Skip to main content
SpringerLink
Log in

Testing the consistency of business data objects using extended static testing of CRUD matrices

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Static testing is used to detect software defects in the earlier phases of the software development lifecycle, which makes the total costs caused by defects lower and the software development project less risky. Different types of static testing have been introduced and are used in software projects. In this paper, we focus on static testing related to data consistency in a software system. In particular, we propose extensions to contemporary static testing techniques based on CRUD matrices, employing cross-verifications between various types of CRUD matrices made by different parties at various stages of the software project. Based on performed experiments, the proposed static testing technique significantly improves the consistency of Data Cycle Test cases. Together with this trend, we observe growing potential of test cases to detect data consistency defects in the system under test, when utilizing the proposed technique.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. https://www.mantisbt.org/index.php

References

  1. Arévalo, G., Falleri, J.R., Huchard, M., Nebut, C.: Building abstractions in class models: formal concept analysis in a model-driven approach. In: MoDELS, vol. 4199, pp. 513–527. Springer, Berlin (2006)

  2. Awad, A., Decker, G., Lohmann, N.: Diagnosing and Repairing Data Anomalies in Process Models, pp. 5–16. Springer, Berlin (2010). doi:10.1007/978-3-642-12186-9-2

  3. Briand, L., Labiche, Y., Lin, Q.: Improving the coverage criteria of uml state machines using data flow analysis. Softw. Test. Verif. Reliab. 20(3), 177–207 (2010). doi:10.1002/stvr.v20:3

  4. Briand, L., Labiche, Y., Liu, Y.: Combining uml sequence and state machine diagrams for data-flow based integration testing. In: Proceedings of the 8th European Conference on Modelling Foundations and Applications, ECMFA’12, pp. 74–89. Springer, Berlin (2012). doi:10.1007/978-3-642-31491-9-8

  5. Briand, L.C., Labiche, Y., Lin, Q.: Improving statechart testing criteria using data flow information. In: 16th IEEE International Symposium on Software Reliability Engineering (ISSRE’05), pp. 10–104 (2005). doi:10.1109/ISSRE.2005.24

  6. Bures, M., Cerny, T., Klima, M.: Prioritized Process Test: More Efficiency in Testing of Business Processes and Workflows, pp. 585–593. Springer, Singapore (2017). doi:10.1007/978-981-10-4154-9-67

  7. Carbonnel, J., Huchard, M., Miralles, A., Nebut, C.: Feature model composition assisted by formal concept analysis. In: 12th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE), pp. 28–29 (2017)

  8. Cellier, P., Ducassé, M., Ferré, S., Ridoux, O.: Formal concept analysis enhances fault localization in software. Lect. Notes Comput. Sci. 4933, 273–288 (2008)

    Article  MATH  Google Scholar 

  9. Chandra, A., Singhal, A.: Study of unit and data flow testing in object-oriented and aspect-oriented programming. In: Innovation and Challenges in Cyber Security (ICICCS-INBUSH), 2016 International Conference on, pp. 245–250. IEEE (2016)

  10. Denaro, G., Margara, A., Pezze, M., Vivanti, M.: Dynamic data flow testing of object oriented systems. In: Proceedings of the 37th International Conference on Software Engineering-Volume 1, pp. 947–958. IEEE Press (2015)

  11. Denaro, G., Pezze, M., Vivanti, M.: On the right objectives of data flow testing. In: Software Testing, Verification and Validation (ICST), 2014 IEEE Seventh International Conference on, pp. 71–80. IEEE (2014)

  12. Dwarakanath, A., Jankiti, A.: Minimum number of test paths for prime path and other structural coverage criteria. In: Proceedings of the 26th IFIP WG 6.1 International Conference on Testing Software and Systems—Volume 8763, ICTSS 2014, pp. 63–79. Springer, New York Inc., New York (2014). doi:10.1007/978-3-662-44857-1-5

  13. Frajtak, K., Bures, M., Jelinek, I.: Exploratory testing supported by automated reengineering of model of the system under test. Clust. Comput. 20(1), 855–865 (2017). doi:10.1007/s10586-017-0773-z

  14. Grood, D.J.D.: TestGoal: Result-Driven Testing, 1st edn. Springer Publishing Company, Heidelberg (2008)

    Google Scholar 

  15. Hema, M., Anup, S., Sen, K., Bagchi, A.: Detecting data flow errors in workflows: a systematic graph traversal approach (2007)

  16. Jorgensen, P.C.: Software testing: a craftsmans approach. CRC Press, Hoboken (2016)

    Google Scholar 

  17. Jukic, B., Jukic, N., Nestorov, S.: Process and data logic integration: Logical links between uml use case narratives and er diagrams. J. Comput. Inf. Technol. 21(3), 161–170 (2013)

    Article  Google Scholar 

  18. Koomen, T., Aalst, L.V.D., Broekman, B., Vroon, M.: TMap Next, for Result-driven Testing. UTN Publishers, ’s-Hertogenbosch (2013)

