as an agent capable of autonomously planning and executing actions to fix bugs.
Directory:

Introduction

Automated program repair is a critical task in software development
Existing approaches have limitations in their ability to gather information and interleave different repair steps

Background on LLM-based, Autonomous Agents

LLMs can be used to build autonomous agents that plan and execute actions to achieve a goal
Agents can be equipped with tools that they can invoke to interact with the world

Approach

Overview of the RepairAgent approach
Terminology: cycles, dynamic prompt
Dynamic prompting of the repair agent

Role, goals, guidelines, state description, available tools, gathered information, output format

Tools for the agent to use

Reading and extracting code, searching and generating code, testing and patching, control tools

Middleware for orchestrating the communication between the LLM and the tools

Implementation

Details on the implementation using Python, Docker, and the AutoGPT framework

Evaluation

RQ1: Effectiveness - RepairAgent fixes 164 bugs in the Defects4J dataset, including 39 bugs not fixed by prior work
RQ2: Costs - RepairAgent imposes an average cost of 14 cents per bug
RQ3: Tool usage - Analysis of how the agent uses the available tools

The note provides a comprehensive overview of the RepairAgent approach, its key components, and the evaluation results, allowing the reader to fully understand the work without referring back to the original content.

Stats

"RepairAgent imposes an average cost of 270,000 tokens per bug, which, under the current pricing of OpenAI's GPT-3.5 model, translates to 14 cents per bug."
"RepairAgent successfully fixes 164 bugs, including 74 and 90 bugs of Defects4J v1.2 and v2.0, respectively."
"The correctly fixed bugs include 49 bugs that require fixing more than one line, showing that RepairAgent is capable of fixing complex bugs."
"Compared to state-of-the-art techniques [19], [21], RepairAgent successfully fixes 39 bugs not fixed by prior work."

Quotes

"To the best of our knowledge, this work is the first to present an autonomous, LLM-based agent for program repair, paving the way for future agent-based techniques in software engineering."
"RepairAgent freely interleaves gathering information about the bug, gathering repair ingredients, and validating fixes, while deciding which tools to invoke based on the gathered information and feedback from previous fix attempts."

Key Insights Distilled From

RepairAgent

by Islem Bouzen... at arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17134.pdf

Deeper Inquiries

How can the RepairAgent approach be extended to handle bugs in other programming languages beyond Java?

To extend the RepairAgent approach to handle bugs in other programming languages, a few key adaptations would be necessary. Firstly, the set of tools available for interacting with code bases and generating fixes would need to be tailored to the specific characteristics and syntax of the target language. For instance, different tools may be required for parsing code structures or identifying common bug patterns in languages like Python or C++.
Secondly, the dynamic prompt format used by RepairAgent would need adjustments to accommodate language-specific nuances and requirements. This includes modifying prompts related to goals, guidelines, state descriptions, available tools, gathered information sections based on the unique aspects of each programming language.
Additionally, training a general-purpose LLM model like GPT-3.5 on a diverse dataset that includes examples from various programming languages would enhance its ability to understand and generate accurate fixes across different contexts. Fine-tuning the model on specific language-related tasks could further improve its performance when handling bugs in those particular languages.
Lastly, incorporating domain-specific knowledge about common bug types and fix strategies prevalent in different programming languages into the agent's decision-making process would also contribute significantly to its effectiveness when dealing with bugs outside of Java.

What are the potential limitations of using a general-purpose LLM, such as GPT-3.5, for an autonomous agent and how could a more specialized LLM model improve performance?

Using a general-purpose Large Language Model (LLM) like GPT-3.5 for an autonomous agent comes with certain limitations:

Domain Specificity: General-purpose models may lack domain-specific knowledge crucial for understanding software engineering concepts deeply.
Fine-grained Control: Tailoring responses precisely according to task requirements might be challenging due to inherent flexibility.
Token Consumption: Interacting with large models incurs high token costs which might not always align with budget constraints.
Model Bias: General models may exhibit biases towards certain types of data seen during training.

A more specialized LLM model designed explicitly for program repair could address these limitations:

Domain Expertise: A specialized LLM can incorporate software engineering principles directly into its training data leading to better comprehension.
2 .Task-Specific Prompts: Custom prompts optimized for program repair tasks can guide responses effectively without ambiguity.
3 .Cost-Efficiency: Specialized models trained specifically for repair tasks might consume fewer tokens per interaction compared to generic models due their focused nature.
4 .Bias Reduction: By focusing solely on relevant software development data during training specialied LLMS have reduced risk of bias affecting results

By leveraging these advantages through specialization towards program repair tasks specifically rather than broader linguistic capabilities ,a dedicated LLM could potentially outperform generalized counterparts like GPT-3..5

Given promising results how can RepairAgent approach integrated into real-world software development workflows assist developers in bug fixing process?

The integration of RepairAgent into real-world software development workflows has several implications:
1 - Automated Bug Fixing: Incorporating RepairAgent allows automated resolutionof identified issues within projects saving timeand effortfor developers who otherwise manually troubleshoot errors.
2 - Enhanced Productivity: Developers benefit from quicker turnaround timesin resolvingbugs allowing themto focuson higher-level designand implementationtasksrather than getting bogged downby routine debugging activities..
3 - Quality Assurance: The consistent applicationofRepair Agent ensures thorough testingand validationof patches before deployment reducingthe likelihoodof introducingnew defectsinto productioncodebases
4 - Continuous Improvement:
Feedback mechanismscanbe implementedto capture developer inputon generatedfixes helpingtorefineRepair Agentover timebasedonreal-world scenarios encounteredduringdevelopmentprocesses
In conclusion,the seamlessintegrationofRepair Agentinto existingsoftwaredevelopmentworkflowsenhances efficiency,reduces manual overhead,and contributes towardsoptimizingoverallproductqualitythroughautomatedbugresolutioncapabilities