An information theoretic approach for extracting and tracing non-functional requirements

Non-functional requirements (NFRs) are high-level quality constraints that a software system should exhibit. Detecting such constraints early in the process is critical for the stability of software architectural design. However, due to their pervasive nature, and the lack of robust modeling and documentation techniques, NFRs are often overlooked during the requirements elicitation phase. Realizing such constraints at later stages of the development process often leads to architecture erosion and poor traceability. Motivated by these observations, we propose an unsupervised, computationally efficient, and scalable approach for extracting and tracing NFRs in software systems. Based on main assumptions of the cluster hypothesis and information theory, the proposed approach exploits the semantic knowledge embedded in the textual content of requirements specifications to discover, classify, and trace high-level software quality constraints imposed by the system's functional features. Three experimental systems are used to conduct the experimental analysis in this paper. Results show that the proposed approach can discover software NFRs with an average accuracy of 73%, enabling these NFRs to be traced to their implementations with accuracy levels adequate for practical applications.