Tackling the term-mismatch problem in automated trace retrieval

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. Machinelearning 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 providing explanations for trace links. In this work, we present a 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. Our approach is fully automated including an NLP pipeline to transform unrestricted natural language requirements into a graph. We evaluate our approach on five common datasets. Depending on the selected configuration, the predictive power strongly varies. With the best tested configuration, the approach achieves a mean average precision of 40% and a Lag of 50%. Even though the predictive power of our approach does not outperform state-of-the-art approaches, we think that an explicit knowledge representation is an interesting artifact to explore in Trace Link Recovery approaches to generate explanations and refine results.

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“…Guo et al [44] present a technique to build an ontology semiautomatically. They focus on term mismatches between related documents.…”

Section: Discussionmentioning

confidence: 99%

Trace Link Recovery using Semantic Relation Graphs and Spreading Activation

Schlutter

Vogelsang

2020

2020 IEEE 28th International Requirements Engineering Conference (RE)

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“…The work of Siena et al [35,36] and Zeni et al [37] spuds in one step earlier to elicit this kind of requirements from sources, such as laws and norms, and to ensure compliance through their intentional alignment with these sources. Guo et al [38] aim with their approach at automated tracing between requirements documents and regulatory codes. Regulatory codes for them can be higher-level requirements, laws-or standards, as we consider them in our approach.…”

Section: Related Workmentioning

confidence: 99%

“…We used the data from the EagleEye specifications specifications, as presented in Sect. 3, to build a trace graph with the native graph database Neo4J 38 . Neo4J comes with a graph database server that can be used via APIs in various programming languages.…”

Section: Derivation 5 (Non-represented Requirements) 7 Case Studymentioning

confidence: 99%

Requirements document relations

2022

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Relations between requirements are part of nearly every requirements engineering approach. Yet, relations of views, such as requirements documents, are scarcely considered. This is remarkable as requirements documents and their structure are a key factor in requirements reuse, which is still challenging. Explicit formalized relations between documents can help to ensure consistency, improve completeness, and facilitate review activities in general. For example, this is relevant in space engineering, where many challenges related to complex document dependencies occur: 1. Several contractors contribute to a project. 2. Requirements from standards have to be applied in several projects. 3. Requirements from previous phases have to be reused. We exploit the concept of “layered traceability”, explicitly considering documents as views on sets of individual requirements and specific traceability relations on and between all of these representation layers. Different types of relations and their dependencies are investigated with a special focus on requirement reuse through standards and formalized in an Object-Role Modelling (ORM) conceptual model. Automated analyses of requirement graphs based on this model are able to reveal document inconsistencies. We show examples of such queries in Neo4J/Cypher for the EagleEye case study. This work aims to be a step toward a better support to handle highly complex requirement document dependencies in large projects with a special focus on requirements reuse and to enable automated quality checks on dependent documents to facilitate requirements reviews.

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“…Guo et al [11] present a technique to build an ontology semiautomatically. They focus on term mismatches between related documents.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Extraction from Natural Language Requirements into a Semantic Relation Graph

Schlutter

Vogelsang

2020

Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops

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Knowledge extraction and representation aims to identify information and to transform it into a machine-readable format. Knowledge representations support Information Retrieval tasks such as searching for single statements, documents, or metadata. Requirements specifications of complex systems such as automotive software systems are usually divided into different subsystem specifications. Nevertheless, there are semantic relations between individual documents of the separated subsystems, which have to be considered in further processes (e.g. dependencies). If requirements engineers or other developers are not aware of these relations, this can lead to inconsistencies or malfunctions of the overall system. Therefore, there is a strong need for tool support in order to detects semantic relations in a set of large natural language requirements specifications. In this work we present a knowledge extraction approach based on an explicit knowledge representation of the content of natural language requirements as a semantic relation graph. Our approach is fully automated and includes an NLP pipeline to transform unrestricted natural language requirements into a graph. We split the natural language into different parts and relate them to each other based on their semantic relation. In addition to semantic relations, other relationships can also be included in the graph. We envision to use a semantic search algorithm like spreading activation to allow users to search different semantic relations in the graph.

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Tackling the term-mismatch problem in automated trace retrieval

Cited by 33 publications

References 54 publications

Trace Link Recovery using Semantic Relation Graphs and Spreading Activation

Trace Link Recovery using Semantic Relation Graphs and Spreading Activation

Requirements document relations

Knowledge Extraction from Natural Language Requirements into a Semantic Relation Graph

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