Scalable Cloud-Based Implementation of the Mutant-Driven Test Sequence Optimization (MDT-TSO) Framework for Small and Medium-Scale Industries

Background/Objectives: One of the basic elements of the contemporary DevOps and continuous integration pipelines is the testing of cloud-based software. However, the Small and Medium-Scale Industries (SMSIS) tend to be constrained due to the high cost of testing, lengthy duration of implementation and ineffective utilization of cloud services. This research targets the development of a scalable and resource-efficient cloud testing model that harnesses as much fault detection capability as possible with the minimal cost and time of execution so that advanced cloud testing will be accessible to SMSIs. Method: A scalable cloud-based architecture of Mutant-Driven Test Sequence Optimization (MDT-TSO) is suggested in this paper. Mutant Driven Testing is used to improve faults coverage through systematic assertion mutation and Test Sequence Optimization is used to do ranking with fuzzy-logic based on occurrence probability, functional interactivity and criticality. The framework is deployed on the dynamically scalable cloud platform and experimented with ANT-TCO, Covrel+ and classical regression-based testing methods. Findings: Experimentally determined MDT-TSO can save cost (35-40 percent) and even saves 25-30 percent of time to fault detection with no compromise in the reliability of the test as tested under functional or non-functional tests. The results indicate that there is a consistent improvement in the performance, which demonstrates that MDT-TSO can be scaled, its fault detection rate is good, and utilizes resources in a cloud testing environment. The research is dedicated to controlled cloud environments; it is recommended that the large-scale industrial DevOps pipelines and heterogeneous cloud systems should be additionally proved. Novelty: The innovation is hybrid combination of fault coverage by using mutations and the prioritization of the tests by the use of fuzzy-logic and the environment is cloud-scale one. The framework enables resource conscious and dynamic test scheduling and is better than statical and single criteria optimization method. Keywords: Cloud Testing, Mutant-Driven Testing, Test Sequence Optimization, Fault Detection Efficiency, Fuzzy Logic, Scalability, Cost Optimization, SME Software Quality