Control Systems and Computers

Introduction. Model-based test cases generation is a popular strategy for test automation. It helps to reduce time spent on the development of a test suite and can improve level of coverage. However, many reports show shortage of such test cases in poor quality and doubtable efficiency.

Purpose. The main goal of the proposed method is cost-effective validation, assessment, debugging and concretization of generated test cases. The method helps improve quality and efficiency of the test cases, make their scenario meaningful and goal-oriented. The method also develops debugging facilities and simplifies data dependency analysis and test scenario editing.

Methods. We propose an automated post-processing method which allows to evaluate path that is examined by the test case, and to make safe changes to the path which will eliminate the shortcomings while leaving the coverage targets of the test case unharmed. The method is based on visualization of the path along the control flow graph of the model with additional information about factual evaluation history of all variables and possible alternative variants of behavior. For consistent substitution of certain values in the signal parameters, which would determine the artifacts of the test environment (such as, for example, files, databases, etc.) and check boundary cases (in predicates of conditions, indexing of arrays, etc.), a method of interactive specification of symbolic traces has been developed.

Results. The role of the user in deciding whether to add a test case to the project test suite and make changes to it remains crucial, but to reduce labor intensity, the following processes are automated: evaluation of test scenarios according to certain objective characteristics (level of coverage, ability to detect defects, data cohesion, etc.); highlighting of possible alternatives for making corrections; consistent updating of computations for the corresponding corrections. A prototype was developed based on the proposed methods. The empirical results demonstrated a positive impact on the overall efficiency (ability to detect defects and reduce resource consumption) and quality (meaningfulness, readability, maintenance, usefulness for debugging, etc) of the generated test suites. The method allows to make automatically generated test cases trustable and usable.

Conclusion. The proposed toolkit significantly reduces the time spent on researching the results of test generation and validation of the obtained tests and their editing. Unlike existing simulation methods, the proposed method not only informs about the values of variables, but also explores the history of their computations and additionally provides information about admissible alternatives. Further we plan to improve the process of localizing the causes of test failure at execution phase to speed up the search for defects.

Download full text! (On Ukrainian)

Keywords: model-based testing, test case generation, debugging, test case validation.

1. Kolchin, A., Potiyenko, S., Weigert, T. (2019). “Challenges for automated, model-based test scenario generation”. In Comm. in Computer and Inf. Sci., vol. 1078, pp. 182-194.
   https://doi.org/10.1007/978-3-030-30275-7_15
2. Kolchin, A., Potiyenko, S. (2022). “Extending Data Flow Coverage to Test Constraint Refinements”. In: ter Beek, M.H., Monahan, R. (eds) Integrated Formal Methods. Lecture Notes in Computer Science, vol. 13274.
   https://doi.org/10.1007/978-3-031-07727-2_17
3. Rushby, J. (2008). “Automated test generation and verified software”. Verified Software: Theories, Tools, Experiments, pp. 161-172.
   https://doi.org/10.1007/978-3-540-69149-5_18
4. Dssouli, R. et. al. (2017). “Testing the control-flow, data-flow, and time aspects of communication systems: a survey”. Advances in Computers. vol. 107, pp. 95-155.
   https://doi.org/10.1016/bs.adcom.2017.06.002
5. Inozemtseva, L., Holmes, R. (2014). “Coverage is not strongly correlated with test suite effectiveness”. In Proc. of ACM ICSE’14, pp. 435-445.
   https://doi.org/10.1145/2568225.2568271
6. Gay, G., Staats, M., Whalen, M., Heimdahl, M. (2015). “The risks of coverage-directed test case generation”. IEEE Transactions on Software Engineering. vol.41, pp. 803-819.
   https://doi.org/10.1109/TSE.2015.2421011
7. Staats, M. et. al. (2012). “On the danger of coverage directed test case generation”. Lecture Notes in Computer Science, vol. 7212, pp. 409-424.
   https://doi.org/10.1007/978-3-642-28872-2_28
8. Palomba, F., et. al. (2016). “Automatic Test Case Generation: What if Test Code Quality Matters?” In proc. of Int. Symp. on Software Testing and Analysis, pp. 130-141.
   https://doi.org/10.1145/2931037.2931057
9. Ying, M., Gay, G., Whalen, M. (2018). “Ensuring the observability of structural test obligations”. IEEE Transactions on Software Engineering, 46 (7), pp.748-772.
   https://doi.org/10.1109/TSE.2018.2869146
10. Perez, F., Font, J., Arcega, L., & Cetina, C. (2022). “Empowering the Human as the Fitness Function in Search-Based Model-Driven Engineering”. IEEE Transactions on Software Engineering, 48 (11), pp. 4553-4568.
    https://doi.org/10.1109/TSE.2021.3121253
11. Ceccato, M., et.al. (2015). “Do automatically generated test cases make debugging easier? An experimental assessment of debugging effectiveness and efficiency”. ACM Transactions on Software Engineering and Methodology, 25(1), pp. 1-38.
    https://doi.org/10.1145/2768829
12. Lucredio, D., Vincenzi, A., Almeida, E., Ahmed, I. (2023). “Test case quality: an empirical study on belief and evidence”. arXiv:2307.06410.
13. Winkler, D., Urbanke, P., Ramler, R. (2022). “What Do We Know About Readability of Test Code? – A Systematic Mapping Study”. In 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), Honolulu, HI, USA, pp. 1167-1174.
    https://doi.org/10.1109/SANER53432.2022.00135
14. Rastello, F., Tichadou, F. (2022). SSA-based Compiler Design. Springer Nature, 382 p.
    https://doi.org/10.1007/978-3-030-80515-9
15. Kolchin, A.V. (2010). “An automatic method for the dynamic construction of abstractions of states of a formal model”. Cybernetics and Systems Analysis, 46 (4), pp. 583-601. https://doi.org/10.1007/s10559-010-9235-9
16. Kolchin, A. (2018). “Interactive method for cumulative analysis of software formal models behavior”. Proc. of the 11th Int. Conf. on Programming UkrPROG’2018, CEUR-WS vol. 2139, pp. 115-123.
    https://doi.org/10.15407/pp2018.02.115
17. Kolchin, A. et. al. (2013). “An approach to creating concretized test scenarios within test automation technology for industrial software projects”. Automatic control and computer science, pp. 433-442.
    https://doi.org/10.3103/S0146411613070213
18. Godlevskiy, A.B., Potiyenko, S.V. (2010). “Obratnaya transformatsiya formul v simvol’nom modelirovanii: ot rezul’tata k iskhodnoy formule”. Problemi programuvannya, 2-3, pp. 363-368.
19. Potiyenko, S.V. (2008). “Metody pryamogo i obratnogo simvol’nogo modelirovaniya sistem, zadannykh bazovymi protokolami”. Problemi programuvannya, 4, pp. 39-45.
20. Vu, F., Leuschel, M. (2023). “Validation of Formal Models by Interactive Simulation”. In: Glässer, U., Creissac Campos, J., Méry, D., Palanque, P. (eds) Rigorous State-Based Methods. ABZ 2023. Lecture Notes in Computer Science, vol 14010. Springer, Cham.
    https://doi.org/10.1007/978-3-031-33163-3_5

Received  14.04.2024