Abstract
Lean processes focus on doing only necessary things in an efficient way. Artificial intelligence and Machine Learning offer new opportunities to optimizing processes. The presented approach demonstrates an improvement of the test process by using Machine Learning as a support tool for test management. The scope is the semi-automation of the selection of regression tests. The proposed lean testing process uses Machine Learning as a supporting machine, while keeping the human test manager in charge of the adequate test case selection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Rothermel, G., Untch, R.H., Chu, C., Harrold, M.J.: Test case prioritization: an empirical study. In: International Conference on Software Maintenance and Evolution, pp. 179–189 (1999)
Gligoric, M., Eloussi, L., Marinov, D.: Practical regression test selection with dynamic file dependencies. In: International Symposium on Software Testing and Analysis, pp. 211–222 (2015)
Zhang, L.: Hybrid regression test selection. In: Proceedings of the 40th International Conference on Software Engineering (ICSE 2018), pp. 199–209. ACM, New York (2018). https://doi.org/10.1145/3180155.3180198
Henard, C., Mike, P., Harman, M., Jia, Y., Le Traon, Y.: Comparing white-box and black-box test prioritization. In: Proceedings of the 38th International Conference on Software Engineering (ICSE 2016), pp. 523–534. ACM, New York (2016). https://doi.org/10.1145/2884781.2884791
Miranda, B., Cruciani, E., Verdecchia, R., Bertolino, A.: FAST approaches to scalable similarity-based test case prioritization. In: Proceedings of the 40th International Conference on Software Engineering (ICSE 2018), pp. 222–232. ACM, New York (2018). https://doi.org/10.1145/3180155.3180210
Liang, J., Elbaum, S., Rothermel, G.: Redefining prioritization: continuous prioritization for continuous integration, pp. 688–698 (2018). https://doi.org/10.1145/3180155.3180213
Lachmann, R., Schulze, S., Nieke, M., Seidl, C., Schaefer, I.: System-level test case prioritization using machine learning, pp. 361–368 (2016). https://doi.org/10.1109/icmla.2016.0065
https://www.istqb.org/downloads/syllabi/foundation-level-syllabus.html
Ohno, T.: Toyota Production System – Beyond Large-Scale Production. Productivity Press, Cambridge (1988)
Boden, M.A.: Creativity and artificial intelligence (1999). https://doi.org/10.1016/s0004-3702(98)00055-1
Wang, F., Yang, S.C., Yang, Y.L.: Regression testing based on neural networks and program slicing techniques. In: Wang, Y., Li, T. (eds.) Practical Applications of Intelligent Systems. AINSC, vol. 124, pp. 409–418. Springer, Berlin (2011). https://doi.org/10.1007/978-3-642-25658-5_50
Agarwal, D., Tamir, D., Last, M., Kandel, A.: A comparative study of artificial neural networks and info-fuzzy networks as automated oracles in software testing. IEEE Trans. Syst. Man Cybern-Part A: Syst. Hum. 42(5), 1183–1193 (2012)
Engstrom, E., Runeson, P., Skoglund, M.: A systematic review on regression test selection techniques. Inf. Softw Technol. 52, 14–30 (2010)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson, London (2010). ISBN-13: 978-0136042594
Rothermel, G., Untch, R.H., Chu, C., Harrold, M.J.: Prioritizing test cases for regression testing. IEEE Trans. Soft. Eng. 27(10), 929–948 (2001)
Korsaa, M., et al.: The SPI Manifesto and the ECQA SPI manager certification scheme. J. Softw.: Evol. Process 24(5), 525–540 (2012)
Messnarz, R., et al.: Social responsibility aspects supporting the success of SPI. J. Softw.: Evol. Process 26(3), 284–294 (2014)
Sanchez-Gordon, M.L., Colomo-Palacios, R., Amescua, A.: Towards measuring the impact of the SPI manifesto: a systematic review. In: Proceedings of European System and Software Process Improvement and Innovation Conference, pp. 100–110 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Poth, A., Beck, Q., Riel, A. (2019). Artificial Intelligence Helps Making Quality Assurance Processes Leaner. In: Walker, A., O'Connor, R., Messnarz, R. (eds) Systems, Software and Services Process Improvement. EuroSPI 2019. Communications in Computer and Information Science, vol 1060. Springer, Cham. https://doi.org/10.1007/978-3-030-28005-5_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-28005-5_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-28004-8
Online ISBN: 978-3-030-28005-5
eBook Packages: Computer ScienceComputer Science (R0)