On the role of semantics in automated requirements tracing

Mahmoud, Anas; Niu, Nan

doi:10.1007/s00766-013-0199-y

Cited by 66 publications

(34 citation statements)

References 76 publications

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“…Requirements classification and retrieval methods provide automated support for several RE tasks, including requirements analysis, traceability, reuse, and modeling [6], [32]. Such methods consider requirements at the text level, where NLP and IR methods are used to match requirements based on their lexical attributes.…”

Section: B Requirements Textmentioning

confidence: 99%

“…Therefore, the amount of text available does not provide sufficient data or shared context for data-intensive or statistical NLP and IR methods to function properly. For instance methods such as Latent Semantic Indexing (LSI) [35] and Latent Dirichlet Allocation (LDA) [36], which are heavily used in requirements traceability and understating tasks [6], often fail to capture any meaningful contextual relations in the system, leading to a significant decline in the accuracy of tools relying on such methods to process requirements.…”

Section: B Requirements Textmentioning

confidence: 99%

“…The heavy utilization of NL in RE has motivated researchers to use conventional Natural Language Processing (NLP) and Information Retrieval (IR) methods to classify and analyze the textual content of software requirements [4]. The main objective is to provide automated solutions for several exhaustive, errorprone, and time-consuming RE tasks, from early phase RE [5] to later phases of traceability and reuse [6].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, given that the amount of semantic knowledge available in requirements is often limited [1], [6], there always seems to be an upper bound on what such methods and techniques can achieve [6]. This can be attributed to the fact that requirements are expressed with limited terminology extracted from the vocabulary of the application domain [8].…”

Section: Introductionmentioning

confidence: 99%

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Exploiting online human knowledge in Requirements Engineering

Mahmoud

Carver

2015

2015 IEEE 23rd International Requirements Engineering Conference (RE)

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Data-driven Natural Language Processing (NLP) methods have noticeably advanced in the past few years. These advances can be tied to the drastic growth of the quality of collaborative knowledge bases (KB) available on the World Wide Web. Such KBs contain vast amounts of up-to-date structured human knowledge and common sense data that can be exploited by NLP methods to discover otherwise-unseen semantic dimensions in text, aiding in tasks related to natural language understanding, classification, and retrieval. Motivated by these observations, we describe our research agenda for exploiting online human knowledge in Requirements Engineering (RE). The underlying assumption is that requirements are a product of the human domain knowledge that is expressed mainly in natural language. In particular, our research is focused on methods that exploit the online encyclopedia Wikipedia as a textual corpus. Wikipedia provides access to a massive number of real-world concepts organized in hierarchical semantic structures. Such knowledge can be analyzed to provide automated support for several exhaustive RE activities including requirements elicitation, understanding, modeling, traceability, and reuse, across multiple application domains. This paper describes our preliminary findings in this domain, current state of research, and prospects of our future work.

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Section: B Requirements Textmentioning

confidence: 99%

Section: B Requirements Textmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploiting online human knowledge in Requirements Engineering

Mahmoud

Carver

2015

2015 IEEE 23rd International Requirements Engineering Conference (RE)

Self Cite

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“…There has been extensive research done on automatics text classification task lately. Several machine learning approaches have also been involved here in order to provide enhancements [12]. There are numerous classifiers proposed that are used during the classification process.…”

Section: Introductionmentioning

confidence: 99%

Null function classification using NFR matrix and decision tree

Hoshmand

Kang

2018

IJET

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This research work is based on the classification of the functions which are required and which is not required for the efficient development of the software. The motivation of this work is to identify non required functions to reduce development cost and efforts. The classification is the technique which can classify data into certain number of classes. The NFR matrix is the existing technique which can classify the Null functions. To classify the null functions NFR use clustering method which can modify to increase accuracy of classification. In the existing method NFR matrix is used for the null function classification, it use the clustering for the classification. In this work, decision tree will based with the clustering. It take input result of clustering and generate classified result.

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An Architectural Approach to Requirements Engineering for Infrastructure Projects

Silmon

Evans

Brownsword

et al. 2015

Systems Engineering

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A brand-new high-speed railway project such as HS2, currently under development in the United Kingdom, presents a requirements engineering problem that is similar to domains where Systems Engineering (SE) is more traditionally applied, such as an aircraft, but with some differences in system structure that preclude a direct mapping of routine techniques. With over 300 separate elements of design in the Country South portion of the route, fitting into roughly 20 categories and inheriting requirements at multiple levels, including location-specific constraints, it was considered necessary to implement a model-based solution to provide an adequate level of technical assurance by managing the requirements and their links to the design elements. A database tool with diagramming capability was used to create a visual traceability structure between the client's original requirements and the refined requirements. A layered model of the system architecture was used to apply requirements to the correct sets of individual design elements. From this combined requirements and architecture model, spreadsheet checklists were generated for designers to perform verification. The resulting data were then re-imported to the database and processed into a detailed report for submission to the client. The solution developed automates assignment of requirements traceability in order to provide exactly the right set of requirements for each deliverable on the route, which may consist of multiple asset types in differing combinations and with local requirements as well as more generic requirements assigned by equivalence. C⃝ 2015 Wiley Periodicals, Inc. Syst Eng 18: 300-309, 2015

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On the role of semantics in automated requirements tracing

Cited by 66 publications

References 76 publications

Exploiting online human knowledge in Requirements Engineering

Exploiting online human knowledge in Requirements Engineering

Null function classification using NFR matrix and decision tree

An Architectural Approach to Requirements Engineering for Infrastructure Projects

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