Automated feature discovery via sentence selection and source code summarization. Issue 2 (9th February 2016)

• Record Type: Journal Article
• Authors: McBurney, Paul W.; Liu, Cheng; McMillan, Collin
• Is Part Of: Journal of software. Volume 28:Issue 2(2016)
• Page Start: 120
• Page End: 145
• Publication Date: 2016-02-09
• Subjects: feature discovery -- sentence selection -- source code summarization; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.1768
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: Programs are, in essence, a collection of implemented features. Feature discovery in software engineering is the task of identifying key functionalities that a program implements. Manual feature discovery can be time consuming and expensive, leading to automatic feature discovery tools being developed. However, these approaches typically only describe features using lists of keywords, which can be difficult for readers who are not already familiar with the source code. An alternative to keyword lists is sentence selection, in which one sentence is chosen from among the sentences in a text document to describe that document. Sentence selection has been widely studied in the context of natural language summarization but is only beginning to be explored as a solution to feature discovery. In this paper, we compare four sentence selection strategies for the purpose of feature discovery. Two are off‐the‐shelf approaches, while two are adaptations we propose. We present our findings as guidelines and recommendations to designers of feature discovery tools. Copyright © 2016 John Wiley & Sons, Ltd. Abstract : Feature discovery in software engineering is the task of identifying key functionalities that a program implements. Manual feature discovery can be time‐consuming and expensive, leading to automatic feature discovery tools being developed. In this paper, we examine four fully automatic sentence selection approaches for feature discovery and perform two case studies. The case studies found that keywords from topic models can be used to select sentences to provide a reader with understanding of the overall purpose of the project.