Trace links explained: An automated approach for generating rationales

Guo, Jin; Monaikul, Natawut; Cleland‐Huang, Jane

doi:10.1109/re.2015.7320423

Cited by 15 publications

(8 citation statements)

References 16 publications

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“…The big disadvantage is the effort to create an ontology. Another benefit of the ontology is that it forms the basis for textual explanations of trace links [45].…”

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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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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“…The big disadvantage is the effort to create an ontology. Another benefit of the ontology is that it forms the basis for textual explanations of trace links [45].…”

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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“…Artificial Intelligence [35], [26], [33], [18], [8], [27], [25], [28], [11], [17], [30], [3] 12 35.29% Documentation Model [42], [38], [13], [21], [37], [45] 6 17.65%…”

Section: Papers Count Percentagementioning

confidence: 99%

“…Souali et al [54], define traceability as the ability to track the history, application or location of what is considered in question. Similarly, S. Gayer et al [16] and J. Guo et al [18] define traceability as the ability to relate the different artefacts generated in the software engineering process to each other and in a unique way, to maintain these links and be able to use them over time, for the purpose of answering relevant questions, both about the product and the process itself. This task of maintaining traceability of ADDs and the different resulting artefacts is a very important task, because it mitigates or in some cases prevents negative impact on system maintainability caused by AK evaporation.…”

Section: Introductionmentioning

confidence: 99%

Traceability of Architectural Design Decisions and Software Artifacts: A Systematic Mapping Study

Hyun,

Hurtado

2023

Foundations of Computing and Decision Sciences

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The definition of architecture is a crucial task in software development, where the architect is responsible for making the right decisions to meet specific functional and quality requirements. These architectural design decisions form the foundation that shapes the arrangement of elements within a system. Unfortunately, these decisions are often poorly documented, implicit in various artifacts, or inadequately updated, leading to negative consequences on the maintainability of a system and resulting in rework and cost overruns. The objective of this systematic mapping study is to comprehend the current state regarding approaches for traceability of architectural design decisions and how these decisions are linked with the different artifacts used in software development. To achieve this, an information extraction protocol is followed, utilizing databases with search strings, inclusion, and exclusion criteria. The findings demonstrate that this knowledge is highly relevant; however, it is rarely explicitly documented. As a result, most works propose diverse approaches to extract this knowledge from existing technical documentation, commonly used tools, and other sources of product and process information. In contrast, it is evident that there is no standard for documenting design decisions, leading each author to present a subjective version of what is important and where to trace these decisions. This suggests that there is still a significant amount of research to be conducted regarding the traceability of these architectural design decisions and their connection with software artifacts. Such research could lead to intriguing new proposals for investigation.

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“…The big disadvantage is the effort to create an ontology. Another benefit of the ontology is that it forms the basis for textual explanations of trace links [12].…”

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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Trace links explained: An automated approach for generating rationales

Cited by 15 publications

References 16 publications

Trace Link Recovery using Semantic Relation Graphs and Spreading Activation

Trace Link Recovery using Semantic Relation Graphs and Spreading Activation

Traceability of Architectural Design Decisions and Software Artifacts: A Systematic Mapping Study

Knowledge Extraction from Natural Language Requirements into a Semantic Relation Graph

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