Vara González, José Luis de laMarín, BeatrizGiachetti, GiovanniAyora, Clara2017-03-242017-03-242017-03-242016-09-08Vara González, Jose Luis de la; Marín, Beatriz; Giachetti, Giovanni; Ayora, Clara (2016). Do Models Improve the Understanding of Safety Compliance Needs?: Insights from a Pilot Experiment. ESEM 2016. Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement. ACM. Pp. 32:1-32:6978-1-4503-4427-2https://hdl.handle.net/10016/24438Context. Many critical systems must meet safety compliance needs from safety standards. These standards are usually large textual documents whose compliance needs can be hard to understand. As a solution, the use of models has been proposed. Goal. We aim to provide evidence of the extent to which models improve the understanding of safety compliance needs. Method. We designed an experiment and ran a pilot to study the effectiveness, efficiency, and perceived benefits of understanding these needs, with the text of standards and with models in the form of UML object diagrams. Results. The overall results from 15 Bachelor students show that the effectiveness of understanding safety compliance needs increases very little with models (2%), and the efficiency even decreases (24%). Nonetheless, the results improve when the potential complexity in navigating the models is taken into account (15% effectiveness increase). The students find benefits in using the models but most consider that the models are hard to understand. Conclusions. The extent to which models improve the understanding of safety compliance needs seems to be lower than what the research community expects. New studies are necessary to confirm our initial insights.text/xmlapplication/pdfeng© ACM, 2016This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.Safety-critical systemSafety standardSafety compliance needsModelUnderstandingPilot experimentconference paperInformáticahttps://doi.org/10.1145/2961111.2962621open access32:132:6ESEM 2016. Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and MeasurementCC/0000025677