Using k-Means for Redundancy and Inconsistency Detection: Application to Industrial Requirements

Requirements are usually “hand-written” and suffers from several problems like redundancy and inconsistency. These problems 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. We propose in this paper to automatically handle redundancy and inconsistency issues in a classification approach. The main contribution of this paper is the use of k-means algorithm for redundancy and inconsistency detection in a new context, which is Requirements Engineering context. Also, we introduce a preprocessing step based on the Natural Language Processing techniques in order to see the impact of this latter to the k-means results. We use Part-Of-Speech (POS) tagging and noun chunking in order to detect technical business terms associated with 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 preprocessing.