The Future of AI Quality Assurance: Emerging Trends, Challenges, and the Need for Automated Testing Frameworks
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V2I1P103Keywords:
Artificial Intelligence, Quality Assurance, Automated Testing, AI Bias, Deep LearningAbstract
Currently, Artificial Intelligence is evolving in various industries, but the major issue is to make it reliable, accurate, and secure. AI Quality Assurance (AIQA) is imperative for handling the risks that stem from every aspect, such as biased datasets, ethics, and performance. This paper aims to identify the new directions in the AIQA area, the shortcomings of current testing approaches, and the requirements for testing automation frameworks. This work reviews the literature, introduces a new higher-level intelligent testing framework, and reports comparative experiments. Machine Learning (ML), NL Processing, and deep learning techniques using the ongoing generational high-end software can be utilized to improve AI Testing paradigms. They indicate that the use of AI-based QA frameworks will help reduce the amount of time taken in testing while at the same time increasing the model's reliability. Some suggestions for the future of AIQA are made at the end of the paper, where the focus is placed on a self-learning testing system
Downloads
References
[1] Arpteg, A., Brinne, B., Crnkovic-Friis, L., & Bosch, J. (2018, August). Software engineering challenges of deep learning. In 2018, the 44th Euro Micro Conference on Software Engineering and Advanced Applications (SEAA) (pp. 50-59). IEEE.
[2] Basili, V. R., & Rombach, H. D. (2002). The TAME project: Towards improvement-oriented software environments. IEEE Transactions on Software Engineering, 14(6), 758-773.
[3] Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., ... & Zimmermann, T. (2019). Software engineering for machine learning: A case study. In Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice (pp. 291–300). https://doi.org/10.1109/ICSE-SEIP.2019.00042
[4] Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data, 5(2), 153-163.
[5] Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
[6] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
[7] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
[8] Test Automation Trends: Navigating the Future of Quality Assurance, Matellio, online. https://www.matellio.com/blog/test-automation-trends/
[9] Marcus, G. (2018). Deep learning: A critical appraisal. arXiv preprint arXiv:1801.00631.
[10] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). " Why should I trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144).
[11] Breck, E., Polyzotis, N., Roy, S., Whang, S. E., & Zinkevich, M. (2017). The ML test score: A rubric for ML production readiness and technical debt reduction. arXiv preprint arXiv:1706.02216.
[12] Hourani, H., Hammad, A., & Lafi, M. (2019, April). The impact of artificial intelligence on software testing. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) (pp. 565-570). IEEE.
[13] Future of Test Automation and QA in the Era of AI and Machine Learning, Qualitrix, online. https://qualitrix.com/future-of-test-automation-and-qa-in-the-era-of-ai-and-ml/
[14] Zhang, J. M., Harman, M., Ma, L., & Liu, Y. (2020). Machine learning testing: Survey, landscapes and horizons. IEEE Transactions on Software Engineering, 48(1), 1–36. https://doi.org/10.1109/TSE.2019.2962027
[15] Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., & Dennison, D. (2015). Hidden technical debt in machine learning systems. In Advances in Neural Information Processing Systems (pp. 2503–2511).
[16] Gambi, A., Toffetti, G., Pezze, M., & Succi, G. (2017). Automatically testing self-adaptive software systems. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 12(2), 1–26. https://doi.org/10.1145/3059647
[17] Future of Test Automation Tools: AI Use Cases and Beyond, Medium, online. https://sandra-parker.medium.com/future-of-test-automation-tools-ai-use-cases-and-beyond-818a90467ac4
[18] Fujii, G., Hamada, K., Ishikawa, F., Masuda, S., Matsuya, M., Myojin, T., & Ujita, Y. (2020). Guidelines for quality assurance of machine learning-based artificial intelligence. International journal of software engineering and knowledge engineering, 30(11n12), 1589-1606.
[19] Xie, X., Ho, D., Murphy, C., & Kaiser, G. (2011). Testing and validating machine learning classifiers by metamorphic testing. Journal of Systems and Software, 84(4), 544–558. https://doi.org/10.1016/j.jss.2010.11.920
[20] Islam, M. B., & Williams, L. (2016). Software quality assurance for machine learning: A survey. In Proceedings of the 2016 IEEE International Conference on Software Quality, Reliability and Security Companion (pp. 197–204). https://doi.org/10.1109/QRS-C.2016.43
[21] Barr, E. T., Harman, M., McMinn, P., Shahbaz, M., & Yoo, S. (2015). The oracle problem in software testing: A survey. IEEE Transactions on Software Engineering, 41(5), 507–525. https://doi.org/10.1109/TSE.2014.2372785
[22] Cherekar, R. (2020). DataOps and Agile Data Engineering: Accelerating Data-Driven Decision-Making. International Journal of Emerging Research in Engineering and Technology, 1(1), 31-39. https://doi.org/10.63282/3050-922X.IJERET-V1I1P104
