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As new requirements are introduced and implemented in a software system,developers must identify the set of source code classes which need to bechanged. Therefore, past effort has focused on predicting the set of classesimpacted by a requirement. In this paper, we introduce and evaluate a new typeof information based on the intuition that the set of requirements which areassociated with historical changes to a specific class are likely to exhibitsemantic similarity to new requirements which impact that class. This newRequirements to Requirements Set (R2RS) family of metrics captures the semanticsimilarity between a new requirement and the set of existing requirementspreviously associated with a class. The aim of this paper is to present andevaluate the usefulness of R2RS metrics in predicting the set of classesimpacted by a requirement. We consider 18 different R2RS metrics by combiningsix natural language processing techniques to measure the semantic similarityamong texts (e.g., VSM) and three distribution scores to compute overallsimilarity (e.g., average among similarity scores). We evaluate if R2RS isuseful for predicting impacted classes in combination and against four otherfamilies of metrics that are based upon temporal locality of changes, directsimilarity to code, complexity metrics, and code smells. Our evaluationfeatures five classifiers and 78 releases belonging to four large open-sourceprojects, which result in over 700,000 candidate impacted classes. Experimentalresults show that leveraging R2RS information increases the accuracy ofpredicting impacted classes practically by an average of more than 60% acrossthe various classifiers and projects.

Leveraging Historical Associations between Requirements and Source Code to Identify Impacted Classes