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Mutation testing helps analyze changes in software to prevent errors, and is particularly important in aviation projects.
Researchers applied mutation testing to open-source Java projects and emphasized its importance in aviation projects that follow DO-178C certification processes. This involves analyzing changes to prevent errors in software. The study aimed to improve the reliability of software in critical systems like aviation.
Abstract
As the complexity of modern software systems increases, changes in software have become crucial to the software lifecycle. For this reason, it is an important issue for software developers to analyze the changes that occur in the software and to prevent the changes from causing errors in the software. In this paper, mutation testing as software test analysis is examined. Mutation tests have been implemented on open-source Java projects. In addition, for aviation projects, emphasis is placed on performing change impact analysis processes in compliance with the certification based on DO-178C processes.
Keywords
Change Impact AnalysisMutation TestingSoftware Testing
References
- 1.Alanazi, R., Gharibi, G., & Lee, Y. (2021). Facilitating program comprehension with call graph multilevel hierarchical abstractions. Journal of Systems and Software, 176, 110945. https://doi.org/https://doi.org/10.1016/j.jss.2021.110945DOI
- 2.baeldung. (2016). Intro to JaCoCo | Baeldung. In Baeldung. https://www.baeldung.com/jacocoLink
- 3.Bhattacharya, P., Iliofotou, M., Neamtiu, I., & Faloutsos, M. (2012). Graph-based analysis and prediction for software evolution. 2012 34th International Conference on Software Engineering (ICSE), 419–429. https://doi.org/10.1109/ICSE.2012.6227173DOI
- 4.Bhattacharya, P., & Neamtiu, I. (2011). Assessing Programming Language Impact on Development and Maintenance: A Study on c and C++. Proceedings of the 33rd International Conference on Software Engineering, 171–180. https://doi.org/10.1145/1985793.1985817DOI
- 5.Firme, R. (2011). Software considerations in airborne systems and equipment certification. Rtca, Inc.
- 6.http://pitest.org. (n.d.).Link
- 7.Kajko-Mattsson, M., & Yulong, F. (2005). Outlining a model of a release management process. Journal of Integrated Design & Process Science, 9, 13–25.
- 8.Lightsey, B. (2001). SYSTEMS ENGINEERING FUNDAMENTALS SUPPLEMENTARY TEXT. THE DEFENSE ACQUISITION UNIVERSITY PRESS FORT BELVOIR. https://acqnotes.com/wp-content/uploads/2017/07/DAU-Systems-Engineering-Fundamentals.pdfLink
- 9.Maisikeli, S. (2016). Evaluation of Software Degradation and Forecasting Future Development Needs in Software Evolution. International Journal of Software Engineering & Applications, 7, 49–64. https://doi.org/10.5121/ijsea.2016.7604DOI
- 10.Musco, V. (2016). Analyse de la propagation basée sur les graphes logiciels et les données synthétiques (Issue 2016LIL30053) [Université Charles de Gaulle - Lille III]. https://tel.archives-ouvertes.fr/tel-01398903Link
- 11.Musco, V., Carette, A., Monperrus, M., & Preux, P. (2016). A Learning Algorithm for Change Impact Prediction. 2016 IEEE/ACM 5th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE), 8–14. https://doi.org/10.1109/RAISE.2016.010DOI
- 12.Musco, V., Monperrus, M., & Preux, P. (2017). A Generative Model of Software Dependency Graphs to Better Understand Software Evolution.
- 13.Musco, V., Monperrus, M., & Preux, P. (2015). An Experimental Protocol for Analyzing the Accuracy of Software Error Impact Analysis. 2015 IEEE/ACM 10th International Workshop on Automation of Software Test, 60–64. https://doi.org/10.1109/AST.2015.20DOI
- 14.Musco, V., Monperrus, M., Preux, P., Yin, X., Musco, V., Neamtiu, I., & Roshan, U. (2019). A large-scale study of call graph-based impact prediction using mutation testing. Software Quality Journal, 25(3), 921–950. https://doi.org/10.1109/AITest.2019.000-1DOI
- 15.Rierson, L. (2013). Developing safety-critical software : a practical guide for aviation software and DO-178c compliance. Taylor & Francis.
- 16.Rierson, L. K. (2001). Changing safety-critical software. IEEE Aerospace and Electronic Systems Magazine, 16(6), 25–30. https://doi.org/10.1109/62.931137DOI
- 17.Semeráth, O., Nagy, A. S., & Varró, D. (2018). A Graph Solver for the Automated Generation of Consistent Domain-Specific Models. Proceedings of the 40th International Conference on Software Engineering, 969–980. https://doi.org/10.1145/3180155.3180186DOI
- 18.Szárnyas, G., KHovári, Z., Salánki, Á., & Varró, D. (2016). Towards the Characterization of Realistic Models: Evaluation of Multidisciplinary Graph Metrics. Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems, 87–94. https://doi.org/10.1145/2976767.2976786DOI
- 19.Walunj, V., Gharibi, G., Ho, D. H., & Lee, Y. (2019). GraphEvo: Characterizing and Understanding Software Evolution using Call Graphs. 2019 IEEE International Conference on Big Data (Big Data), 4799–4807. https://doi.org/10.1109/BigData47090.2019.9005560DOI
- 20.Woodward, M. R. (1993). Mutation testing—its origin and evolution. Information and Software Technology, 35(3), 163–169. https://doi.org/https://doi.org/10.1016/0950-5849(93)90053-6DOI