Identifying Requirements Association Based on Class Diagram Using Semantic Similarity

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Hernawati Susanti Samosir Daniel Siahaan

Abstract

RRequirements association depicts inter-relation between two or more requirements within a software project. It provides necessary information for developers during decision-making processes, such as change management, development milestones, bug prediction, cost estimation, and work breakdown structure generation. Modeling association between requirements became a focus of software requirements researchers. Previous studies indicate that requirements association was pre-defined by requirements engineer based on their expert judgments. The judgments require knowledge on requirements and their class realizations. This paper introduces a method to generate a mapping between a set of requirement statements and a set of classes of a given project that realized the respected requirements. The method also generates associations among requirements based on information on associations between classes and the class-requirement mapping. The method utilizes element of relational information resided in a class diagram of respected project. A semantic similarity method was used to define the requirements with their realization classes. A class is considered realizing a requirement if and only if their semantic similarity is higher than a certain threshold. A set of experimentation on four different projects was conducted. The result of the approach was compared with the output produced by human annotators using kappa statistics. The approach is considered as having a fair agreement level (i.e. with kappa value 0.37) with the human annotators to identify and model requirement associations.

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SAMOSIR, Hernawati Susanti; SIAHAAN, Daniel. Identifying Requirements Association Based on Class Diagram Using Semantic Similarity. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], p. 19-28, may 2019. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/43931>. Date accessed: 30 nov. 2020. doi: https://doi.org/10.24843/LKJITI.2019.v10.i01.p03.
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