Enhancing Automated Code Translation with Large Language Models: Exploring Challenges and Proposing a Unified Framework

Alapfogalmak

Large Language Models can outperform state-of-the-art learning-based transpilers in automated code translation tasks, but they still suffer from limitations related to program comprehension, I/O type handling, and discrepancy ignorance. UniTrans, a unified code translation framework, leverages auto-generated test cases to effectively address these limitations and substantially boost the performance of various LLMs.

Kivonat

The content explores the potential of using Large Language Models (LLMs) for automated code translation tasks, which aim to translate source code from one programming language (PL) to another in an automated fashion.

The key highlights are:

1. Empirical Study on LLMs and Learning-based Transpilers:

   • The authors conducted an empirical study to investigate the performance of recent LLMs, including GPT-3.5, LLaMA, and CodeGen, on code translation tasks between Python, Java, and C++.
   • They compared the LLMs' performance with state-of-the-art learning-based transpilers like TransCoder, TransCoder-IR, and TransCoder-ST.
   • The results showed that certain LLMs can outperform the learning-based transpilers, but they still suffer from some accuracy issues.

2. In-depth Analysis of LLM Failures:

   • The authors manually analyzed 174 failed cases of the best-performing LLM (GPT-3.5) and categorized the failures into six classes, including Logic, Syntax, I/O, API, Precision, and Others.
   • The analysis revealed that the main limitations of LLMs are: (1) lack of comprehension of the source program, (2) missing explicit I/O type instructions, and (3) ignoring the discrepancies between source and target programming languages.

3. Proposed UniTrans Framework:

   • Motivated by the findings, the authors proposed UniTrans, a unified code translation framework that leverages auto-generated test cases to address the limitations of LLMs.
   • UniTrans consists of three phases: (1) Test Case Generation, (2) Translation Augmentation, and (3) Translation Repair.
   • The test cases provide information about program requirements, I/O types, and execution results to help LLMs overcome the identified limitations.

4. Extensive Experiments and Evaluations:

   • The authors conducted extensive experiments to evaluate the effectiveness of UniTrans with three LLMs (GPT-3.5, LLaMA-13B, and LLaMA-7B) on six translation datasets between Python, Java, and C++.
   • The results showed that UniTrans substantially boosts the code translation performance of the tested LLMs, with significant improvements in both Computational Accuracy (CA) and Exact Match Accuracy (EM Acc).
   • Ablation studies and discussion experiments were also performed to investigate the contribution and influence of each component in UniTrans.

Overall, the content demonstrates the potential of using LLMs for automated code translation and proposes the UniTrans framework to effectively leverage the power of LLMs in this domain.

Statisztikák

The average Computational Accuracy (CA) of GPT-3.5 is improved by 4.02% with UniTrans.
The average Exact Match Accuracy (EM Acc) of GPT-3.5 is improved by 13.28% with UniTrans.
The average CA of LLaMA-13B is improved by 19.20% with UniTrans.
The average EM Acc of LLaMA-13B is improved by 36.42% with UniTrans.
The average CA of LLaMA-7B is improved by 28.58% with UniTrans.
The average EM Acc of LLaMA-7B is improved by 71.22% with UniTrans.

Idézetek

"Large Language Models (LLMs), pre-trained on billions of text/code tokens, bypass the need for re-training/fine-tuning but demonstrate the powerful generality of various code-related tasks, such as code generation [7, 11, 23, 25, 28, 52], program repair [12, 48], and code summarization [2, 14]."
"Enlightened by the above findings, we further propose UniTrans, an Unified code Translation framework, applicable to various LLMs, for unleashing their power in this field."
"Extensive experiments are conducted on six settings of translation datasets between Python, Java, and C++. Three recent LLMs of diverse sizes, including GPT-3.5, and LLaMA-13B/7B, are tested with UniTrans, and all achieve substantial improvements in terms of computational accuracy and exact match accuracy among almost all translation settings, showing the universal effectiveness of UniTrans in practice."

Főbb Kivonatok

Exploring and Unleashing the Power of Large Language Models in Automated Code Translation

by Zhen Yang,Fa... : arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14646.pdf

Mélyebb kérdések

How can the auto-generated test cases in UniTrans be further improved to better capture the requirements and edge cases of the source programs?

