Cost-Efficient Automated Program Repair with Large Language Models

Centrala begrepp

CIGAR, a novel automated program repair approach, effectively explores the patch search space of large language models while minimizing the computational cost, as measured by the number of tokens used.

Sammanfattning

The paper introduces CIGAR, a new automated program repair (APR) approach that leverages large language models (LLMs) while focusing on minimizing the computational cost. CIGAR works in two major steps:

First Plausible Patch Search:

Plausible Patch Search Initiation: CIGAR starts with an "initiation prompt" to identify a first plausible patch. If no plausible patch is found, it proceeds to the "partial patch improvement" phase.
Partial Patch Improvement: CIGAR uses "improvement prompts" to iteratively generate a plausible patch by improving on the partial patches found in the previous step. It also employs a "reboot" strategy to explore different parts of the search space.

Plausible Patch Multiplication:

After generating the first plausible patch, CIGAR uses a "patch amplification prompt" to generate more distinct plausible patches, maximizing the chances of finding the correct patch.

CIGAR's key innovations are its prompt engineering techniques and the reboot and patch multiplication strategies, which enable it to explore the patch search space effectively while minimizing the token cost.
The authors evaluate CIGAR on 429 bugs from the DEFECTS4J and HUMANEVAL-JAVA datasets. CIGAR outperforms state-of-the-art LLM-based APR tools, fixing 171 out of 429 (39.8%) bugs. More importantly, CIGAR reduces the token cost by 73% compared to the baseline.

Statistik

CIGAR spends an average of 127k tokens per bug, while the baseline uses 467k tokens per bug.
On the subset of bugs that are fixed by both CIGAR and the baseline, CIGAR spends 20k tokens per bug, while the baseline uses 608k tokens, a cost saving of 96%.

Citat

"CIGAR is the first LLM-based APR tool that aims at minimizing the computational cost, as measured by the number of tokens employed."
"CIGAR reduces the token cost by 73% on average."
"CIGAR outperforms the state-of-the-art APR tools, incl. recent LLM-based APR tools, by fixing 171/429 (39.8%) of the considered bugs."

Viktiga insikter från

CigaR: Cost-efficient Program Repair with LLMs

by Dávi... på arxiv.org 04-19-2024

https://arxiv.org/pdf/2402.06598.pdf

Djupare frågor

How can the prompt engineering techniques and the reboot and patch multiplication strategies used in CIGAR be applied to other software engineering tasks beyond program repair?

In the context of software engineering tasks beyond program repair, the prompt engineering techniques and strategies employed in CIGAR can be adapted and applied to various areas to enhance the effectiveness and efficiency of LLM-based tools.

Prompt Engineering Techniques:

In-Context Learning: Prompt engineering techniques can be utilized to provide contextual information to LLMs in tasks such as code generation, code summarization, or natural language processing. By crafting prompts that encapsulate the necessary context and information, LLMs can generate more accurate and relevant outputs.
Iterative Prompting: The iterative approach used in CIGAR can be beneficial in tasks where multiple iterations are required to refine and improve the output. For example, in code completion tasks, iterative prompting can help LLMs generate more accurate code suggestions over multiple interactions.
Feedback Summarization: Summarizing feedback from previous interactions with the LLM can guide the model in subsequent iterations. This can be valuable in tasks like code translation or documentation generation, where feedback from previous outputs can inform the generation of more coherent and accurate results.

Reboot and Patch Multiplication Strategies:

Exploration in Search Space: The reboot strategy in CIGAR can be applied to tasks where exploration of different solution spaces is crucial. For instance, in code optimization tasks, rebooting the process can help LLMs explore alternative optimization strategies and find more efficient solutions.
Diversity in Outputs: Patch multiplication strategies can be beneficial in tasks that require generating diverse outputs. In tasks like code refactoring or style correction, generating multiple diverse patches can provide developers with a range of options to choose from, improving the overall quality of the output.

By incorporating these prompt engineering techniques and strategies into various software engineering tasks, LLM-based tools can be optimized to produce more accurate, diverse, and contextually relevant outputs, enhancing their utility and effectiveness across different domains.

What are the potential limitations of the token cost metric as the sole measure of efficiency, and how can other cost factors be incorporated into the evaluation of LLM-based tools?

While the token cost metric is a valuable measure of efficiency in LLM-based tools, it has certain limitations when used as the sole measure of efficiency. Some potential limitations include:

Limited Scope: Token cost may not capture the full computational or resource cost associated with using LLMs. Other factors like model training, infrastructure costs, and maintenance overhead are not accounted for in token cost alone.

Quality vs. Quantity: Token cost focuses on the quantity of tokens used but may not reflect the quality of the generated outputs. Efficiency should also consider the accuracy, relevance, and usefulness of the generated results.

Task Complexity: Different tasks may have varying token cost requirements based on the complexity of the task and the model's proficiency in that domain. Token cost alone may not provide a comprehensive view of efficiency across diverse tasks.

To address these limitations and provide a more holistic evaluation of LLM-based tools, other cost factors can be incorporated, such as:

Model Training Cost: Including the cost of training the LLM model can provide a more comprehensive view of the overall cost of using the tool.
Infrastructure Cost: Considering the infrastructure and computational resources required to run the LLM can give insights into the operational cost of the tool.
Maintenance and Support Cost: Factoring in ongoing maintenance, updates, and support for the LLM-based tool can provide a more accurate assessment of long-term cost efficiency.
By integrating these additional cost factors into the evaluation of LLM-based tools, a more nuanced understanding of efficiency can be achieved, taking into account both the token cost and other associated expenses.

Given the significant performance improvements of CIGAR over state-of-the-art, what are the key insights that can be drawn about the role of LLMs in automating software engineering tasks, and how can these insights guide future research in this area?

The performance improvements demonstrated by CIGAR highlight the pivotal role of LLMs in automating software engineering tasks and offer valuable insights for future research in this area:

Efficiency and Cost-Effectiveness: CIGAR's success in minimizing token cost while improving effectiveness underscores the potential of LLMs to automate complex software engineering tasks in a cost-effective manner. Future research can focus on optimizing LLM usage to enhance efficiency and reduce operational costs in software development processes.

Prompt Engineering Techniques: The effectiveness of prompt engineering techniques in guiding LLMs to generate accurate outputs emphasizes the importance of tailored prompts in enhancing model performance. Future research can explore advanced prompting strategies to improve LLM capabilities in various software engineering domains.

Exploration and Diversity: The use of reboot and patch multiplication strategies in CIGAR showcases the significance of exploring diverse solution spaces and generating multiple plausible patches. Future research can delve into techniques for promoting diversity in LLM outputs to provide developers with a range of high-quality solutions.

Generalizability and Adaptability: The success of CIGAR across different projects and bug types highlights the generalizability and adaptability of LLM-based tools in software engineering tasks. Future research can focus on extending the applicability of LLMs to a broader range of software development challenges and domains.

By leveraging these insights, future research in LLM-based automation of software engineering tasks can advance the development of more efficient, accurate, and cost-effective tools, ultimately enhancing productivity and innovation in the software development process.