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Main Title: Predicting How to Test Requirements: An Automated Approach
Author(s): Winkler, Jonas Paul
Grönberg, Jannis
Vogelsang, Andreas
Type: Conference Object
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.
Subject(s): requirements engineering
requirements validation
test engineering
machine learning
natural language processing
neural networks
Issue Date: 5-Dec-2019
Date Available: 12-Apr-2021
Language Code: en
DDC Class: 004 Datenverarbeitung; Informatik
006 Spezielle Computerverfahren
Proceedings Title: 2019 IEEE 27th International Requirements Engineering Conference (RE)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI: 10.1109/RE.2019.00023
Page Start: 120
Page End: 130
EISSN: 2332-6441
ISBN: 978-1-7281-3912-8
TU Affiliation(s): Fak. 4 Elektrotechnik und Informatik » Inst. Telekommunikationssysteme » FG IT-basierte Fahrzeuginnovationen
Appears in Collections:Technische Universität Berlin » Publications

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