Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-6975
Main Title: Using tools to assist identification of non-requirements in requirements specifications
Subtitle: A controlled experiment
Author(s): Winkler, Jonas Paul
Vogelsang, Andreas
Type: Conference Object
Language Code: en
Abstract: [Context and motivation] In many companies, textual fragments in specification documents are categorized into requirements and non-requirements. This categorization is important for determining liability, deriving test cases, and many more decisions. In practice, this categorization is usually performed manually, which makes it labor-intensive and error-prone. [Question/Problem] We have developed a tool to assist users in this task by providing warnings based on classification using neural networks. However, we currently do not know whether using the tool actually helps increasing the classification quality compared to not using the tool. [Principal idea/results] Therefore, we performed a controlled experiment with two groups of students. One group used the tool for a given task, whereas the other did not. By comparing the performance of both groups, we can assess in which scenarios the application of our tool is beneficial. [Contribution] The results show that the application of an automated classification approach may provide benefits, given that the accuracy is high enough.
URI: https://depositonce.tu-berlin.de//handle/11303/7797
http://dx.doi.org/10.14279/depositonce-6975
Issue Date: 2018
Date Available: 16-May-2018
DDC Class: 004 Datenverarbeitung; Informatik
Subject(s): requirements engineering
machine learning
convolutional neural networks
natural language processing
License: http://rightsstatements.org/vocab/InC/1.0/
Proceedings Title: Requirements Engineering: Foundation for Software Quality. REFSQ 2018
Publisher: Springer
Publisher Place: Cham
Volume: 2018
Publisher DOI: 10.1007/978-3-319-77243-1_4
Page Start: 57
Page End: 71
Series: Lecture Notes in Computer Science
Series Number: 10753
ISBN: 978-3-319-77243-1
Appears in Collections:FG IT-basierte Fahrzeuginnovationen » Publications

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