Impact of Automation in Software Testing on Defect DiscoveryRates

Abstract

Software Development Life Cycle processes depend heavily on software testing activities to verify essential software elements like reliability and quality and performance outcomes. Manual testing approaches consume too much time and produce numerous errors due to their insufficient capability in handling complex modern applications. Software automation represents a revolutionized testing technique which develops efficiency alongside better scalability alongside higher defect discovery frequency. The investigation explores how automation techniques improve software testing by boosting defect discovery but simultaneously reducing the entire testing duration and financial requirements. The investigation utilizes research from numerous studies together with experimental results to study present-day automation frameworks and machine learning-based testing methods for continuous testing methods. Test defect identification performs better through automated testing because software testing covers more targets in less time while reducing operator mistakes. The implementation of automation in a live software development project delivered three major advantages: the detection of defects rose by 50% and testing duration decreased by 60% and testing spread to new software elements. Switching to automated testing leads to installation expenses while also requiring ongoing script updates and potential problems with updated software systems. The paper examines new test automation trends through research of AI-based systems alongside codeless testing frameworks alongside shift-left testing approaches to maximize defect identification outcomes. According to this research automation functions as an essential condition which drives reliable and efficient software testing operations. Organizations benefit most from automation when they use strategic planning to deploy automation while handling related difficulties. Marketing research should focus on AI-based deep learning automation because it will help improve both defect discovery and predictive analytic functions within the software testing framework.