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Optimizing AI-Assisted Code Generation

Simon Torka, Sahin Albayrak

TL;DR

The paper addresses the security, reliability, functionality, and accessibility challenges of AI-assisted code generation based on large language models. It surveys the current state, including encoder–decoder architectures and platforms, and analyzes risks such as data poisoning, prompt injection, and vulnerabilities in generated code. A conceptual framework is proposed, advocating an encoder–decoder setup (e.g., CodeT5, CodeBERT) with zero-trust data practices, iterative runtime learning, prompt-support tools, and defense-in-depth to produce secure conventional code and AI models accessible to non-experts. The work emphasizes human-centered design, governance, and evaluation, aiming to realize AI4G while ensuring safe, high-quality software development.

Abstract

In recent years, the rise of AI-assisted code-generation tools has significantly transformed software development. While code generators have mainly been used to support conventional software development, their use will be extended to powerful and secure AI systems. Systems capable of generating code, such as ChatGPT, OpenAI Codex, GitHub Copilot, and AlphaCode, take advantage of advances in machine learning (ML) and natural language processing (NLP) enabled by large language models (LLMs). However, it must be borne in mind that these models work probabilistically, which means that although they can generate complex code from natural language input, there is no guarantee for the functionality and security of the generated code. However, to fully exploit the considerable potential of this technology, the security, reliability, functionality, and quality of the generated code must be guaranteed. This paper examines the implementation of these goals to date and explores strategies to optimize them. In addition, we explore how these systems can be optimized to create safe, high-performance, and executable artificial intelligence (AI) models, and consider how to improve their accessibility to make AI development more inclusive and equitable.

Optimizing AI-Assisted Code Generation

TL;DR

The paper addresses the security, reliability, functionality, and accessibility challenges of AI-assisted code generation based on large language models. It surveys the current state, including encoder–decoder architectures and platforms, and analyzes risks such as data poisoning, prompt injection, and vulnerabilities in generated code. A conceptual framework is proposed, advocating an encoder–decoder setup (e.g., CodeT5, CodeBERT) with zero-trust data practices, iterative runtime learning, prompt-support tools, and defense-in-depth to produce secure conventional code and AI models accessible to non-experts. The work emphasizes human-centered design, governance, and evaluation, aiming to realize AI4G while ensuring safe, high-quality software development.

Abstract

In recent years, the rise of AI-assisted code-generation tools has significantly transformed software development. While code generators have mainly been used to support conventional software development, their use will be extended to powerful and secure AI systems. Systems capable of generating code, such as ChatGPT, OpenAI Codex, GitHub Copilot, and AlphaCode, take advantage of advances in machine learning (ML) and natural language processing (NLP) enabled by large language models (LLMs). However, it must be borne in mind that these models work probabilistically, which means that although they can generate complex code from natural language input, there is no guarantee for the functionality and security of the generated code. However, to fully exploit the considerable potential of this technology, the security, reliability, functionality, and quality of the generated code must be guaranteed. This paper examines the implementation of these goals to date and explores strategies to optimize them. In addition, we explore how these systems can be optimized to create safe, high-performance, and executable artificial intelligence (AI) models, and consider how to improve their accessibility to make AI development more inclusive and equitable.

Paper Structure

This paper contains 35 sections, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Current state of development
  3. AI code generation
  4. AI code generation of conventional code
  5. AI code generation of AI models
  6. Challenges and risks when using AI-based code generation
  7. Technical challenges
  8. Code quality
  9. Challenges in prompt engineering
  10. Assessing the security and trust of AI code generators based on user studies
  11. Social, legal and ethical challenges
  12. Attack possibilities and protection of AI code generators
  13. Options for attacking AI generators
  14. Defense of AI code generators
  15. Data creation
  16. ...and 20 more sections

Figures (5)

  • Figure 1: The Transformer - model architecture. Vaswani.2017.
  • Figure 2: This example shows how ChatGPT effectively solves a simple problem by generating a comprehensive Python script to manage a list of events. The script includes detailed comments to aid understanding and make it easier for non-experts to follow along. ChatGPT also provides a detailed explanation of how the code works and a sample output.
  • Figure 3: This example shows how ChatGPT effectively solves a problem using deep learning techniques by generating a comprehensive Python script to classify the Iris dataset. The output includes details of the process and code functionality, required libraries, useful side information and the code itself.
  • Figure 4: Relations between Common Attacks and Mitigations on AI Code Generators
  • Figure 5: Design of the trainings loop. Left side: Initial training of the base model. Middle: Zero-Trust training of the base model. Right: Continues training during life time.