Industrial Requirements Classification for Redundancy and Inconsistency Detection in SEMIOS

Requirements are usually "hand-written" and suffers from several problems like redundancy and inconsistency. The problems of redundancy and inconsistency between requirements or sets of requirements impact negatively the success of final products. Manually processing these issues requires too much time and it is very costly. The main contribution of this paper is the use of k-means algorithm for a redundancy and inconsistency detection in a new context, which is Requirements Engineering context. Also, we introduce a filtering approach to eliminate "noisy" requirements and a preprocessing step based on the Natural Language Processing (NLP) technique to see the impact of this latter on the k-means results. We use Part-Of-Speech (POS) tagging and noun chunking to detect technical business terms associated to the requirements documents that we analyze. We experiment this approach on real industrial datasets. The results show the efficiency of the k-means clustering algorithm, especially with the filtering and preprocessing steps. Our approach is using the software SEMIOS and will be integrated as a new functionality.