Software Testing in the Era of AI: Leveraging Machine Learning and Automation for Efficient Quality Assurance

Chunhua Deming; Md Abul Khair; Suman Reddy Mallipeddi; Aleena Varghese

doi:10.18034/ajase.v10i1.88

Authors

Chunhua Deming NUS Graduate School (NUSGS), National University of Singapore, Singapore
Md Abul Khair Manager, Consulting Services, Hitachi Vantara, 101 Park Ave #10a, New York, NY 10178, USA
Suman Reddy Mallipeddi Lead Software Engineer, Discover Financial Services, 2500 Lake Cook Rd, Riverwoods, IL 60015, USA
Aleena Varghese Software Developer, IT WorkForce (Schneider Electric), 127 E Michigan St #100, Indianapolis, IN 46204, USA

Keywords:

Software Testing

AI Integration

Machine Learning

Automation

Testing Paradigms

AI-driven QA

Test Automation

Abstract

Automation and machine learning incorporated into software testing procedures are significant improvements over current quality assurance procedures. The potential of AI-driven testing methodologies to improve software testing's efficacy and efficiency is examined in this paper. The study's principal goals are investigating AI-driven testing methods, empirical assessments, case studies, identification of issues and policy consequences, and recommendations for responsible adoption. A thorough analysis of the body of research on AI-driven testing, including case studies, research papers, and policy documents, is part of the process. The main conclusions highlight the efficiency gains made possible by intelligent test prioritizing, automated test generation, and anomaly detection. They also discuss the difficulties and policy ramifications of bias, data security, privacy, and regulatory compliance. The creation of moral standards, legal frameworks, and educational initiatives to encourage the appropriate and ethical application of AI-driven testing methodologies are examples of policy ramifications. This study advances knowledge about AI-driven testing and offers guidance to researchers, practitioners, and legislators involved in software quality assurance.

References

Ande, J. R. P. K., & Khair, M. A. (2019). High-Performance VLSI Architectures for Artificial Intelligence and Machine Learning Applications. International Journal of Reciprocal Symmetry and Theoretical Physics, 6, 20-30. https://upright.pub/index.php/ijrstp/article/view/121

Ardagna, D., Casale, G., Ciavotta, M., Pérez, J. F., Wang, W. (2014). Quality-of-service in Cloud Computing: Modeling Techniques and Their Applications. Journal of Internet Services and Applications, 5(1), 1-17. https://doi.org/10.1186/s13174-014-0011-3

Basit, M. A., Baldwin, K. L., Kannan, V., Flahaven, E. L., Parks, C. J. (2018). Agile Acceptance Test–Driven Development of Clinical Decision Support Advisories: Feasibility of Using Open Source Software. JMIR Medical Informatics, 6(2), https://doi.org/10.2196/medinform.9679

Batarseh, F. A., Yang, R., Deng, L. (2017). A Comprehensive Model for Management and Validation of Federal Big Data Analytical Systems. Big Data Analytics, 2(1). https://doi.org/10.1186/s41044-016-0017-x

Bertolino, A., Calabro’, A., Giandomenico, F. D., Lami, G., Lonetti, F. (2018). A Tour of Secure Software Engineering Solutions for Connected Vehicles. Software Quality Journal, 26(4), 1223-1256. https://doi.org/10.1007/s11219-017-9393-3

Huang, J., Zhang, C. (2016). Debugging Concurrent Software: Advances and Challenges. Journal of Computer Science and Technology,31(5), 861-868. https://doi.org/10.1007/s11390-016-1669-8

Jiang, M., Munawar, M. A., Reidemeister, T., Ward, P. A. S. (2011). Efficient Fault Detection and Diagnosis in Complex Software Systems with Information-Theoretic Monitoring. IEEE Transactions on Dependable and Secure Computing, 8(4), 510-522. https://doi.org/10.1109/TDSC.2011.16

Karna, A. K., Chen, Y., Yu, H., Zhong, H., Zhao, J. (2018). The Role of Model Checking in Software Engineering. Frontiers of Computer Science, 12(4), 642-668. https://doi.org/10.1007/s11704-016-6192-0

Khair, M. A. (2018). Security-Centric Software Development: Integrating Secure Coding Practices into the Software Development Lifecycle. Technology & Management Review, 3, 12-26. https://upright.pub/index.php/tmr/article/view/124

