Ambiguous software requirement specification detection

Abstract

Software requirement specification (SRS) document is the most crucial document in software development process. All subsequent steps in software development are influenced by this document. However, issues in requirement, such as ambiguity or incomplete specification may lead to misinterpretation of requirements which consequently, influence the testing activities and higher the risk of time and cost overrun of the project. Finding defects in the initial development phase is crucial since the defect that found late is more expensive than if it was found early. This study describes an automated approach for detecting ambiguous software requirement specification. To this end, we propose the combination of text mining and machine learning. Since the dataset is derived from Malaysian industrial SRS documents, this study only focuses on the Malaysian context. We used text mining for feature extraction and for preparing the training set. Based on this training set, the method ‘learns’ to detect the ambiguous requirement specification. In this paper, we study a set of nine (9) classification algorithms from the machine learning community and evaluate which algorithm performs best to detect the ambiguous software requirement specification. Based on the experiment’s result, we develop a working prototype which later is used for our initial validation of our approach. The initial validation shows that the result produced by the classification model is reasonably acceptable. Even though this study is an experimental benchmark, we optimist that this approach may contributes to enhance the quality of SRS.