In UniTrans, the auto-generated test cases play a crucial role in guiding the code translation process and identifying errors in the translated programs. To enhance the effectiveness of these test cases in capturing the requirements and edge cases of the source programs, several improvements can be considered:

Diversification of Test Cases: Instead of relying solely on randomly generated test cases, UniTrans can incorporate a more diverse set of test cases that cover a wide range of scenarios, including boundary cases, corner cases, and complex logic paths. This diversity will help in uncovering potential issues in the translation process.

Dynamic Test Case Generation: Implementing a dynamic test case generation mechanism that adapts based on the complexity of the source program can be beneficial. This approach can prioritize generating test cases for critical or complex parts of the code, ensuring thorough coverage.

Feedback Loop for Test Case Improvement: UniTrans can incorporate a feedback loop mechanism where the results of test case execution are analyzed to identify patterns of errors or missed edge cases. This feedback can then be used to refine the test case generation process for future translations.

Incorporation of Real-World Data: To better simulate real-world scenarios, UniTrans can integrate actual usage data or historical test cases from software development projects. This real-world data can provide more realistic and relevant test cases for the translation process.

Collaborative Test Case Generation: Introducing a collaborative test case generation feature where developers can contribute their own test cases or validate existing ones can enhance the quality and coverage of the test suite. This crowdsourced approach can bring in diverse perspectives and insights.

By implementing these improvements, UniTrans can ensure that the auto-generated test cases effectively capture the requirements and edge cases of the source programs, leading to more accurate and reliable code translations.

What other techniques, beyond test case generation and translation repair, could be explored to address the limitations of LLMs in automated code translation tasks?

In addition to test case generation and translation repair, several other techniques can be explored to address the limitations of Large Language Models (LLMs) in automated code translation tasks:

Semantic Understanding Models: Incorporating semantic understanding models that can analyze the context and intent of the code snippets can help LLMs better comprehend the source programs. These models can provide additional context to improve the accuracy of translations.

Domain-Specific Fine-Tuning: Fine-tuning LLMs on domain-specific code repositories or datasets can enhance their understanding of specialized programming languages or industry-specific code patterns. This targeted fine-tuning can improve the translation quality for specific domains.

Code Summarization: Utilizing code summarization techniques to generate concise and informative summaries of the source code can assist LLMs in capturing the essential logic and functionality of the programs. These summaries can guide the translation process more effectively.

Interactive Learning: Implementing interactive learning mechanisms where developers can provide feedback on the translated code and corrections in real-time can help LLMs learn from human input and improve their translation accuracy iteratively.

Multi-Modal Learning: Exploring multi-modal learning approaches that combine code with other modalities like comments, documentation, or diagrams can provide additional context for LLMs to generate more accurate translations.

By incorporating these techniques alongside test case generation and translation repair, LLMs can overcome their limitations and achieve higher accuracy and reliability in automated code translation tasks.

Given the promising results of UniTrans, how can the framework be extended to support translation between a wider range of programming languages, including domain-specific or legacy languages?

To extend the UniTrans framework to support translation between a wider range of programming languages, including domain-specific or legacy languages, the following strategies can be considered:

Language Model Expansion: Incorporating additional language models trained specifically for domain-specific or legacy languages can broaden the language support of UniTrans. These models can be fine-tuned on relevant datasets to improve translation accuracy for specialized languages.

Custom Prompt Templates: Developing custom prompt templates tailored to the syntax and conventions of domain-specific or legacy languages can guide the translation process effectively. These templates can provide language-specific instructions and constraints for the LLMs.

Dataset Augmentation: Augmenting the training dataset with examples from domain-specific or legacy languages can enhance the LLMs' understanding of these languages and improve their translation capabilities. Curating a diverse and representative dataset is essential for supporting a wider range of languages.

Adaptive Learning Mechanisms: Implementing adaptive learning mechanisms that can dynamically adjust the translation approach based on the characteristics of the target language can optimize the translation process for different language types. This adaptability can ensure accurate translations across diverse language domains.

Collaboration with Language Experts: Collaborating with language experts or domain specialists to validate translations, provide feedback, and refine the translation models can enhance the framework's language support. Domain-specific knowledge can be integrated to improve the accuracy and relevance of translations.

By implementing these extensions and strategies, UniTrans can effectively support translation between a wider range of programming languages, including domain-specific or legacy languages, and maintain its high performance and accuracy in diverse language translation tasks.