Detecting requirements defects with NLP patterns: an industrial experience in the railway domain

Ferrari, Alessio; Gori, Gloria; Rosadini, Benedetta; Trotta, Iacopo; Bacherini, Stefano; Fantechi, Alessandro; Gnesi, Stefania

doi:10.1007/s10664-018-9596-7

Cited by 65 publications

(39 citation statements)

References 59 publications

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“…Os tipos de Requirements Smells descritos por [20,21] Observa-se que algumas pesquisas estão investigando problemas de má qualidade na especificação de requisitos e fazendo uso do catálogo de Requirements Smells proposto por [20] e apresentado em [21]. Também, outras anomalias que não estão presentes no catálogo proposto por [20] [4,15,19,22,25,26,27,28,29,33,36,41,43,45,47,50,51] 17 Utilizam ferramentas de terceiros [3,8] 2…”

Section: Resultados Da Buscaunclassified

Requirements Smells como indicadores de má qualidade na especificação de requisitos: Um Mapeamento Sistemático da Literatura

Nascimento¹,

Aranha

Kulesza

et al. 2018

Anais Do WER18 - Workshop Em Engenharia De Requisitos

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Section: Resultados Da Buscaunclassified

Requirements Smells como indicadores de má qualidade na especificação de requisitos: Um Mapeamento Sistemático da Literatura

Nascimento¹,

Aranha

Kulesza

et al. 2018

Anais Do WER18 - Workshop Em Engenharia De Requisitos

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“…Other approaches extract entities and facts from unstructured NL sources according to no standard, often for sake of defects detection 19 or generating models such as ontology, 20 Use case 21,22 or Class Diagram 23,24 . Entities such as subject, verb and object useful for work‐flow generation are also identified in some approaches to generate process model from NL Description 25,26 or from structured text such as text artifacts, 27 and group stories 28 …”

Section: Related Workmentioning

confidence: 99%

Automated generation of terminological dictionary from textual business rules

Haj

Balouki

Gadi

2021

J Software Evolu Process

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To support decision making, organizations tend to operate according to Business Rules, which are usually represented in a natural language format easily understood by all intervenors. According to the business rules manifesto by the Business Rules Group (OMG), rules build on facts, and facts build on concepts as expressed by terms. To avoid ambiguity and misunderstanding, the standardization of the terminology used at the business level becomes a persistent need. However, doing so manually is error prone and time consuming, especially that the Business Rules are the subject of continuous updating. In this paper, we present an automated approach to generate the Business Vocabulary from textual statements of Business Rules. Our approach is distinguished from existing works in that it extracts the Terminological Dictionary as described by the Semantic of Business Vocabulary and Rules (SBVR) standard to provide a more comprehensive meaning for each concept. Accordingly, an in‐depth Natural Language Processing (NLP) is used to extract not only flat list of terms and relations, but also extra specifications and implicit knowledge. With a satisfactory result, our approach has proved its capability to automatically generate the SBVR Terminological Dictionary from large number of natural language business rules statements.

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“…In this section we review some typical cases of ambiguity in requirements documents, based on publications available from the literature [22], and we show how such realworld ambiguities can be explained by means of the presented framework.…”

Section: Ambiguity Cases In Requirements Documentsmentioning

confidence: 99%

“…Other works focusing on the development of tools for ambiguity detection are those of Gleich et al [35] and Tjong and Berry [36]. Recent advances in natural language processing technologies [37], and the rising awareness about requirements quality in industry have led to the application of this previous research in extensive industrial case studies [38,22]. Furthermore, different companies have developed commercial tools to support automated ambiguity detection, as well as other defects or smells.…”

Section: Related Workmentioning

confidence: 99%

Ambiguity in Requirements Engineering: Towards a Unifying Framework

Gervasi

Ferrari

Zowghi

et al. 2019

Lecture Notes in Computer Science

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A long stream of research in RE has been devoted to analyzing the occurrences and consequences of ambiguity in requirements documents. Ambiguity often occurs in documents, most often in natural language (NL) ones, but occasionally also in formal specifications, be it because of abstraction, or of imprecise designation of which real-world entities are denotated by certain expressions. In many of those studies, ambiguity has been considered a defect to be avoided. In this paper, we investigate the nature of ambiguity, and advocate that the simplistic view of ambiguity as merely a defect in the document does not do justice to the complexity of this phenomenon. We offer a more extensive analysis, based on the multiple linguistic sources of ambiguity, and present a list of real-world cases, both in written matter and in oral interviews, that we analyze based on our framework. We hope that a better understanding of the phenomenon can help in the analysis of practical experiences and in the design of more effective methods to detect, mark and handle ambiguity.

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Detecting requirements defects with NLP patterns: an industrial experience in the railway domain

Cited by 65 publications

References 59 publications

Requirements Smells como indicadores de má qualidade na especificação de requisitos: Um Mapeamento Sistemático da Literatura

Requirements Smells como indicadores de má qualidade na especificação de requisitos: Um Mapeamento Sistemático da Literatura

Automated generation of terminological dictionary from textual business rules

Ambiguity in Requirements Engineering: Towards a Unifying Framework

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