Khair, M. A., Ande, J. R. P. K., Goda, D. R., & Yerram, S. R. (2019). Secure VLSI Design: Countermeasures against Hardware Trojans and Side-Channel Attacks. Engineering International, 7(2), 147–160. https://doi.org/10.18034/ei.v7i2.699

Khair, M. A., Mahadasa, R., Tuli, F. A., & Ande, J. R. P. K. (2020). Beyond Human Judgment: Exploring the Impact of Artificial Intelligence on HR Decision-Making Efficiency and Fairness. Global Disclosure of Economics and Business, 9(2), 163-176. https://doi.org/10.18034/gdeb.v9i2.730

Kreines, M. G. (2013). Methods of Computational Analysis of Semantic Models for Quality Assessment of Scientific Texts. Journal of Computer & Systems Sciences International, 52(2), 226-236. https://doi.org/10.1134/S1064230713020044

Maddula, S. S. (2018). The Impact of AI and Reciprocal Symmetry on Organizational Culture and Leadership in the Digital Economy. Engineering International, 6(2), 201–210. https://doi.org/10.18034/ei.v6i2.703

Maddula, S. S., Shajahan, M. A., & Sandu, A. K. (2019). From Data to Insights: Leveraging AI and Reciprocal Symmetry for Business Intelligence. Asian Journal of Applied Science and Engineering, 8(1), 73–84. https://doi.org/10.18034/ajase.v8i1.86

Mullangi, K. (2017). Enhancing Financial Performance through AI-driven Predictive Analytics and Reciprocal Symmetry. Asian Accounting and Auditing Advancement, 8(1), 57–66. https://4ajournal.com/article/view/89

Mullangi, K., Maddula, S. S., Shajahan, M. A., & Sandu, A. K. (2018). Artificial Intelligence, Reciprocal Symmetry, and Customer Relationship Management: A Paradigm Shift in Business. Asian Business Review, 8(3), 183–190. https://doi.org/10.18034/abr.v8i3.704

Porter, A.. Yilmaz, C., Memon, A. M., Schmidt, D. C., Natarajan, B. (2007). Skoll: A Process and Infrastructure for Distributed Continuous Quality Assurance. IEEE Transactions on Software Engineering, 33(8), 510. https://doi.org/10.1109/TSE.2007.70719

Sandu, A. K., Surarapu, P., Khair, M. A., & Mahadasa, R. (2018). Massive MIMO: Revolutionizing Wireless Communication through Massive Antenna Arrays and Beamforming. International Journal of Reciprocal Symmetry and Theoretical Physics, 5, 22-32. https://upright.pub/index.php/ijrstp/article/view/125

Seng, L. K., Ithnin, N., Said, S. Z. M. (2018). The Approaches to Quantify Web Application Security Scanners Quality: A Review. International Journal of Advanced Computer Research, 8(38), 285-312. https://doi.org/10.19101/IJACR.2018.838012

Shajahan, M. A. (2018). Fault Tolerance and Reliability in AUTOSAR Stack Development: Redundancy and Error Handling Strategies. Technology & Management Review, 3, 27-45. https://upright.pub/index.php/tmr/article/view/126

Varghese, A., & Bhuiyan, M. T. I. (2020). Emerging Trends in Compressive Sensing for Efficient Signal Acquisition and Reconstruction. Technology & Management Review, 5, 28-44. https://upright.pub/index.php/tmr/article/view/119

Yerram, S. R. (2020). AI-Driven Inventory Management with Cryptocurrency Transactions. Asian Accounting and Auditing Advancement, 11(1), 71–86. https://4ajournal.com/article/view/86

Yerram, S. R. (2021). Driving the Shift to Sustainable Industry 5.0 with Green Manufacturing Innovations. Asia Pacific Journal of Energy and Environment, 8(2), 55-66. https://doi.org/10.18034/apjee.v8i2.733

Yerram, S. R., & Varghese, A. (2018). Entrepreneurial Innovation and Export Diversification: Strategies for India’s Global Trade Expansion. American Journal of Trade and Policy, 5(3), 151–160. https://doi.org/10.18034/ajtp.v5i3.692

Yerram, S. R., Mallipeddi, S. R., Varghese, A., & Sandu, A. K. (2019). Human-Centered Software Development: Integrating User Experience (UX) Design and Agile Methodologies for Enhanced Product Quality. Asian Journal of Humanity, Art and Literature, 6(2), 203-218. https://doi.org/10.18034/ajhal.v6i2.732