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AI-assisted software development
AI software development optimisation From Wikipedia, the free encyclopedia
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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
Summarize
Perspective
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]
Application maintenance
AI-assisted tools are being increasingly used to support application maintenance tasks, which often consume a majority of developer time. Such tools carry out a range of tasks:
- Codebase exploration: Mapping relationships between components and tracing execution flows.
- Intent discovery: Identifying the purpose behind complex functions.
- Documentation generation: Creating or enhancing comments and usage examples.
- Legacy system support: Deciphering poorly documented code where original authors may no longer be available.
- Impact analysis: Predicting the effects of code changes across interconnected modules.
Such capabilities reduce onboarding time for new developers, improve long-term maintainability, and mitigate technical debt. AI-powered maintenance workflows have particular applicability in legacy systems, where traditional manual comprehension is time-intensive.[4]
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Industry adoption
Major software companies have integrated AI-assisted development tools into their workflows, with many reporting significant productivity gains.[3][5]
Gains
Among the gains brought by AI agents to software development are:[2]
- Increased productivity: Developers can focus on higher-level design and problem-solving while AI is tasked with routine coding tasks.
- Reduced errors: AI agents can intercept common errors and offer corrective changes before code deployment.
- Faster prototyping: Rapid code generation enables increased experimentation on prototypes, with expert feedback.
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.[6] Changes in the role of software engineers are inevitable.[7][8]
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.”[9]
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Ongoing research
The field continues to evolve with ongoing research into:[5]
- Better context awareness: Improving AI agents' ability to gain the degree of understanding of entire codebases and project requirements that expert human software developers possess.
- Personalization: Harmonizing AI assistance with individual developer preferences and coding styles.
- Ethical AI development: Ensuring that AI agents promote good software engineering practices and steer clear of biases.
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See also
References
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