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Main Title: Improving Trace Link Recovery using Semantic Relation Graphs and Spreading Activation
Author(s): Schlutter, Aaron
Vogelsang, Andreas
Type: Conference Object
Abstract: [Context & Motivation] Trace Link Recovery tries to identify and link related existing requirements with each other to support further engineering tasks. Existing approaches are mainly based on algebraic Information Retrieval or machine-learning. [Question/Problem] Machine-learning approaches usually demand reasonably large and labeled datasets to train. Algebraic Information Retrieval approaches like distance between tf-idf scores also work on smaller datasets without training but are limited in considering the context of semantic statements. [Principal Ideas/Results] In this work, we revise our existing Trace Link Recovery approach that is based on an explicit representation of the content of requirements as a semantic relation graph and uses Spreading Activation to answer trace queries over this graph. The approach generates sorted candidate lists and is fully automated including an NLP pipeline to transform unrestricted natural language requirements into a graph and does not require any external knowledge bases or other resources. [Contribution] To improve the performance, we take a detailed look at five common datasets and adapt the graph structure and semantic search algorithm. Depending on the selected configuration, the predictive power strongly varies. With the best tested configuration, the approach achieves a mean average precision of 50%, a Lag of 30% and a recall of 90%.
Subject(s): traceability
trace link recovery
natural language processing
spreading activation
semantic relation graph
information retrieval
Issue Date: 2-Apr-2021
Date Available: 12-Apr-2021
References: 10.14279/depositonce-7776
Language Code: en
DDC Class: 006 Spezielle Computerverfahren
Proceedings Title: Requirements Engineering: Foundation for Software Quality : 27th International Working Conference, REFSQ 2021, Essen, Germany, April 12–15, 2021, Proceedings
Editor: Dalpiaz, Fabiano
Spoletini, Paola
Publisher: Springer
Publisher DOI: 10.1007/978-3-030-73128-1_3
Page Start: 37
Page End: 53
Series: Lecture Notes in Computer Science
Series Number: 12685
EISSN: 1611-3349
ISBN: 978-3-030-73127-4
ISSN: 0302-9743
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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2 10.14279/depositonce-11390.2 2021-04-12 10:57:45.759 Paper published, include DOI
1 10.14279/depositonce-11390 2021-02-12 15:23:42.0
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