@article{4750,
  author = {Pit Pichappan},
  title = {Requirement Traceability and Intelligent Test Selection for Industrial IoT Systems},
  journal = {International Journal of Web Applications},
  year = {2026},
  volume = {18},
  number = {2},
  doi = {https://doi.org/10.6025/ijwa/2026/18/2/66-93},
  url = {https://www.dline.info/ijwa/fulltext/v18n2/ijwav18n2_2.pdf},
  abstract = {Industrial Internet of Things (IIoT) systems underpin modern smart manufacturing, yet their heterogeneous,
interconnected architectures pose significant challenges for regression testing and software validation. This
study proposes a requirement-aware intelligent regression test prioritization framework designed to enhance
fault detection efficiency and testing scalability in IIoT environments. The framework integrates requirement
traceability analysis, multi-factor prioritization scoring, and optimization-driven test selection to
dynamically order regression test cases based on fault detection capability, execution time, requirement
criticality, and interface complexity. Empirical evaluation using a GSM2017 Mobile IoT dataset comprising
51 requirements and 41 test cases demonstrates that the proposed Dynamic Prioritization strategy achieves
superior performance across multiple metrics: Average Percentage of Fault Detection (APFD) of 0.92, runtime
reduction of 44.14%, fault coverage of 96%, and requirement coverage retention of 98.04%. Statistical
validation via Wilcoxon Signed-Rank and Friedman tests confirms the significance of observed improvements.
Convergence analysis indicates that Genetic Algorithm and Simulated Annealing metaheuristics effectively
balance exploration and exploitation in large-scale test suite optimization. Feature importance analysis
reveals fault detection capability as the dominant prioritization factor, while Pareto optimization
demonstrates achievable trade-offs between execution efficiency and verification completeness. Machine
learning classification using XGBoost and Neural Networks further validates the framework’s capacity for
autonomous, AI-driven test prioritization. Collectively, these findings establish a robust, scalable
methodology for intelligent IIoT regression testing that supports requirement traceability, adaptive
prioritization, and statistically verified performance gains, advancing the state of software validation in
Industry 4.0 ecosystems.},
}