Feature Location Using Crowd-Based Screencasts

Abstract

Crowd-based multi-media documents such as screencasts have emerged as a source for documenting requirements of agile software projects. For example, screencasts can describe buggy scenarios of a software product, or present new features in an upcoming release. Unfortunately, the binary format of videos makes traceability between the video content and other related software artifacts (e.g., source code, bug reports) difficult. In this paper, we propose an LDA-based feature location approach that takes as input a set of screencasts (i.e., the GUI text and/or spoken words) to establish traceability link between the features described in the screencasts and source code fragments implementing them. We report on a case study conducted on 10 WordPress screencasts, to evaluate the applicability of our approach in linking these screencasts to their relevant source code artifacts. We find that the approach is able to successfully pinpoint relevant source code files at the top 10 hits using speech and GUI text. We also found that term frequency rebalancing can reduce noise and yield more precise results.