Towards Sustainable Software Testing Practices in IT Firms

As the demand for environmentally responsible and cost-efficient software development grows, sustainable software testing practices have become a critical focus. This study examines the role of automation, optimization techniques, AI/ML integration, and efficient test environments in achieving sustainability goals within software testing processes. Using a mixed-method research approach, the findings reveal that efficient test environments leveraging virtualization have the greatest impact, significantly reducing idle resource usage and energy consumption. Automation and optimization techniques enhance workflow efficiency, minimize resource wastage, and ensure timely, high-quality outputs. While AI/ML technologies show transformative potential in predictive analytics and selfhealing processes, their adoption is constrained by high implementation costs and limited organizational expertise. Testing Process Maturity (PRM) emerged as a key moderating factor, amplifying the benefits of automation but requiring balance to avoid rigid, inflexible workflows. Barriers such as high initial costs, resistance to change, and limited awareness highlight the need for organizational cultural shifts, scalable implementation strategies, and collaborative industry standards. This study provides actionable recommendations for aligning software testing with environmental, economic, and social sustainability objectives, offering a roadmap for organizations to achieve eco-efficient and resilient software development.