GPT4 research topic for "write a research proposal for a master's thesis in computer science on the topic of Automatic translation of C to Rust with Language Models""

topic.md

Title: Automatic Translation of C to Rust using Language Models

Abstract:

The proposed research aims to develop an automatic translation system for converting C language code to Rust language code, using state-of-the-art natural language processing techniques and deep learning models. The primary goal is to facilitate the migration of legacy C codebases to Rust, ensuring safer, more efficient, and more maintainable software systems.

Background:

C is a widely used programming language, known for its efficiency and versatility. However, it lacks certain safety features, such as memory management and type safety, which can result in vulnerabilities and undefined behavior. Rust, a systems programming language designed for safety and performance, has gained popularity for its memory safety guarantees and ease of maintenance.

While migrating from C to Rust can offer significant benefits, the process is often challenging and time-consuming, particularly for large codebases. Automating this process using language models could significantly reduce the effort required, improve code quality, and accelerate the adoption of Rust.

Objectives:

1. Investigate existing techniques for automatic code translation and identify their limitations.
2. Develop a novel approach for translating C code to Rust, leveraging state-of-the-art language models.
3. Evaluate the performance of the proposed approach on various benchmark codebases.
4. Compare the translated Rust code with manually written Rust code in terms of safety, maintainability, and performance.

Methods:

1. Literature review: Conduct a comprehensive review of existing studies on automatic code translation, with a focus on C to Rust translation methods and the use of language models in code generation.
2. Dataset creation: Compile a diverse dataset of C and Rust code, including open-source projects and synthetic examples, to be used for training and evaluation purposes.
3. Model selection and training: Select an appropriate language model (e.g., Transformer-based models like GPT) and fine-tune it on the dataset to generate Rust code from C code input.
4. Evaluation framework: Design a set of quantitative and qualitative metrics for evaluating the translated Rust code, such as compile success rate, runtime performance, memory usage, and adherence to Rust coding standards.
5. Comparative analysis: Compare the generated Rust code with manually written Rust code to assess the quality and efficiency of the translation process.

Expected Outcomes:

1. A comprehensive understanding of the current state of automatic code translation techniques and their limitations.
2. A novel approach for C to Rust translation using state-of-the-art language models, capable of generating high-quality Rust code from C code input.
3. An evaluation framework for assessing the quality and efficiency of the translated Rust code.
4. Insights into the potential benefits and challenges of using language models for automatic code translation, and recommendations for future research in this area.

Timeline:

1. Months 1-2: Conduct a literature review and compile a dataset of C and Rust code.
2. Months 3-4: Fine-tune the selected language model and develop the translation approach.
3. Months 5-6: Evaluate the performance of the proposed approach and conduct a comparative analysis.
4. Months 7-8: Analyze the results, draw conclusions, and write the master's thesis.

Budget:

The budget will cover the cost of computational resources required for model training and evaluation, as well as any software licenses and data access fees. Additionally, funds will be allocated for conference attendance and publication costs.