Role of Data Science in Improving Software Reliability and Performance

Abstract

Reliability and performance are the most important requirements of user satisfaction and system integrity in today's software systems. Thereby, data science is the transformative field that enhances sophistication in the methods for prediction failure on the one hand and optimized resources over the improvement of the performance of the software on the other hand. This paper attempts to discuss the scenario of using data science for software improvement in reliability and performance through predictive analytics, machine learning, and real-time monitoring. It offers an overview of the key fundamentals, tools, models, and methodologies all with supporting data, tables, and sample codes. Further, it summarizes the challenges faced today, ethical issues, and future directions of research on the use of data science in software engineering.