Doctoral Seminar: Parisa Moslehi

Speaker: Parisa Moslehi

Supervisors: Drs. B. Adams, J. Rilling

Supervisory Committee: Drs. J. Bentahar, J. Paquet, E. Shihab

Title: Feature Location Using Crowd-based Screencasts

Date: Thursday, March 21, 2019

Time: 10:15 a.m.

Place: EV 3.309

ABSTRACT

Crowd-based multimedia documents such as screencasts have emerged as a source for documenting requirements of open source and agile software projects. For example, screencasts can describe usage scenarios of a software product or present new features in an upcoming release. Unfortunately, the binary format of videos makes analyzing the video content difficult. As a result, dissecting and filtering multimedia information based on its relevance to a given project is an inherently difficult task. Therefore, it is necessary to provide automated approaches for mining and linking this crowd-based multimedia documentation to their relevant software artifacts. In this research, we apply LDA-based mining approaches that take as input a set of screencast artifacts, such as GUI text and spoken words to perform information extraction and, therefore, increase the availability of both textual and multimedia documentation for various stakeholders of a software product. As part of the research, we present an LDA-based approach that links screencasts to their relevant source code artifacts. To evaluate the applicability of the proposed approach, we report on case studies conducted on existing WordPress and Mozilla Firefox screencasts that describe different usage scenarios in each software application.