Abstract
Context and motivation. Traceability is an essential part of quality assurance tasks for software maintainability, validation, and verification. However, the effort required to create and maintain traces is still high compared to their benefits. Problem. Some authors have proposed traceability tools to address this challenge, yet some of those tools require historical traceability data to generate traces, representing an entry barrier to software development teams that do not do traceability. Another common requirement of existing traceability tools is the scope of artefacts to be traced, hindering the adaptability of traceability tools in practice. Principal ideas. Motivated by the mentioned challenges, in this paper we propose OntoTraceV2.0: a tool for supporting trace generation of arbitrary software artefacts without depending on historical traceability data. The architecture of OntoTraceV2.0 integrates ontology-based automatic reasoning to facilitate adaptability for tracing arbitrary artefacts and natural language processing for discovering traces based on text-based similarity between artefacts. We conducted a quasi-experiment with 36 subjects to validate OntoTraceV2.0 in terms of efficiency, effectiveness, and satisfaction. Contribution. We found that OntoTraceV2.0 positively affects the subjects’ efficiency and satisfaction during trace generation compared to a manual approach. Although the subjects’ average effectiveness is higher using OntoTraceV2.0, we observe no statistical difference with the manual trace generation approach. Even though such results are promising, further replications are needed to avoid certain threats to validity. We conclude the paper by analysing the experimental results and limitations we found, drawing on future challenges, and proposing the next research endeavours.
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Notes
- 1.
Notice that this question can also be written as: having selected a EDG model element \(t_{a} \in T_{a}\), which is the set of possible user story parts \(PS_{a} \subseteq S_{a}\) to trace? However, we use the source-to-target variant instead of target-to-source variant for simplicity.
- 2.
- 3.
To facilitate further replications, all material related to the experimental objects, demographics, and results can be found at https://doi.org/10.5281/zenodo.7360221.
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Acknowledgments
This research is fully funded by the ZHAW Institute for Applied Information Technology (InIT), the Innosuisse Flagship SHIFT project, and the ZHAW School of Engineering. Moreover, we would like to thank all RASOP course students for actively participating on the quasi-experiment, allowing us to gather all the data we used to build our research.
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Mosquera, D., Ruiz, M., Pastor, O., Spielberger, J. (2023). Ontology-Based Automatic Reasoning and NLP for Tracing Software Requirements into Models with the OntoTrace Tool. In: Ferrari, A., Penzenstadler, B. (eds) Requirements Engineering: Foundation for Software Quality. REFSQ 2023. Lecture Notes in Computer Science, vol 13975. Springer, Cham. https://doi.org/10.1007/978-3-031-29786-1_10
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