Terminological inconsistency analysis of natural language requirements

Misra, Janardan

doi:10.1016/j.infsof.2015.11.006

Cited by 13 publications

(11 citation statements)

References 11 publications

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“…Misra et al [16] focused on the problem of inconsistency of phrases in software requirements described in natural language. They proposed different findings and solutions for different cases, such as the inconsistency between phrases and abbreviations of phrases and semantically similar phrases to describe the same object.…”

Section: Related Workmentioning

confidence: 99%

“…By now, many approaches have been proposed for this purpose by different researchers. For example, Chantree et al [12] proposed methods to discover the presence of duality in natural language requirements; Gervasi et al [13] proposed the method which transforms the requirements into propositional logic to discover potential conflicts in natural language requirements; Arora et al [14,15] presented a method to detect whether the requirements described in natural language conform to the qualified templates with the natural language processing techniques; Misra et al [16] proposed a series of methods to detect terminological inconsistencies in the natural language requirements. Due to the arbitrariness that requirements analysts use the natural language to describe software requirements, existing methods tend to detect some specific types of requirement defects.…”

Section: Introductionmentioning

confidence: 99%

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Automated Conditional Statements Checking for Complete Natural Language Requirements Specification

et al. 2021

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Defects such as the duality and the incompleteness in natural language software requirements specification have a significant impact on the success of software projects. By now, many approaches have been proposed to assist requirements analysts to identify these defects. Different from these approaches, this paper focuses on the requirements incompleteness implied by the conditional statements, and proposes a sentence embedding- and antonym-based approach for detecting the requirements incompleteness. The basic idea is that when one condition is stated, its opposite condition should also be there. Otherwise, the requirements specification is incomplete. Based on the state-of-the-art machine learning and natural language processing techniques, the proposed approach first extracts the conditional sentences from the requirements specification, and elicits the conditional statements which contain one or more conditional expressions. Then, the conditional statements are clustered using the sentence embedding technique. The conditional statements in each cluster are further analyzed to detect the potential incompleteness by using negative particles and antonyms. A benchmark dataset from an aerospace requirements specification has been used to validate the proposed approach. The experimental results have shown that the recall of the proposed approach reaches 68.75%, and the F1-measure (F1) 52.38%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Conditional Statements Checking for Complete Natural Language Requirements Specification

et al. 2021

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“…In diesem Projekt wird ein Ansatz zur automatisierten Abhängigkeitsanalyse mit Fokus auf die Software-Entwicklung entwickelt. Auf Basis der Projektergebnisse und Publikationen werden weitere relevante Ansätze zur Abhängigkeitsanalyse identifiziert und in die Tally-Matrix aufgenommen [21][22][23][24][25][26][27][28][29]. Die Inhalte der Tally-Matrix werden genutzt, um die Lösungsansätze hinsichtlich der verschiedenen Kriterien (siehe Abschnitt 4) zu bewerten.…”

Section: Forschungsproblem Und Forschungszielunclassified

Automatisierte Identifikation und Charakterisierung von Anforderungsabhängigkeiten – Literaturstudie zum Vergleich von Lösungsansätzen

Gräßler¹,

Preuß²,

Oleff³

2020

Proceedings of the 31st Symposium Design for X (DFX2020)

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Many development projects of complex, interdisciplinary products fail due to an inefficient handling of requirement changes. One reason for this is that requirement dependencies and the resulting change propagation are not sufficiently recognized and considered by existing approaches. Based on a literature study and two industry workshops, approaches of automated dependency analysis of requirements for interdisciplinary development are evaluated. The result is that ontology-based approaches are particularly suitable for the automated dependency analysis of requirements due to the use of expert knowledge and the possibility of automation. On this basis, subsequent research activities can further develop the handling of requirements changes in interdisciplinary projects in a targeted manner.

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“…In addition to term extraction, reviewed in Section 2.4.1, there are several other Requirements Engineering tasks in which NLP has been used for automation. These tasks include, among others, identification of inconsistencies and ambiguities [61], [62], [63], [64], [65], [66], [67], requirements tracing [59], [68], [69], [70], [71], [72], requirements change analysis [73], detection of redundancies and implicit requirements relations [74], [75], extraction of models from requirements [76], [77], identification of use cases [78], markup generation for legal requirements [79], [80], enforcement of requirements templates [16], synthesis of user opinions about features [81], [82], and requirements identification [83].…”

Section: Natural Language Processingmentioning

confidence: 99%

Automated Extraction and Clustering of Requirements Glossary Terms

Arora

Sabetzadeh

Briand

et al. 2017

IIEEE Trans. Software Eng.

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A glossary is an important part of any software requirements document. By making explicit the technical terms in a domain and providing definitions for them, a glossary helps mitigate imprecision and ambiguity. A key step in building a glossary is to decide upon the terms to include in the glossary and to find any related terms. Doing so manually is laborious, particularly for large requirements documents. In this article, we develop an automated approach for extracting candidate glossary terms and their related terms from natural language requirements documents. Our approach differs from existing work on term extraction mainly in that it clusters the extracted terms by relevance, instead of providing a flat list of terms. We provide an automated, mathematically-based procedure for selecting the number of clusters. This procedure makes the underlying clustering algorithm transparent to users, thus alleviating the need for any user-specified parameters. To evaluate our approach, we report on three industrial case studies, as part of which we also examine the perceptions of the involved subject matter experts about the usefulness of our approach. Our evaluation notably suggests that: (1) Over requirements documents, our approach is more accurate than major generic term extraction tools. Specifically, in our case studies, our approach leads to gains of 20% or more in terms of recall when compared to existing tools, while at the same time either improving precision or leaving it virtually unchanged. And, (2) the experts involved in our case studies find the clusters generated by our approach useful as an aid for glossary construction.

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Terminological inconsistency analysis of natural language requirements

Cited by 13 publications

References 11 publications

Automated Conditional Statements Checking for Complete Natural Language Requirements Specification

Automated Conditional Statements Checking for Complete Natural Language Requirements Specification

Automatisierte Identifikation und Charakterisierung von Anforderungsabhängigkeiten – Literaturstudie zum Vergleich von Lösungsansätzen

Automated Extraction and Clustering of Requirements Glossary Terms

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