AI-Based Automated Software Testing Framework for Enhancing Testing Efficiency and Product Quality

Abstract

The increasing complexity of software systems, distributed architectures, Internet of Things (IoT) environments, and data-intensive applications has intensified the need for efficient and adaptive software testing. Conventional testing approaches frequently depend on manually designed test cases, predefined execution sequences, and extensive human intervention, creating challenges in regression testing, defect identification, security validation, and quality assurance. This research proposes an AI-based automated software testing framework designed to improve testing efficiency and product quality through intelligent test generation, prioritization, execution, defect identification, and feedback-driven optimization. The proposed framework theoretically integrates machine intelligence with automated testing processes and incorporates security-oriented considerations derived from research on wireless sensor networks, IoT vulnerabilities, routing security, data trust, and service-oriented computing. The methodology conceptualizes a multi-layer framework consisting of software input analysis, intelligent test-case generation, risk-based prioritization, automated execution, defect classification, quality assessment, and continuous feedback. Particular emphasis is placed on the applicability of deep learning to automated testing, where learning-based mechanisms can identify complex behavioral patterns and improve testing decisions over successive execution cycles. The analysis indicates that AI-supported automation can reduce repetitive testing effort, improve test prioritization, strengthen defect detection, and provide more systematic quality evaluation. However, model-training requirements, data quality, interpretability, security of testing infrastructure, and false-positive or false-negative predictions remain important limitations. The proposed framework therefore positions AI not as a replacement for conventional software testing principles but as an intelligent augmentation mechanism capable of improving scalability, adaptability, and quality-oriented decision making.