• Manoj Bhoyar

Optimizing Software Development Lifecycle with Predictive Analytics: The title of the greatest work is Defect Prediction and Management using AI, in which a new approach is introduced to change the context of software quality assurance by using artificial intelligence and predictive analysis. This research utilizes a large systemic approach that employs machine learning techniques based on AI to analyze previous project data and predict potential software defects during initial development realizations. The present research indicates that the overall of correct classification rate for defect prediction is 73% which if brought into practice will reduce debugging time by 28% and the late stage of part identification of defect by 42%. This approach was okayed as quite efficient for large-scale enterprise application since it enhanced reliability of software developed through increase of 31%;and kept the right number of cycles for quality assurance. On the economic point of view it has been revealed that each gets 2.25 fold return within one year. The proposed research also serves a purpose contributing to the theory of AI-based software defect prediction. It offers potential for subsequent investigations in how automated code correction as well as real-time defect prevention may be employed. These approaches bear several implications, somehow enriching gears from post-mortem approaches methodologies in software quality assurance to foresighted models.

Software Defect Prediction, Artificial Intelligence, Machine Learning, Software Development Lifecycle, Predictive Analytics, Quality Assurance, DevOps

"Optimizing Software Development Lifecycle with Predictive Analytics: An AI-Based Approach to Defect Prediction and Management", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.f540-f550, September 2023, Available :http://www.jetir.org/papers/JETIR2309680.pdf

"Optimizing Software Development Lifecycle with Predictive Analytics: An AI-Based Approach to Defect Prediction and Management", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppf540-f550, September 2023, Available at : http://www.jetir.org/papers/JETIR2309680.pdf