Deep Learning for Contextual Bug Detection and Automated Fixes in Software Systems

Modern software systems are becoming more and more complicated, which calls for sophisticated techniques to ensure code quality and dependability. The subtleties of complex software systems are difficult for traditional techniques to problem discovery and fixing to handle as they frequently rely on manual code reviews and static analysis tools. With a new focus on context-aware bug identification, this research explores the use of deep learning models to automate the detection and correction of problems in software code. The suggested models are made to find issues in the context of the software system by using a huge dataset of code annotated with bug information and associated remedies, together with metadata like project architecture, dependencies, and runtime circumstances. Various neural network architectures, including transformers and graph neural networks (GNNs), are explored to capture both the syntactic and semantic aspects of code while considering the contextual interactions between different code modules. This context-aware approach enhances the model’s ability to detect bugs that may arise from the interaction between components, runtime behaviors, or specific usage scenarios often missed by traditional methods. Additionally, the integration of these models into continuous integration/continuous deployment (CI/CD) pipelines is examined, enabling real-time bug detection and automated patch generation. The proposed system aims to improve code quality, reduce debugging time, and minimize human intervention, accelerating the software development lifecycle. Preliminary results demonstrate that the inclusion of contextual information significantly improves the accuracy and relevance of bug detection and fixing, paving the way for more robust and self-healing software systems.