AI-assisted software development

AI-assisted software development is the use of artificial intelligence agents to augment the software development life cycle. It leverages large language models (LLMs), 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.^[1]

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.^[2]

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.^[3]

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.^[1]

Challenges

The incorporation of AI tools has introduced new ethical dilemmas and intellectual property challenges. The ownership of AI-generated code is unclear: who is responsible for the generated end-product? Also unclear are the ethical responsibilities of generated code.^[4] Changes in the role of software engineers are inevitable.^[5][6]

Industry perspectives

Technology sector leaders have highlighted the transformative potential of AI-assisted software development. In an 'Unlocking AI Potential' session of 'Advancing AI 2025' hosted by AMD Developer Central, Andrew Ng and Lisa Su emphasized the strategic and operational implications of integrating AI tools into development workflows. Ng noted that AI systems are increasingly capable of “helping programmers focus on higher-level problem solving”, while Su framed the shift as “an opportunity to redefine performance and productivity across industries.”^[7]

See also

• Integrated development environment
• Vibe coding
• GitHub Copilot
• Google DeepMind AlphaCode
• No-code development platform
• Machine learning
• Natural language processing
• Code completion