AI-assisted software development
AI-assisted software development is the use of artificial intelligence agents to augment the software development life cycle. It uses large language models, natural language processing, and other AI technologies to assist software developers in a range of tasks from initial code generation to subsequent debugging, testing and documentation.
Technologies
Code generation
LLMs that have been trained on source code repositories are able to generate functional code from natural language prompts. Such models have knowledge of programming syntax, common design patterns and best practices in a variety of programming languages.Intelligent code completion
AI agents using pre-trained and fine-tuned LLMs can predict and suggest code completions based on context, going beyond simple keyword matching to infer the developer's intent and picture the broader structure of the developing codebase. An analysis has shown that such use of LLMs significantly enhances code completion performance across several programming languages and contexts, and the resulting capability of predicting relevant code snippets based on context and partial input boosts developer productivity substantially.Testing, debugging, code review and analysis
AI is used to automatically generate test cases, identify potential bugs, and suggest fixes. LLMs trained on historical bug data can enable prediction of likely failure points in generated code. Similarly, AI agents are used to perform static code analysis, identify security vulnerabilities, suggest performance improvements and ensure adherence to coding standards and best practices.Beyond detection, researchers have explored using LLMs for automated program repair, where models propose candidate patches for buggy code. Off-the-shelf LLMs have been reported to repair some security-relevant defects in a zero-shot setting, including issues categorized by the Common Weakness Enumeration, being comparable to contemporary, non-AI bug fixing tools. These approaches build on LLMs’ code-generation capability and the resulting patches still require validation through software testing, static program analysis, and human code review.