Analyzing the understandability of Requirements Engineering languages for CSCW systems: A family of experiments

Context: A collaborative system is a special kind of software whose users can perform collaboration, communication and collaboration tasks. These systems usually have a high number of non-functional requirements, resulting from the users¿ need of being aware of other users with whom to collaborate, that is, the workspace awareness. Objective: This paper aims at evaluating two Requirements Engineering languages i and CSRML (an extension of i ) in order to determine which is the most suitable one to specify requirements of collaborative systems, taking into account their special characteristics regarding collaboration and awareness. Method: We performed a family of experiments comprising an original experiment and two replicas. They were performed by 30, 45 and 9 Computer Science students, respectively, from Spain and Argentina. These subjects filled in two understandability questionnaires once they analyzed the requirements models of two systems: an e-learning collaborative system and a conference review system with some collaborative aspects support. Both models were specified by using the evaluated languages. Results: The statistical analysis of the family of experiments showed that the understandability was higher for the models specified with CSRML than for those specified with i , especially for collaborative aspects. This result was also confirmed by the meta-analysis conducted. Conclusions: CSRML surpasses i when modeling collaborative systems requirements models due to the specific expressiveness introduced to represent collaboration between users and awareness and the new resorts to manage actors and roles.
This work has been partially supported by the grant (PEII09-0054-9581) from the Junta de Comunidades de Castilla-La Mancha and by the grant (DESACO, TIN2008-06596-C02-01) from the Spanish Government. We would like to thank Leandro Antonelli (Software Engineering teacher at UNLP), Francisco Parreno (Statistics teacher at UCLM) and Oscar Dieste (Empirical Software Engineering expert at UPM) for their advices during the course of this study. We would also like to thank Symbia IT Corp. software engineers for their feedback to help us to improve the questionnaires.