Abstract
Often, when requirements are written, parts of the domain knowledge are assumed by the domain experts and not formalized in writing, but nevertheless used to build software artifacts. This issue, known as tacit knowledge, affects the performance of Traceability Links Recovery. Through this work we propose LORE, a novel approach that uses Natural Language Processing techniques along with an Ontological Requirements Expansion process to minimize the impact of tacit knowledge on TLR over process models. We evaluated our approach through a real-world industrial case study, comparing its outcomes against those of a baseline. Results show that our approach retrieves improved results for all the measured performance indicators. We studied why this is the case, and identified some issues that affect LORE, leaving room for improvement opportunities. We make an open-source implementation of LORE publicly available in order to facilitate its adoption in future studies.
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Acknowledgements
This work has been partially supported by the Ministry of Economy and Competitiveness and ERDF funds under the project Model-Driven Variability Extraction for Software Product Lines Adoption (TIN2015-64397-R). We also thank the ITEA3 15010 REVaMP2 Project.
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Lapeña, R., Pérez, F., Cetina, C., Pastor, Ó. (2019). Improving Traceability Links Recovery in Process Models Through an Ontological Expansion of Requirements. In: Giorgini, P., Weber, B. (eds) Advanced Information Systems Engineering. CAiSE 2019. Lecture Notes in Computer Science(), vol 11483. Springer, Cham. https://doi.org/10.1007/978-3-030-21290-2_17
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