The Intersection of Data Science and Software Quality Engineering

Noah George

Noah George Department of Computer Science, University of Harvard

Keywords: Data Science, Software Quality Engineering, Data-Driven Decision-Making, Advanced Analytics, Machine Learning in Software Testing, Defect Prediction, Software Reliability, Collaborative Software Development, Case Studies, Best Practices

Abstract

The intersection of data science and software quality engineering represents a dynamic confluence where the principles of data-driven decision-making converge with the imperative for robust software systems. This paper explores the symbiotic relationship between these two domains, examining how data science methodologies enhance software quality engineering practices and, reciprocally, how software quality engineering ensures the reliability and integrity of data science applications. From leveraging advanced analytics for comprehensive software testing to employing machine learning in defect prediction and resolution, the synergy between data science and software quality engineering is reshaping the landscape of software development. The paper delves into case studies and best practices, illuminating the transformative potential when these disciplines collaborate effectively. As organizations strive for excellence in both data-driven insights and software reliability, understanding and harnessing the intersection of data science and software quality engineering emerges as a strategic imperative.