Towards Data-Driven Software Engineering: Integrating Machine Learning for Smarter Development Lifecycle Management

Abstract

The rapid evolution of software systems has heightened the demand for efficiency, reliability, and adaptability across the development lifecycle. Traditional software engineering practices, while structured, often struggle to manage complex, dynamic, and large-scale projects. Machine Learning (ML) has emerged as a transformative technology that enables data-driven decision-making, predictive analytics, and automation in software engineering. This paper explores the integration of ML techniques across different phases of the Software Development Lifecycle (SDLC), including requirements analysis, design, coding, testing, maintenance, and project management. The study highlights how ML-driven models enhance defect prediction, effort estimation, test case prioritisation, and adaptive maintenance. Additionally, challenges such as data quality, interpretability, scalability, and ethical implications are examined. The paper concludes by outlining future research directions, emphasising the need for explainable ML models and hybrid approaches that combine domain expertise with data-driven intelligence to build smarter, more resilient software systems.