The proposed study aims to integrate and assess the currently available AI models to provide a facility in terms of code analysis and bug detection in software development projects. Although AI has already proven to be useful in automating the bug-detection process, the interpretability of most AI-based solutions remains low, which restricts their susceptibility to developers. To resolve this challenge, the research will improve such AI models with the use of the Explainable AI (XAI) solutions, which will explain the reasons behind bugs identified to be detected in a human readable manner. The intent of this improvement is to unlock the effect of the transparency, trust, and effectiveness of the bug identification systems. The evaluation of the process of integration is carried out with real-life codebases by comparing the output of the AI models to the outcomes obtained after applying the XAI techniques. Findings show that besides the improved accuracy in detecting bugs the improved models also provide actionable and understandable information and hence the bug faults are resolved more quickly and efficiently in the development environment.