Please use this identifier to cite or link to this item:
Main Title: Predicting How to Test Requirements: An Automated Approach
Author(s): Winkler, Jonas Paul
Grönberg, Jannis
Vogelsang, Andreas
Type: Conference Object
Language Code: en
Abstract: [Context] An important task in requirements engineering is to identify and determine how to verify a requirement (e.g., by manual review, testing, or simulation; also called potential verification method). This information is required to effectively create test cases and verification plans for requirements. [Objective] In this paper, we propose an automatic approach to classify natural language requirements with respect to their potential verification methods (PVM). [Method] Our approach uses a convolutional neural network architecture to implement a multiclass and multilabel classifier that assigns probabilities to a predefined set of six possible verification methods, which we derived from an industrial guideline. Additionally, we implemented a backtracing approach to analyze and visualize the reasons for the network’s decisions. [Results] In a 10-fold cross validation on a set of about 27,000 industrial requirements, our approach achieved a macro averaged F1 score of 0.79 across all labels. For the classification into test or non-test, the approach achieves an even higher F1 score of 0.94. [Conclusions] The results show that our approach might help to increase the quality of requirements specifications with respect to the PVM attribute and guide engineers in effectively deriving test cases and verification plans.
Issue Date: 2019
Date Available: 31-Jul-2019
DDC Class: 004 Datenverarbeitung; Informatik
006 Spezielle Computerverfahren
Subject(s): requirements engineering
requirements validation
test engineering
machine learning
natural language processing
neural networks
Proceedings Title: 27th IEEE International Requirements Engineering Conference (RE'19)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publisher Place: Jeju Island
Appears in Collections:FG IT-basierte Fahrzeuginnovationen » Publications

Files in This Item:
File Description SizeFormat 
winkler_etal_2019.pdf1.04 MBAdobe PDFThumbnail

Items in DepositOnce are protected by copyright, with all rights reserved, unless otherwise indicated.