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Main Title: Extraction of System States from Natural Language Requirements
Author(s): Pudlitz, Florian
Brokhausen, Florian
Vogelsang, Andreas
Type: Conference Object
Abstract: In recent years, simulations have proven to be an important means to verify the behavior of complex software systems. The different states of a system are monitored in the simulations and are compared against the requirements specification. So far, system states in natural language requirements cannot be automatically linked to signals from the simulation. However, the manual mapping between requirements and simulation is a time-consuming task. Named-entity Recognition is a sub-task from the field of automated information retrieval and is used to classify parts of natural language texts into categories. In this paper, we use a self-trained Named-entity Recognition model with Bidirectional LSTMs and CNNs to extract states from requirements specifications. We present an almost entirely automated approach and an iterative semi-automated approach to train our model. The automated and iterative approach are compared and discussed with respect to the usual manual extraction. We show that the manual extraction of states in 2,000 requirements takes nine hours. Our automated approach achieves an F1-score of 0.51 with 15 minutes of manual work and the iterative approach achieves an F1-score of 0.62 with 100 minutes of work.
Subject(s): natural language requirements
named-entity recognition
system states
state extraction
Issue Date: 5-Dec-2019
Date Available: 31-Jul-2019
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.2019.00031
EISSN: 2332-6441
ISBN: 978-1-7281-3912-8
ISSN: 1090-705X
TU Affiliation(s): Fak. 4 Elektrotechnik und Informatik » Inst. Telekommunikationssysteme » FG IT-basierte Fahrzeuginnovationen
Appears in Collections:Technische Universit├Ąt Berlin » Publications

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2 10.14279/depositonce-8717.2 2019-12-18 16:29:05.007 add publisher metadata
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