Artificial Intelligence in Quality Assurance for Software Systems

Authors

  • Santhosh Bussa Independent Researcher, USA.

DOI:

https://doi.org/10.55544/sjmars.2.2.2

Keywords:

Software Quality Assurance, Artificial Intelligence, Machine Learning, Defect Detection, Automated Testing, User Experience, QA Challenges

Abstract

The rapid advancement in software development has taken place with the invention of a new quality assurance (QA) process for producing robust, reliable, and efficient systems. Artificial Intelligence is a "force of change" that promises automating most QA activities with promising predictive insight into the generation of dynamic test cases and intelligent detection of defects. This paper covers the theme of integrating AI with SQA through techniques such as Machine Learning, Natural Language Processing, and Neural Networks. The paper covers automation of testing, AI-driven management of defects, and enhancement of user experience as well as challenges and limitation that is encountered while implementing AI within QA. A glimpse of emerging trends illustrates the dynamic landscape of AI-driven QA.

References

[1] Amershi, S., Begel, A., Bird, C., Deline, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., & Zimmermann, T. (2019). Software engineering for machine learning: A case study. IEEE Software, 36(5), 87-95.

[2] Beller, M., Spínola, R. O., & Nugroho, A. (2018). Taking a longitudinal look at technical debt and bugs in machine learning software. In Proceedings of the 14th International Conference on Mining Software Repositories (MSR) (pp. 152-162).

[3] Chen, J., Hou, S., Chen, X., Zhang, Y., & Zhang, J. (2019). An empirical study of machine learning-based code review using static analysis. Empirical Software Engineering, 24(4), 2375-2405.

[4] Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.

[5] Daka, E., & Wallace, D. R. (2015). A survey of software fault localization techniques. ACM Computing Surveys, 48(2), 1-27.

[6] Ebert, C., & Cihak, D. (2015). Is machine learning the magic bullet for predictive quality improvement? IEEE Software, 32(6), 20-25.

[7] Garcez, A., & Zaverucha, G. (1999). The connection between neural logic and probabilistic networks. Neural Computation, 11(8), 2065-2091.

[8] Ghotra, B., McIntosh, S., & Hassan, A. E. (2015). Revisiting the impact of classification techniques on the performance of defect prediction models. In Proceedings of the 37th International Conference on Software Engineering (ICSE) (pp. 789-799).

[9] Hall, T., Beecham, S., & Bowes, D. (2012). Using an empirical study of defect prediction: A practitioner's perspective. In Proceedings of the 18th IEEE International Requirements Engineering Conference (pp. 162-171).

[10] Hinton, G. E. (2010). Connectionist learning procedures. Artificial Intelligence, 40(1-3), 185-234.

[11] Jia, Y., & Harman, M. (2011). An analysis and survey of the development of mutation testing. IEEE Transactions on Software Engineering, 37(5), 649-678.

[12] Kar, A., Iyer, M. R. S. S., & Rajan, P. (2019). Improving software testing using machine learning: A survey. Journal of Software Engineering and Applications, 12(8), 402–412.

[13] Khaled, M., & Ramadan, H. (2017). Neural networks based software defect prediction. International Journal of Advanced Computer Science and Applications, 8(6), 178-186.

[14] Kroening, D., & Strichman, O. (2008). Decision procedures: An algorithmic point of view. Springer Science & Business Media.

[15] Kumar, R. M. K. Z. H. J., & Pradhan, A. (2018). The role of reinforcement learning in automated software testing. Journal of Software Engineering, 21(3), 289–300.

[16] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.

[17] Liu, D. B., & Kumar, R. L. (2020). AI-based regression testing techniques: Challenges and future directions. ACM Computing Surveys, 52(6), 1–28.

[18] Malhotra, R. (2015). A systematic review of machine learning techniques for software fault prediction. International Journal of Software Engineering and Knowledge Engineering, 25(04), 535-562.

[19] Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval. Cambridge University Press.

[20] Menzies, T., Greenwald, J., & Frank, A. (2007). Data mining static code attributes to learn defect predictors. IEEE Transactions on Software Engineering, 33(1), 2-13.

[21] Panichella, A., Kifetew, F. M., & Tonella, P. (2018). Automated test case generation as a constraint solving problem. IEEE Transactions on Software Engineering, 44(4), 340-362.

[22] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825-2830.

[23] Pradhan, P. S. D. S. R. R. K. V. S., & Xie, L. (2019). Sentiment analysis of user feedback using deep learning: A review. International Journal of Computer Applications, 182(5), 1–7.

[24] Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. OpenAI Technical Report.

[25] Rahman, A. M. S. K., Jha, R. P. K., & Kumar, S. S. M. K. S. (2019). AI-based defect prediction in software systems: A survey. IEEE Access, 7, 129426–129443.

[26] Saha, R. K., Mondal, A. K., Keidar, I., & Neamtiu, I. (2015). Continuous verification: Promises and challenges. In Proceedings of the 2015 International Symposium on Software Testing and Analysis (pp. 257-268).

[27] Seliya, N., Khoshgoftaar, T. M., & Van Hulse, J. (2009). A study on the relationships of classifier performance metrics. In 2009 IEEE International Conference on Tools with Artificial Intelligence (pp. 59-66).

[28] Tantithamthavorn, C., McIntosh, S., Hassan, A. E., & Matsumoto, K. (2018). The impact of class imbalance and classifier choice on defect prediction. IEEE Transactions on Software Engineering, 45(3), 252-269.

