O
Orclever
Back to Journal
Research Article Open AccessOrclever Native

Change Impact Analysis Case Study for Aviation: Mutation Testing

Nurbanu Hınık1✉,
Özcan Çağırıcı2,
Ufuk Sakarya3
1Turkish Aerospace Industries
2Turkish Aerospace Industries
3Yildiz Technical University
Received:Apr 23, 2022→Accepted:May 31, 2022→Published:June 7, 2022
DOI: 10.56038/ejrnd.v2i2.58
Vol. 2, No. 2
1 Views0 Downloads0 Cited by
AI SummaryAI-generated · verify against the full text

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. 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. 2.baeldung. (2016). Intro to JaCoCo | Baeldung. In Baeldung. https://www.baeldung.com/jacocoLink
  3. 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. 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. 5.Firme, R. (2011). Software considerations in airborne systems and equipment certification. Rtca, Inc.
  6. 6.http://pitest.org. (n.d.).Link
  7. 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. 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. 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. 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. 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. 12.Musco, V., Monperrus, M., & Preux, P. (2017). A Generative Model of Software Dependency Graphs to Better Understand Software Evolution.
  13. 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. 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. 15.Rierson, L. (2013). Developing safety-critical software : a practical guide for aviation software and DO-178c compliance. Taylor & Francis.
  16. 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. 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. 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. 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. 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
Download PDF
Cite This Article
Hınık, N., Çağırıcı, Ö., Sakarya, U. (2022). Change Impact Analysis Case Study for Aviation: Mutation Testing. *The European Journal of Research and Development*, 2(2), 213 - 223. https://doi.org/10.56038/ejrnd.v2i2.58

Bibliographic Info

JournalThe European Journal of Research and Development
Volume2
Issue2
Pages213–223
PublishedJune 7, 2022
eISSN2822-2296

Indexing & License

Open AccessCC BY 4.0CrossRef DOIORCIDOAI-PMH

More from The European Journal of Research and Development

Challenges in Maize Root Phenotyping: Preprocessing Limits and Class Imbalance in Deep Learning

Hüdanur Engin, Ali Murat Tiryaki

2026 · Vol 6 · Issue 1

The Bleaching of Woven Fabrics Using the Foam Application Technique

Aylin Kuşen, Onur Balcı, Koray Pektaş

2026 · Vol 6 · Issue 1

Automated Monkeypox Disease Classification Using Texture and Focus-Based Image Features

Tuğba Şentürk, Çiğdem Gülüzar Altıntop, Fatma Latifoğlu

2026 · Vol 6 · Issue 1

EEG-Based Assessment of Stress Levels Using Time–Frequency Features and Machine Learning

Sevde Samsa, Çiğdem Gülüzar Altıntop

2026 · Vol 6 · Issue 1

A Compact Non-Intrusive Measurement System for Critical Dimensions and Calibration Chart Generation of Underground Fuel Tanks

İlker Değirmencioğlu, Savaş Barış, Yusuf Kaya

2025 · Vol 5 · Issue 1

An AI-Based Question–Answering System for Corporate Documents: VK ArtiFin

Zeynep Örpek, Büşra Tural, Zeynep Destan

2025 · Vol 5 · Issue 1

Browse all articles in The European Journal of Research and Development →