This publication presents the design and evaluation of an AI-powered code assistant aimed at improving developer productivity. By leveraging large language models (LLMs), the system provides context-aware code suggestions, automated debugging support, and documentation generation. The assistant was tested across multiple programming languages, and results demonstrate a significant reduction in development time, as well as increased code quality and consistency.
The rapid growth of software development complexity has created demand for intelligent tools that help programmers write, debug, and maintain code efficiently. Traditional IDEs offer syntax highlighting and autocompletion, but they lack semantic understanding of code. Recent advances in artificial intelligence, particularly transformer-based LLMs, enable context-sensitive reasoning about code. This paper introduces an AI-powered code assistant capable of providing real-time support to developers.
Several tools, such as GitHub Copilot, TabNine, and Kite, have attempted to integrate AI into software development. While these systems provide useful code completions, they often struggle with accuracy, domain adaptation, and explainability. Our approach builds upon these foundations by integrating debugging insights, documentation synthesis, and performance optimization recommendations into one assistant.
The system is built using a fine-tuned LLM trained on a diverse corpus of open-source repositories. A retrieval-augmented generation (RAG) pipeline enriches responses with external knowledge. The assistant integrates with common IDEs via extensions, capturing context from project files, recent edits, and error logs. We benchmarked performance across tasks such as code completion, bug fixing, and documentation.
Experiments were conducted with 50 developers across varying levels of expertise. Participants used the assistant in real coding sessions, measured against control groups without AI assistance. Metrics included task completion time, number of errors, and subjective satisfaction scores collected via surveys
The assistant reduced task completion time by an average of 32% and lowered syntax/logic errors by 27%. Developers reported improved confidence in their coding and appreciated auto-generated documentation. However, challenges remain in handling highly domain-specific code and ensuring consistent accuracy across languages.
Findings suggest that AI code assistants have the potential to transform developer workflows. The main challenges include managing hallucinations, maintaining privacy when processing proprietary code, and ensuring adaptability to different coding standards. Future research should explore hybrid approaches combining symbolic reasoning with neural models.
This work highlights the potential of AI in software engineering, demonstrating tangible improvements in productivity and code quality. While limitations exist, AI-powered assistants can evolve into indispensable companions for developers, augmenting rather than replacing human expertise.
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We thank the open-source community for providing datasets and the developers who participated in our user studies.
Additional code snippets, evaluation details, and survey templates are included in the supplementary material.