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
[edit]Code Generation
[edit]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
[edit]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
[edit]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]
Industry adoption
[edit]Major software companies have integrated AI-assisted development tools into their workflows, with many reporting significant productivity gains.[3][4]
Challenges
[edit]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.[5] Changes in the role of software engineers are inevitable.[6][7]
See also
[edit]- Integrated development environment
- Vibe coding
- GitHub Copilot
- No-code development platform
- Machine learning
- Natural language processing
- Code completion
References
[edit]- ^ a b "Transforming software =with generative AI". MIT Technology Review Insights. 17 October 2024. Retrieved 5 July 2025.
- ^ Soral, Sulabh (6 November 2024). "The future of coding is here: How AI is reshaping software development". Deloitte. Retrieved 6 July 2025.
- ^ a b Husein, Rasha Ahmad; Aburajouh, Hala; Catal, Cagatay (12 June 2025). "Large language models for code completion: A systematic literature review". Computer Standards & Interfaces. 92 (C) – via ACM Digital Library.
- ^ Floyd, Rebecca; Diachkova, Olga; Wilson, Julia (16 April 2024). "AI Trends Report 2024: AI's Growing Role in Software Development". Docker. Retrieved 5 July 2025.
- ^ Sauvola, Jaakko; Tarkoma, Sasu; Klemettinen, Mika; Riekki, Jukka; Doermann, David (11 March 2024). "Future of software development with generative AI". Automated Software Engineering. 31 (26) – via Springer Nature Link.
- ^ Dryka, Marcin; Pluszczewska, Bianka (9 May 2025). "Is There a Future for Software Engineers? The Impact of AI [2025]". Brainhub. Retrieved 5 July 2025.
- ^ Walsh, Philip; Gupta, Gunjan; Poitevin, Helen; Mann, Keith; Micko, Dave; Bhat, Manjunath (30 August 2024). "AI Will Not Replace Software Engineers (and May, in Fact, Require More)". Gartner Research. Retrieved 5 July 2025.
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