AI-Driven Automated Defect Prediction and Software Testing for Quality Improvement
Keywords:
Artificial Intelligence, Defect Prediction, Automated Software Testing, Machine LearningAbstract
The increasing complexity of contemporary software systems has intensified the need for automated mechanisms capable of identifying defects before they propagate into later stages of development and deployment. Conventional testing approaches remain valuable, but their effectiveness can be constrained by the scale of software artifacts, heterogeneous execution environments, and the increasing number of possible test conditions. This research develops a conceptual AI-driven framework for automated defect prediction and software testing that integrates software-data preprocessing, defect-risk prediction, intelligent test prioritization, automated test execution, feedback analysis, and continuous model refinement. The methodology positions defect prediction as a risk-estimation problem in which historical and structural characteristics of software artifacts are transformed into actionable testing priorities. The theoretical design also draws on principles of adaptive control, feedback-driven decision making, and intelligent automation represented in the supplied literature. Particular attention is given to the transferability of these principles into software quality engineering, where prediction and testing should operate as a closed feedback loop rather than as isolated activities. The proposed framework is evaluated conceptually through its expected effects on defect identification, testing efficiency, prioritization, regression coverage, and quality improvement. The analysis indicates that AI can provide the greatest value when prediction outputs are directly connected to test-selection and execution mechanisms. However, model bias, inadequate training data, false positives, explainability, and domain shift remain significant limitations. The study concludes that AI-driven testing should augment rather than completely replace engineering judgment and should be implemented through continuously monitored, feedback-oriented quality processes.
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A. Esquenazi, M. Talaty, A. Packel and M. Saulino, “The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury,” Am. J. Phys. Med. Rehabilit., vol. 91, no. 11, pp. 911–921, 2012.
A. Yatsun and S. Jatsun, “Modeling quasi-static gait of a person wearing lower limb exoskeleton,” in International Conference on Industrial Engineering, Cham: Springer, 2018, pp. 565–575.
D. Shi, W. Zhang, W. Zhang and X. Ding, “A review on lower limb rehabilitation exoskeleton robots,” Chin. J. Mech. Eng., vol. 32, no. 1, pp. 1–11, 2019.
D. Shi, W. Zhang, W. Zhang, L. Ju and X. Ding, “Human-centred adaptive control of lower limb rehabilitation robot based on human–robot interaction dynamic model,” Mech. Mach. Theory, vol. 162, pp. 104340, 2021.
J. C. Wu and Z. Popovi ́c, “Terrain-adaptive bipedal locomotion control,” ACM Trans. Graphics (TOG), vol. 29, no. 4, pp. 1–10, 2010.
J. F. Veneman, R. Kruidhof, E. E. Hekman, R. Ekkelenkamp, E. H. Van Asseldonk and H. Van Der Kooij, “Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 15, no. 3, pp. 379–386, 2007.
J. Pratt and G. Pratt, “Intuitive control of a planar bipedal walking robot,” in Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No. 98CH36146), IEEE, 1998, Vol. 3, pp. 2014–2021.
L. Zhou, W. Chen, J. Wang, S. Bai, H. Yu and Y. Zhang, “A novel precision measuring parallel mechanism for the closed-loop control of a biologically inspired lower limb exoskeleton,” IEEE/ASME Trans. Mechatr. vol. 23, no. 6, pp. 2693–2703, 2018.
S. H. Collins, A. Ruina, “A bipedal walking robot with efficient and human-like gait,”, in Proceedings of the 2005 IEEE international conference on robotics and automation, IEEE, 2005, pp. 1983–1988.
S. H. Collins, M. Wisse and A. Ruina, “A three-dimensional passive-dynamic walking robot with two legs and knees,” Int. J. Robotics Res., vol. 20, no. 7, pp. 607–615, 2001.
S. Jatsun, L. Vorochaeva, A. Yatsun and A. Malchikov, “Theoretical and experimental studies of transverse dimensional gait of five-link mobile robot on rough surface,” in 2015 10th International Symposium on Mechatronics and its Applications (ISMA), 2015, pp. 1–6.
S. Jatsun, S. Savin, A. Yatsun and R. Turlapov, “Adaptive control system for exoskeleton performing sit-to-stand motion,” in 2015 10th International Symposium on Mechatronics and Its Applications (ISMA). IEEE, 2015, pp. 1–6.
W. T. Miller, “Real-time neural network control of a biped walking robot,” IEEE Control Syst. Mag., vol. 14, no. 1, pp. 41–48, 1994.
W. Zhang, W. Zhang, X. Ding and L. Sun, “Optimization of the rotational asymmetric parallel mechanism for hip rehabilitation with force transmission factors,” J. Mech. Robotics, vol. 12, no. 4, 2020.
Philip, P. G. (2024). Artificial Intelligence-Driven Project Risk Prediction Models: Enhancing Decision-Making Accuracy in Large-Scale Infrastructure Projects . American Journal of Technology, 3(1), 52–69. https://doi.org/10.58425/ajt.v3i1.571
Ramamurthy, K. (2023). AI-Driven Test Automation Frameworks for the Modern Software Quality Engineering. International Journal of Emerging Trends in Computer Science and Information Technology, 4(4), 257-269.
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