From Code Generation to Software Testing: AI Copilot with Context-Based RAG
This addresses efficiency and accuracy challenges in software testing for developers, though it is incremental as it extends previous AI-assisted programming work.
The paper tackles the bottleneck in software testing by proposing an AI copilot system that synchronizes bug detection with codebase updates, achieving a 31.2% improvement in bug detection accuracy, a 12.6% increase in critical test coverage, and a 10.5% higher user acceptance rate.
The rapid pace of large-scale software development places increasing demands on traditional testing methodologies, often leading to bottlenecks in efficiency, accuracy, and coverage. We propose a novel perspective on software testing by positing bug detection and coding with fewer bugs as two interconnected problems that share a common goal, which is reducing bugs with limited resources. We extend our previous work on AI-assisted programming, which supports code auto-completion and chatbot-powered Q&A, to the realm of software testing. We introduce Copilot for Testing, an automated testing system that synchronizes bug detection with codebase updates, leveraging context-based Retrieval Augmented Generation (RAG) to enhance the capabilities of large language models (LLMs). Our evaluation demonstrates a 31.2% improvement in bug detection accuracy, a 12.6% increase in critical test coverage, and a 10.5% higher user acceptance rate, highlighting the transformative potential of AI-driven technologies in modern software development practices.