A Multimodal Approach to Software Quality Assurance: Integrating Static Analysis, Dynamic Testing, and AI-based Anomaly Detection
Gopinath Kathiresan · International Journal of Innovative Research in Computer and Communication Engineering · 2024
: The combination of software architecture evolutions and cloud computing and cyber-physical systems creates advanced complexity when ensuring software reliability and security and efficiency. The once typical software quality assurance (SQA) practices using manual reviews and isolated testing methods fail to provide acceptable modern results anymore. This study develops a multimodal software quality assurance enhancement approach which combines static analysis together with dynamic testing and AI anomaly detection techniques. Software quality examines both potential defects alongside security vulnerabilities through code-level static analysis before running the program while dynamic testing evaluates real-time functionalities and security features. AI-based anomaly detection systems develop through machine learning models which help software testing teams by predicting failures as well as detecting security threats and adjusting testing strategies in real-time. When these technologies work together it reduces undetected defects while attaining higher software security and quality levels and decreasing testing requirements. The paper explores implementation barriers together with ethical matters and evolving AI-powered software testing patterns while discussing the future trajectory of automated predictive and adaptive SQA methods