Classifying requirements: towards a more rigorous analysis of natural-language specifications

Requirements play a pivotal role in planning, selection, development, testing and operation of NASA's missions. Starting from mission objectives, requirements are successively decomposed into finer detail and ultimately allocated to individual components. This decomposition process is sometimes referred to as "requirements flowdown" between successive levels. The correctness of this decomposition is obviously critical to mission success: if a higher level requirement is improperly decomposed into lower level requirements, the subsequent development from that stage onwards will be jeopardized. A task to determine how to improve the rigor of requirements flowdown analyses has recently been completed. Since requirements continue to be written in natural language, the task focused on examining only natural language specifications for a JPL space mission system. One particular aspect of flowdown analysis examined was classifying requirements by type -subsetting a collection of requirements in this fashion may significantly reduce the effort required to analyze a flowdown by restricting the number of requirements analyzed at any one time, and may increase the accuracy of the flowdown analysis. The application of natural language processing techniques and machine learning techniques has resulted in classifiers that perform well in distinguishing between temporal and non-temporal requirements written in natural language. Such a capability could reduce the effort required to analyze requirements of a given type. Current plans call for extending this capability to improving automated tracing of related requirements and checking a set of related requirements for consistency