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Role of Artificial Intelligence in Software Quality Assurance

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Intelligent Systems and Applications (IntelliSys 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 295))

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Artificial intelligence has taken its place in almost every industry individual operate in, it has become integral part of applications and systems in our surrounding. The world quality report estimates that 64% of the companies will implement Artificial Intelligence (AI) for the Software Quality Assurance (SQA) processes. It is predicted that in the very near future, SQA engineer will not be testing manually. But they would be acquiring skills to use AI enabled tools techniques for Software Quality assurances in order to contribute to the business growth. AI proves to play a crucial role in the software testing as it makes processes leaner and yields more accurate results. This paper will discuss about how Artificial Intelligence makes impact in the software testing industry. The new era of Quality Assurance will be dominated by the power of Artificial Intelligence as it significantly reduces time and increase efficiency of the firm to develop more sophisticated software. This studies focuses on artificial intelligence applications in software testing, which of the AI algorithms are popularly adopted by the QA industry, Furthermore, this paper talks about real issues that resides in the industry for instance; why young testers are more flexible towards adopting latest technological changes.

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  1. Hourani, H., Hammad, A., Lafi, M.: 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, April 2019

    Google Scholar 

  2. Sharma, D., Chandra, P.: Applicability of soft computing and optimization algorithms in software testing and metrics–a brief review. In: International Conference on Soft Computing and Pattern Recognition, pp. 535–546. Springer, Cham, December 2016

    Google Scholar 

  3. Mera, E., Lopez-García, P., Hermenegildo, M.: Integrating software testing and run-time checking in an assertion verification framework. In: International Conference on Logic Programming, pp. 281–295. Springer, Berlin, July 2009

    Google Scholar 

  4. Kanstrén, T.: Experiences in testing and analysing data intensive systems. In: 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), pp. 589–590. IEEE, July 2017

    Google Scholar 

  5. Karpov, Y.L., Karpov, L.E., Smetanin, Y.G.: Adaptation of general concepts of software testing to neural networks. Program. Comput. Softw. 44(5), 324–334 (2018)

    Article  MathSciNet  Google Scholar 

  6. Li, B., Vendome, C., Linares-Vásquez, M., Poshyvanyk, D., Kraft, N.A.: Automatically documenting unit test cases. In: 2016 IEEE International Conference on Software Testing, Verification and Validation (ICST), pp. 341–352. IEEE, April 2016

    Google Scholar 

  7. Tan, T.B., Cheng, W.K.: Software testing levels in internet of things (IoT) architecture. In: International Computer Symposium, pp. 385–390. Springer, Singapore, December 2018

    Google Scholar 

  8. Yang, S., Man, T., Xu, J., Zeng, F., Li, K.: RGA: a lightweight and effective regeneration genetic algorithm for coverage-oriented software test data generation. Inf. Softw. Technol. 76, 19–30 (2016)

    Article  Google Scholar 

  9. Grano, G., Titov, T.V., Panichella, S., Gall, H.C.: How high will it be? Using machine learning models to predict branch coverage in automated testing. In: 2018 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE), pp. 19–24. IEEE, March 2018

    Google Scholar 

  10. Sarah, C., Jane, B., Rónán, O.B., Ben, R.: Quality assurance for digital learning object repositories: issues for the metadata creation process. ALT-J 12(1), 5–20 (2004)

    Article  Google Scholar 

  11. Malviya, R.: Revolutionizing Quality Assurance with AI and Automation, Infosys (2020)

    Google Scholar 

  12. Poth, A., Heimann, C.: How to innovate software quality assurance and testing in large enterprises?. In: European Conference on Software Process Improvement, pp. 437–442. Springer, Cham, September 2018

    Google Scholar 

  13. Gabor, T., et al.: The scenario coevolution paradigm: adaptive quality assurance for adaptive systems. Int. J. Softw. Tools Technol. Transfer 22(4), 457–476 (2020).

    Article  Google Scholar 

  14. Dao-Phan, V., Huynh-Quyet, T., Le-Quoc, V.: Developing method for optimizing cost of software quality assurance based on regression-based model. In: International Conference on Nature of Computation and Communication, Cham (2016)

    Google Scholar 

  15. Crews, B.O., Drees, J.C., Greene, D.N.: Data-driven quality assurance to prevent erroneous test results. Crit. Rev. Clin. Lab. Sci. 57(3), 146–160 (2020)

    Article  Google Scholar 

  16. Lee, C., Ho, G., Choy, K., Pang, G.: A RFID-based recursive process mining system for quality assurance in the garment industry. Int. J. Prod. Res. 52(14), 4216–4238 (2017)

    Article  Google Scholar 

  17. Poth, A., Beck, Q., Riel, A.: Artificial intelligence helps making quality assurance processes leaner. In: Walker, A., O’Connor, R.V., Messnarz, R. (eds.) EuroSPI 2019. CCIS, vol. 1060, pp. 722–730. Springer, Cham (2019).

    Chapter  Google Scholar 

  18. Mahmoud, T., Ahmed, B.S.: An efficient strategy for covering array construction with fuzzy logic-based adaptive swarm optimization for software testing use. Expert Syst. Appl. 42(22), 8753–8765 (2017)

    Article  Google Scholar 

  19. Li, Z., Li, M., Liu, Y., Geng, J.: Identify coincidental correct test cases based on fuzzy classification. In: International Conference on Software Analysis, Testing and Evolution (SATE), Kunming, China (2016)

    Google Scholar 

  20. Khuranaa, N., Chillar, R.S.: Test case generation and optimization using UML models and genetic algorithm. Procedia Comput. Sci. 57, 996–1004 (2016)

    Article  Google Scholar 

  21. Ansari, A., Shagufta, M.B., Fatima, A.S., Tehreem, S.: Constructing test cases using natural language processing. In: Third International Conference on Advances in Electrical. Electronics, Information, Communication and Bio-Informatics (AEEICB), Chennai, India (2017)

    Google Scholar 

  22. Shehab, M., Abualigah, L., Jarrah, M.I., Alomari, O.A.: Artificial intelligence in software engineering and inverse: review. Int. J. Comput. Integr. Manuf. 33, 1129–1144 (2020)

    Article  Google Scholar 

  23. Lachmann, R., Schulze, S., Nieke, M., Seidl, C., Schaefer, I.: System-level test case prioritization using machine learning. In: 15th IEEE International Conference on Machine Learning and Applications (ICMLA), Anaheim, CA, USA (2017)

    Google Scholar 

  24. AlShathry, O.: Operational profile modeling as a risk assessment tool for software quality techniques. In: International Conference on Computational Science and Computational Intelligence, Las Vegas, NV, USA (2016)

    Google Scholar 

  25. Saad, A., Saad, M., Emaduddin, S.M., Ullah, R.: Optimization of Bug Reporting System (BRS): artificial intelligence based method to handle duplicate bug report. In: International Conference on Intelligent Technologies and Applications, Singapore (2020)

    Google Scholar 

  26. Umer, Q., Liu, H., Sultan, Y.: Emotion based automated priority prediction for bug reports. IEEE Access 6(10), 35743–35752 (2018)

    Article  Google Scholar 

  27. Rauf, A., Alanazi, M.N.: Using artificial intelligence to automatically test GUI. In: 9th International Conference on Computer Science & Education, Vancouver, BC, Canada (2016)

    Google Scholar 

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Correspondence to Sarang Shaikh .

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Ramchand, S., Shaikh, S., Alam, I. (2022). Role of Artificial Intelligence in Software Quality Assurance. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 295. Springer, Cham.

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