Semi-Automated Feature Traceability with Embedded Annotations

Abstract

Engineering software amounts to implementing and evolving features. While some engineering approaches advocate the explicit use of features, developers usually do not record feature locations in software artifacts. However, when evolving or maintaining features – especially in long-living or variant-rich software with many developers – the knowledge about features and their locations quickly fades and needs to be recovered. While automated or semi-automated feature-location techniques have been proposed, their accuracy is usually too low to be useful in practice. We propose a semi-automated, machine-learning-assisted feature-traceability technique that allows developers to continuously record feature-traceability information while being supported by recommendations about missed locations. We show the accuracy of our proposed technique in a preliminary evaluation, simulating the engineering of an open-source web application that evolved in different, cloned variants.