AI-Powered Code Autocompletion and Bug Detection for Developers

Omkar Reddy Polu

Abstract: Today's software programming has changed into extensive productivity, fewer human errors, and therefore helps enhance the reliability of software based on AI - powered code autocompletion and bug detection. This research takes into account various machine learning - based autocompletion models such as Codex and AlphaCode, which provide context - aware and syntactically correct suggestions in as much as 45% fewer keystrokes. It also examines some AI - assisted bug detection techniques such as Graph Neural Networks, symbolic execution, or static analysis that give users the ability to detect syntactic error, logical inconsistency, and security vulnerabilities with an accuracy above 90%. However, even with such advances, there are still some problems, including AI hallucinations, false positives, run - time expensive, and explainability. The next wave of improvement will incorporate human and AI interaction, knowledge distillation for efficiency, explainable AI (XAI), adversarial training, and federated learning. Combining these into the DevSecOps pipeline will allow debugging and security analysis of software in near real - time while automating code generation and improving the security robustness of the software using AI.

Keywords: Artificial Intelligence, Code Autocompletion, Bug Detection, Machine Learning, Large Language Models, Codex, AlphaCode, Graph Neural Networks, Symbolic Execution, Static Analysis, AI Hallucinations, False Positives, Explainable AI, Federated Learning, DevSecOps, Software Security, Automated Debugging, Code Efficiency