In recent years, there has been an increase in the use of automated source code generation using transformer-based generative models. These models are capable of generating functional code based on developers' requirements. However, recent research has shown that the automatically generated source code can contain vulnerabilities and other quality issues. Despite efforts to improve code generation models, retraining and fine-tuning large language models is time-consuming and resource-intensive. To address these challenges, the researchers propose FRANC, a lightweight framework for recommending more secure and high-quality source code derived from transformer-based code generation models. FRANC includes a static filter that ensures the generated code is compilable by applying heuristics. It also incorporates a quality-aware ranker that sorts the code snippets based on their quality scores. Additionally, prompt engineering techniques are used to fix persistent quality issues. The framework was evaluated using five Python and Java code generation models and six prompt datasets, including a newly created one called SOEval. The results showed that the static filter improved Java suggestions by 9% to 46% regarding compilability and Python suggestions by 10% to 43%. The average improvement over the NDCG@10 score for the ranking system was 0.0763, indicating better ranking of high-quality code snippets. The repairing techniques were able to fix the highest 80% of prompts. One notable aspect of FRANC is that it does not require retraining or fine-tuning of language models. Instead, it filters out vulnerable and low-quality code from the model's output without modifying the model itself. This approach reduces costs associated with modifying existing models while still addressing quality issues. In related work, previous studies have focused on improving code generation models through various methods such as property-specific continuous vectors or incorporating human feedback during training. However, these approaches either require fine-tuning or modification of the model itself. In contrast, FRANC provides a lightweight solution that filters out vulnerable and low-quality code without the need for retraining or modifying the original model. In conclusion, FRANC offers a novel framework for enhancing the quality of source code generated by transformer-based models. It provides a static filter, quality-aware ranking system, and prompt engineering techniques to address vulnerabilities and other quality issues. The evaluation results demonstrate significant improvements in compilability and ranking of code snippets.
- Increase in use of automated source code generation using transformer-based generative models
- Generated source code can contain vulnerabilities and quality issues
- FRANC is a lightweight framework for recommending secure and high-quality source code
- FRANC includes a static filter to ensure compilability and a quality-aware ranker for sorting code snippets
- Prompt engineering techniques are used to fix persistent quality issues
- Evaluation results show improvements in compilability and ranking of code snippets
- FRANC does not require retraining or fine-tuning of language models, reducing costs associated with modifying existing models
- Previous studies have focused on fine-tuning or modifying the model itself, while FRANC offers a lightweight solution without these requirements.
Key Points1. More people are using computer programs that automatically write code using special models.
- Sometimes, the code that these programs generate can have mistakes or problems.
- FRANC is a special tool that helps recommend good and safe code to use.
- FRANC has a way to check if the code will work and also sorts the code based on how good it is.
- Special techniques are used to fix any problems with the code.
Definitions
- Automated: When something is done by a machine without needing a person to do it.
- Source code: Instructions written in a programming language that tell a computer what to do.
- Vulnerabilities: Weaknesses or flaws in something that can be taken advantage of by others.
- Quality issues: Problems or mistakes in something that make it not work well or be of low quality.
- Framework: A set of tools or rules that help with building something specific, like software or websites.
- Static filter: A tool that checks if something meets certain requirements without actually running it.
- Compilability: The ability for source code to be turned into an executable program by a compiler.
- Ranker: Something that puts things in order based on how good they are compared to each other.
- Prompt engineering techniques: Special methods used to improve the instructions given to the automated program.
- Evaluation results: The findings or conclusions from testing or studying something.
- Retraining: Teaching something again from scratch, usually because there have
Enhancing Source Code Quality with FRANC: A Lightweight Framework for Automated Code Generation
In recent years, there has been an increase in the use of automated source code generation using transformer-based generative models. These models are capable of generating functional code based on developers' requirements. However, recent research has shown that the automatically generated source code can contain vulnerabilities and other quality issues. Despite efforts to improve code generation models, retraining and fine-tuning large language models is time-consuming and resource-intensive. To address these challenges, researchers have proposed a lightweight framework called FRANC for recommending more secure and high-quality source code derived from transformer-based code generation models.
What is FRANC?
FRANC (Framework for Recommending Automatically Generated Code) is a lightweight framework designed to enhance the quality of source code generated by transformer-based generative models without requiring any modifications or retraining of the model itself. The framework includes three components: a static filter, a quality-aware ranker, and prompt engineering techniques.
The static filter ensures that all generated snippets are compilable by applying heuristics such as checking for syntax errors or missing imports/declarations. It also checks whether each snippet contains any known security vulnerabilities or coding conventions violations before allowing it to be ranked by the system's quality score algorithm.
The quality score algorithm ranks each snippet based on its overall readability, maintainability, complexity level, security risk level, etc., using metrics such as cyclomatic complexity or lines of codes (LOC). This allows developers to quickly identify which snippets are most likely to produce higher quality results when used in their projects.
Finally, prompt engineering techniques are used to fix persistent issues identified by the static filter and ranking system such as incorrect variable names or missing declarations/imports. This helps ensure that all generated snippets meet certain standards before being accepted into production systems.
Evaluation Results
To evaluate FRANC's effectiveness at improving source code quality from transformer-based generative models, researchers tested it against five Python and Java datasets containing six different prompts including one newly created dataset called SOEval (Stack Overflow Evaluation). The evaluation results showed significant improvements in both compilability scores (9%-46% improvement) and ranking scores (0.0763 average improvement over NDCG@10). Additionally, prompt engineering techniques were able to fix 80% of persistent issues identified by the static filter and ranking system combined with manual inspection from experts in software development best practices..
Conclusion
In conclusion, FRANC offers a novel approach for enhancing the quality of source code generated by transformer-based generative models without requiring any modification or retraining of existing language models - reducing costs associated with modifying existing systems while still addressing common vulnerabilities and other potential flaws in automatically generated source codes . The evaluation results demonstrate significant improvements in compilability scores as well as better rankings for high-quality snippets compared to traditional methods relying on human feedback during training or property specific continuous vectors . As such , this lightweight solution provides an effective way for developers looking to generate reliable , secure , high -quality codes quickly without having to invest additional resources into retraining existing language models .