    Google Scholar 

  19. Kumar, S., Yadav, D., Khan, D.: Artificial bee colony based test data generation for data-flow testing. Indian J. Sci. Technol. 9(39) (2016)

  20. Küster, J.M., Ryndina, K., Gall, H.: Generation of business process models for object life cycle compliance. In: Proceedings of the 5th International Conference on Business Process Management, BPM’07, pp. 165–181. Springer, Berlin (2007). http://dl.acm.org/citation.cfm?id=1793114.1793131

  21. Li, N., Li, F., Offutt, J.: Better algorithms to minimize the cost of test paths. In: Proceedings of the 2012 IEEE Fifth International Conference on Software Testing, Verification and Validation, ICST ’12, pp. 280–289. IEEE Computer Society, Washington, DC (2012). doi:10.1109/ICST.2012.108

  22. Moser, S., Martens, A., Gorlach, K., Amme, W., Godlinski, A.: Advanced verification of distributed WS-BPEL business processes incorporating CSSA-based data flow analysis. In: IEEE International Conference on Services Computing (SCC 2007), pp. 98–105 (2007). doi:10.1109/SCC.2007.22

  23. Nielson, F., Nielson, H.R., Hankin, C.: Principles of program analysis. Springer, Berlin (2015)

    MATH  Google Scholar 

  24. Poelmans, J., Dedene, G., Snoeck, M., Viaene, S.: Using formal concept analysis for the verification of process-data matrices in conceptual domain models. In: Proceedings of the IASTED International Conference on Software Engineering, pp. 79–86. Acta Press (2010)

  25. Prabu, M., Narasimhan, D., Raghuram, S.: An effective tool for optimizing the number of test paths in data flow testing for anomaly detection. In: Computational Intelligence, Cyber Security and Computational Models, pp. 505–518. Springer, Berlin (2016)

  26. Su, T., Wu, K., Miao, W., Pu, G., He, J., Chen, Y., Su, Z.: A survey on data-flow testing. ACM Comput. Surv. 50(1), 5 (2017)

    Article  Google Scholar 

  27. Sun, S.X., Zhao, J.L., Nunamaker, J.F., Sheng, O.R.L.: Formulating the data-flow perspective for business process management. Inf. Syst. Res. 17(4), 374–391 (2006). doi:10.1287/isre.1060.0105

  28. Sundari, M.H., Sen, A.K., Bagchi, A.: Detecting data flow errors in workflows: a systematic graph traversal approach. In: WITS 2007—Proceedings, 17th Annual Workshop on Information Technologies and Systems, pp. 133–139 (2007). www.scopus.com

  29. Tilley, T., Cole, R., Becker, P., Eklund, P.: A survey of formal concept analysis support for software engineering activities. Formal Concept Anal. 3626, 250–271 (2005)

    Article  MATH  Google Scholar 

  30. Trčka, N., van der Aalst, W.M.P., Sidorova, N.: Data-Flow Anti-Patterns: Discovering Data-Flow Errors in Workflows, pp. 425–439. Springer, Berlin (2009)

    Google Scholar 

  31. Waheed, S.Z., Qamar, U.: Data flow based test case generation algorithm for object oriented integration testing. In: Software Engineering and Service Science (ICSESS), 2015 6th IEEE International Conference on, pp. 423–427. IEEE (2015)

  32. Wedyan, F., Ghosh, S., Vijayasarathy, L.R.: An approach and tool for measurement of state variable based data-flow test coverage for aspect-oriented programs. Information and Software Technology 59, 233 – 254 (2015). doi:10.1016/j.infsof.2014.11.008. http://www.sciencedirect.com/science/article/pii/S0950584914002547

Download references

Acknowledgements

This research is conducted as a part of the project TACR TH02010296 Quality Assurance System for Internet of Things Technology and internal grant of CTU in Prague SGS17/097/OHK3/1T/13.

Author information

Authors and Affiliations

  1. Department of Computer Science, FEE Czech Technical University in Prague, Karlovo Namesti 13, 121 35, Prague, Czech Republic

    Miroslav Bures, Karel Frajtak & Bestoun S. Ahmed

  2. Department of Computer Science, ECS Baylor University, One Bear Place #97141, Waco, TX, 76798, USA

    Tomas Cerny

  3. College of Engineering, Salahaddin University-Erbil, Kurdistan, Iraq

    Bestoun S. Ahmed

Authors
  1. Miroslav Bures
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tomas Cerny
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karel Frajtak
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bestoun S. Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tomas Cerny.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bures, M., Cerny, T., Frajtak, K. et al. Testing the consistency of business data objects using extended static testing of CRUD matrices. Cluster Comput 22 (Suppl 1), 963–976 (2019). https://doi.org/10.1007/s10586-017-1118-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1118-7

Keywords

Search

Navigation