Detecting similarities and conflicts in software requirements

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Requirements are written in natural language and hence are subject to ambiguity, vagueness, and subjectivity. They are not always clear and coherent. They may also contain many duplicates and/or conflicting information. Automated solutions use information retrieval and machine learning techniques to capture such anomalies; however, current techniques ignore the semantics of the software artifacts or ignore integrating domain knowledge into this process and therefore tend to deliver imprecise and inaccurate results. In this research project, we would like to propose a solution that uses deep learning to incorporate requirements artifact semantics and domain knowledge into consideration. Our aim is to capture conflicting and duplicate requirements as accurately and timely as possible. We propose a network architecture that utilizes Word Embedding and Convolutional Neural Network (CNN) models to generate links. Word embedding learns word vectors that represent knowledge of the domain corpus and CNN uses these word vectors to learn the sentence semantics of requirements artifacts. We will build baseline methods using the Vector Space Model and Latent Semantic Indexing to compare and comply our proposed model with the state-of-the-art models as well as Watson Discovery. We will use datasets from DOORS Next Generation requirements. Our proposed model could further be extended to build the links between design requirements and source code to enable having a full traceability matrix.