Predicting requirement change propagation through investigation of physical and functional domains

Requirements play a critical role in the design process and are important to the project’s success. The design process is iterative, and requirements are constantly changed and updated to reflect stakeholders’ expectations, design changes, regulations, and resource limitations. Since requirements drive product development from initial development of concepts to final production of the finished product, mismanaged requirement changes can lead to monetary and time losses. The ability to assess a requirement change, predict its propagation, and evaluate the impact early in the design process will enable engineers to make informed decisions regarding change implementation. Prior research performed by Morkos culminated in the Automated Requirement Change Propagation Prediction (ARCPP) tool to mitigate issues due to requirement change propagation. The ARCPP tool utilized syntactic natural language data, part of speech (POS) elements in requirement statements, as relators to form relationships between requirements. The resulting requirement network serves to predict change propagation as a result of an initiating requirement change using the performance metric of the tool, requirement review depth. Whereas the prior research proved that change propagation can be predicted using requirements, the purpose of this research is to understand why requirements can be used. Specifically, what parts of a requirement affect its ability to predict change propagation? This is performed by addressing three key research questions (RQs): (1) Is the requirement review depth affected by the number of relators selected to relate requirements, (2) Is the requirement review depth affected by the frequency of relators selected to relate requirements, and (3) Which element of a requirement, the physical or functional domain, is responsible for instigating change propagation? The results indicate that the review depth, an indicator of the performance of the ARCPP tool, is not affected by the number and frequency of relators, but rather by the ability of relators in capturing the propagating relationships. Further, the physical domain is found to contribute more towards predicting change propagation than the functional domain. Finally, a recommendation on selecting the number of requirement relators is presented.