Requirements Traceability Matrix: Automatic Generation and Visualization

Background: Requirements management is considered one of the activities responsible for system failures. The difficulty regarding to requirements trace ability makes the system changes hard to be managed. Objective: This paper presents two approaches that allow the automated generation of the Requirements Trace ability Matrix (RTM): the RTM-E approach, which is based on the requirement input data, and the RTM-NLP approach, which is based on Natural Language Processing-NLP. Method: The RTM-E comprises the requirements dependency related to the data manipulated by them, while the RTM-NLP comprises the requirements dependency related to the similarities of their functionality descriptions. The results are shown through visualization of information in order to facilitate the understanding of such dependencies. Results: We conducted an experimental study in which both approaches were applied to 18 requirements documents. The RTMs created automatically were compared with the reference RTM created manually based on the stakeholders knowledge. Comparing the generated matrices, it was seen that the RTM-E on average matches 82% to the reference RTM, while the RTM-NLP approach on average matches 53%. Conclusions: The results show that generating the RTM based on these approaches, the effectiveness on determining the requirements dependences is satisfactory and motivates to keep studying in order to make improvements for both approaches.