[29] Wan, Z., Wu, X., & Li, Y. (2019). Predicting software defects using deep learning. In Proceedings of the 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) (pp. 1040-1050).

[30] Williams, B. G. B. R. L., & Rao, V. M. (2020). AI in DevOps: Enhancing software development and deployment. Journal of Software Engineering, 35(2), 147–162.

[31] Williams, O. S., & Liu, A. R. (2019). Understanding the application of AI in software quality assurance. Software Testing, Verification & Reliability, 29(7), 1–15.

[32] Xie, A. S. J., Liu, L. S., & Yuen, H. (2019). Generative models in test case generation. IEEE Transactions on Software Engineering, 45(4), 314–328.

[33] Zhang, F., Huang, Q., Luo, C., Qiu, X., & Wang, C. (2018). An empirical study on developer-tracker interactions in collaborative software testing. Empirical Software Engineering, 23(5), 2751-2786.

[34] Mouna Mothey. (2022). Automation in Quality Assurance: Tools and Techniques for Modern IT. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 11(1), 346–364. Retrieved from https://eduzonejournal.com/index.php/eiprmj/article/view/694282–297. Retrieved from https://ijmirm.com/index.php/ijmirm/article/view/138

[35] Mothey, M. (2022). Leveraging Digital Science for Improved QA Methodologies. Stallion Journal for Multidisciplinary Associated Research Studies, 1(6), 35–53. https://doi.org/10.55544/sjmars.1.6.7

[36] Mothey, M. (2023). Artificial Intelligence in Automated Testing Environments. Stallion Journal for Multidisciplinary Associated Research Studies, 2(4), 41–54. https://doi.org/10.55544/sjmars.2.4.5

[37] SQL in Data Engineering: Techniques for Large Datasets. (2023). International Journal of Open Publication and Exploration, ISSN: 3006-2853, 11(2), 36-51. https://ijope.com/index.php/home/article/view/165

[38] Data Integration Strategies in Cloud-Based ETL Systems. (2023). International Journal of Transcontinental Discoveries, ISSN: 3006-628X, 10(1), 48-62. https://internationaljournals.org/index.php/ijtd/article/view/116

[39] Shiramshetty, S. K. (2023). Advanced SQL Query Techniques for Data Analysis in Healthcare. Journal for Research in Applied Sciences and Biotechnology, 2(4), 248–258. https://doi.org/10.55544/jrasb.2.4.33

[40] Sai Krishna Shiramshetty "Integrating SQL with Machine Learning for Predictive Insights" Iconic Research And Engineering Journals Volume 1 Issue 10 2018 Page 287-292Sai Krishna Shiramshetty, International Journal of Computer Science and Mobile Computing, Vol.12 Issue.3, March- 2023, pg. 49-62

[41] Sai Krishna Shiramshetty. (2022). Predictive Analytics Using SQL for Operations Management. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 11(2), 433–448. Retrieved from https://eduzonejournal.com/index.php/eiprmj/article/view/693

[42] Shiramshetty, S. K. (2021). SQL BI Optimization Strategies in Finance and Banking. Integrated Journal for Research in Arts and Humanities, 1(1), 106–116. https://doi.org/10.55544/ijrah.1.1.15

[43] Sai Krishna Shiramshetty, " Data Integration Techniques for Cross-Platform Analytics, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN: 2456-3307, Volume 6, Issue 4, pp.593-599, July-August-2020. Available at doi : https://doi.org/10.32628/CSEIT2064139

[44] Sai Krishna Shiramshetty, "Big Data Analytics in Civil Engineering: Use Cases and Techniques", International Journal of Scientific Research in Civil Engineering (IJSRCE), ISSN: 2456-6667, Volume 3, Issue 1, pp.39-46, January-February.2019

[45] Mouna Mothey. (2022). Automation in Quality Assurance: Tools and Techniques for Modern IT. Eduzone: International Peer Reviewed/Refereed Multidisciplinary Journal, 11(1), 346–364. Retrieved from https://eduzonejournal.com/index.php/eiprmj/article/view/694

[46] Mothey, M. (2022). Leveraging Digital Science for Improved QA Methodologies. Stallion Journal for Multidisciplinary Associated Research Studies, 1(6), 35–53. https://doi.org/10.55544/sjmars.1.6.7

[47] Mothey, M. (2023). Artificial Intelligence in Automated Testing Environments. Stallion Journal for Multidisciplinary Associated Research Studies, 2(4), 41–54. https://doi.org/10.55544/sjmars.2.4.5

[48] SQL in Data Engineering: Techniques for Large Datasets. (2023). International Journal of Open Publication and Exploration, ISSN: 3006-2853, 11(2), 36-51. https://ijope.com/index.php/home/article/view/165

[49] Data Integration Strategies in Cloud-Based ETL Systems. (2023). International Journal of Transcontinental Discoveries, ISSN: 3006-628X, 10(1), 48-62. https://internationaljournals.org/index.php/ijtd/article/view/116

[50] Harish Goud Kola. (2022). Best Practices for Data Transformation in Healthcare ETL. Edu Journal of International Affairs and Research, ISSN: 2583-9993, 1(1), 57–73. Retrieved from https://edupublications.com/index.php/ejiar/article/view/106

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Published

2023-04-30

How to Cite

Bussa, S. (2023). Artificial Intelligence in Quality Assurance for Software Systems. Stallion Journal for Multidisciplinary Associated Research Studies, 2(2), 15–26. https://doi.org/10.55544/sjmars.2.2.2

Issue

Vol. 2 No. 2 (2023): April Issue

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