Establishing a Scalable Test Automation and Traceability Framework for Enterprise Software Development Platforms
Keywords:
Enterprise software, Quality assurance, Continuous integration, Defect tracking, Requirements traceability, Behavior driven development, Test automation.Abstract
Software platforms used by enterprises require effective quality assurance practices to be taken to ensure that they remain reliable and compliant throughout development life cycle. The design and application of scalable verification and validation framework is presented in this paper. The requirements management, test planning, automated testing, defect tracking, and traceability are linked in the framework as one co-ordinated system. It interconnects requirements, development artifacts, test assets and quality records to allow teams to know about all the validation coverage. In the paper, an automated integration testing capability and a behavior driven testing architecture, which enhance the maintainability, reusability and speed of execution of tests are also introduced. The architecture was developed and experimented using geographically dispersed teams of engineers. Findings indicate that the quality of releases, compliance of processes and productivity of validation activities have improved. The paper defines the structure of the architecture, the traceability model, the test technique, and the results of the research were experienced in four release cycles. These results confirm the point that automation, traceability and quality governance are effective when implemented as a set of initiatives rather than considered as separate processes.
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Binamungu, L. P., Embury, S. M., & Konstantinou, N. (2020). Characterising the quality of behaviour driven development specifications. Lecture Notes in Business Information Processing, 87–102. https://doi.org/10.1007/978-3-030-49392-9_6
Escalona, M., Koch, N., & Garcia-Borgoñon, L. (2022). Lean requirements traceability automation enabled by model-driven engineering. PeerJ Computer Science, 8, e817. https://doi.org/10.7717/peerj-cs.817
Flemström, D., Potena, P., Sundmark, D., Afzal, W., & Bohlin, M. (2018). Similarity-based prioritization of test case automation. Software Quality Journal, 26(4), 1421–1449. https://doi.org/10.1007/s11219-017-9401-7
Garousi, V., Tasli, S., Sertel, O., Tokgoz, M., Herkiloglu, K., Arkin, H. F. E., & Bilir, O. (2018). Automated Testing of simulation software in the Aviation industry: An Experience report. IEEE Software, 36(4), 63–75. https://doi.org/10.1109/ms.2018.227110307
Großer, K., Riediger, V., & Jürjens, J. (2022). Requirements document relations. Software & Systems Modeling, 21(6), 1–37. https://doi.org/10.1007/s10270-021-00958-y
Islam, M. N., & Quadri, S. M. K. (2020). Framework for Automation of Cloud-Application Testing using Selenium (FACTS). Advances in Science Technology and Engineering Systems Journal, 5(1), 226–232. https://doi.org/10.25046/aj050129
Juhnke, K., Tichy, M., & Houdek, F. (2020). Challenges concerning test case specifications in automotive software testing: assessment of frequency and criticality. Software Quality Journal, 29(1), 39–100. https://doi.org/10.1007/s11219-020-09523-0
Krause, J., Kaufmann, A., Riehle, D., & Jung, M. (2022). The Benefits of Pre-Requirements Specification Traceability. The Benefits of Pre-Requirements Specification Traceability, 166–177. https://doi.org/10.1109/re54965.2022.00021
Mustafa, A., Wan-Kadir, W. M. N., Ibrahim, N., Shah, M. A., Younas, M., Khan, A., Zareei, M., & Alanazi, F. (2021). Automated Test Case Generation from Requirements: A Systematic Literature Review. Computers, Materials & Continua/Computers, Materials & Continua (Print), 67(2), 1819–1833. https://doi.org/10.32604/cmc.2021.014391
Slaughter, A. E., Permann, C. J., Miller, J. M., Alger, B. K., & Novascone, S. R. (2021). Continuous integration, In-Code documentation, and automation for nuclear quality assurance conformance. Nuclear Technology, 207(7), 923–930. https://doi.org/10.1080/00295450.2020.1826804
Wang, B., Peng, R., Li, Y., Lai, H., & Wang, Z. (2018). Requirements traceability technologies and technology transfer decision support: A systematic review. Journal of Systems and Software, 146, 59–79. https://doi.org/10.1016/j.jss.2018.09.001
Wang, F., Yang, Z., Huang, Z., Liu, C., Zhou, Y., Bodeveix, J., & Filali, M. (2019). An approach to generate the traceability between restricted natural language requirements and AADL models. IEEE Transactions on Reliability, 69(1), 154–173. https://doi.org/10.1109/tr.2019.2936072
Wang, Y., Mäntylä, M. V., Liu, Z., & Markkula, J. (2022). Test automation maturity improves product quality—Quantitative study of open source projects using continuous integration. Journal of Systems and Software, 188, 111259. https://doi.org/10.1016/j.jss.2022.111259
White, R., & Krinke, J. (2022). TCTracer: Establishing test-to-code traceability links using dynamic and static techniques. Empirical Software Engineering, 27(3). https://doi.org/10.1007/s10664-021-10079-1
Zampetti, F., Di Sorbo, A., Visaggio, C. A., Canfora, G., & Di Penta, M. (2020). Demystifying the adoption of behavior-driven development in open source projects. Information and Software Technology, 123, 106311. https://doi.org/10.1016/j.infsof.2020.106311
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