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

TL;DR

This paper introduces R2RS metrics that use semantic similarity between new requirements and historical requirements associated with classes, significantly improving the accuracy of impacted class prediction in software maintenance.

Contribution

It proposes and evaluates R2RS metrics based on semantic similarity, demonstrating their effectiveness in predicting impacted classes over traditional metrics.

Findings

R2RS metrics improve prediction accuracy by over 60%.

Semantic similarity measures outperform traditional change-based metrics.

Evaluation across multiple projects confirms robustness of R2RS approach.

Abstract

As new requirements are introduced and implemented in a software system, developers must identify the set of source code classes which need to be changed. Therefore, past effort has focused on predicting the set of classes impacted by a requirement. In this paper, we introduce and evaluate a new type of information based on the intuition that the set of requirements which are associated with historical changes to a specific class are likely to exhibit semantic similarity to new requirements which impact that class. This new Requirements to Requirements Set (R2RS) family of metrics captures the semantic similarity between a new requirement and the set of existing requirements previously associated with a class. The aim of this paper is to present and evaluate the usefulness of R2RS metrics in predicting the set of classes impacted by a requirement. We consider 18 different R2RS metrics by combining six natural language processing techniques to measure the semantic similarity among texts (e.g., VSM) and three distribution scores to compute overall similarity (e.g., average among similarity scores). We evaluate if R2RS is useful for predicting impacted classes in combination and against four other families of metrics that are based upon temporal locality of changes, direct similarity to code, complexity metrics, and code smells. Our evaluation features five classifiers and 78 releases belonging to four large open-source projects, which result in over 700,000 candidate impacted classes. Experimental results show that leveraging R2RS information increases the accuracy of predicting impacted classes practically by an average of more than 60% across the various classifiers and projects.