Abstract
Process models tend to become more and more complex and, therefore, also more and more test cases are required to assure their correctness and stability during design and maintenance. However, executing hundreds or even thousands of process model test cases leads to excessive test suite execution times and, therefore, high costs. Hence, this paper presents a novel approach for process model test case selection which is able to address flexible user-driven test case selection requirements and which can integrate a diverse set of knowledge sources to select an appropriate minimal set of test cases which can be executed in minimal time. Additionally, techniques are proposed which enable the representation of unique coverage requirements and effects for each process node and process test case in a comprehensive way. For test case selection, a genetic algorithm is proposed. Its effectiveness is shown in comparison with other test case selection approaches.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Askaruinisa, A., Abirami, A.: Test case reduction technique for semantic based web services. Computer Science & Engineering 3, 566–576 (2010)
Borrego, D., Gómez-López, M.T., Gasca, R.M.: Minimizing test-point allocation to improve diagnosability in business process models. Systems and Software 11, 2725–2741 (2013)
Cardoso, J.: Process control-flow complexity metric: an empirical validation. In: Services Computing, pp. 167–173. IEEE (2006)
Eiben, A., Smith, J.: Introduction to evolutionary computing. Natural Computing Series. Springer (2008)
Farooq, U., Lam, C.P.: Evolving the quality of a model based test suite. In: Software Testing, Verification and Validation, pp. 141–149. IEEE (2009)
Farooq, U., Lam, C.P.: A max-min multiobjective technique to optimize model based test suite. In: Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing, pp. 569–574. IEEE (2009)
Gruhn, V., Laue, R.: Complexity metrics for business process models. In: Business Information Systems, pp. 1–12 (2006)
Harman, M., Jones, B.F.: Search-based software engineering. Information and Software Technology 14, 833–839 (2001)
Kaschner, K., Lohmann, N.: Automatic test case generation for interacting services. In: Feuerlicht, G., Lamersdorf, W. (eds.) ICSOC 2008. LNCS, vol. 5472, pp. 66–78. Springer, Heidelberg (2009)
Kriglstein, S., Wallner, G., Rinderle-Ma, S.: A visualization approach for difference analysis of process models and instance traffic. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 219–226. Springer, Heidelberg (2013)
Leymann, F., Roller, D.: Production workflow concepts and techniques. Prentice Hall PTR (2000)
Li, B., Qiu, D., Ji, S., Wang, D.: Automatic test case selection and generation for regression testing of composite service based on extensible bpel flow graph. In: Software Maintenance, pp. 1–10 (2010)
Li, Z.J., Sun, W., Jiang, Z.B., Zhang, X.: BPEL4WS unit testing: framework and implementation. In: Web Services, pp. 103–110. IEEE (2005)
Mendling, J.: Metrics for process models: empirical foundations of verification, error prediction, and guidelines for correctness. LNBIP, vol. 6. Springer, Heidelberg (2008)
Rinderle, S., Reichert, M., Dadam, P.: Flexible support of team processes by adaptive workflow systems. Distributed and Parallel Databases 16, 91–116 (2004)
Ruth, M.E.: Concurrency in a decentralized automatic regression test selection framework for web services. In: Mardi Gras Conference, pp. 7:1–7:8. ACM (2008)
Stoyanova, V., Petrova-Antonova, D., Ilieva, S.: Automation of test case generation and execution for testing web service orchestrations. In: Service-Oriented Systems Engineering, pp. 274–279. IEEE (2013)
Zakaria, Z., Atan, R., Ghani, A.A.A., Sani, N.F.M.: Unit testing approaches for BPEL: a systematic review. In: Asia-Pacific Software Engineering, pp. 316–322. IEEE (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Böhmer, K., Rinderle-Ma, S. (2015). A Genetic Algorithm for Automatic Business Process Test Case Selection. In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2015 Conferences. OTM 2015. Lecture Notes in Computer Science(), vol 9415. Springer, Cham. https://doi.org/10.1007/978-3-319-26148-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-26148-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26147-8
Online ISBN: 978-3-319-26148-5
eBook Packages: Computer ScienceComputer Science (R0)