Understanding Uncertainty of Software Requirements Engineering: A Systematic Literature Review Protocol

Salih, Ahmad M.; Omar, Mazni; Yasin, Azman

doi:10.1007/978-981-10-7796-8_13

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Other uncertainty classifications have also been proposed for specific domains within software development and engineering, mainly in the areas of adaptive systems [73,135,144,180,199,201,223], complex event processing [125], databases [131,156,175,178,195,200,224], requirements engineering [207], and cyber-physical systems [120,121]. All of them try to harmonize the terminology of uncertainty and propose conceptual frameworks that classify different types of uncertainty according to several criteria.…”

Section: Existing Classifications Of Uncertaintymentioning

confidence: 99%

“…Third, we did not consider works proposing representations of uncertainty that do not use software modeling notations; for example those that use mathematical models [204], programming languages [136], programming libraries [152,173], or use lower-level modeling notations, such as partial Kripke structures [132]. Finally, we excluded papers that provide classifications of the different kinds of uncertainty applied to specific application domains, such as [135,144,180,199,201,207], or that do not explicitly represent uncertainty [124].…”

Section: Inclusion and Exclusion Criteriamentioning

confidence: 99%

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Uncertainty representation in software models: a survey

et al. 2021

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This paper provides a comprehensive overview and analysis of research work on how uncertainty is currently represented in software models. The survey presents the definitions and current research status of different proposals for addressing uncertainty modeling, and introduces a classification framework that allows to compare and classify existing proposals, analyse their current status and identify new trends. In addition, we discuss possible future research directions, opportunities and challenges.A fundamental characteristic of software models is their ability to represent the relevant characteristics of the system under study, at the appropriate level of abstraction. Software models were initially conceived to design and develop general Information Technology (IT) systems, such as financial applications, enterprise databases or component-based systems, and have proven to be excellent artefacts for representing the basic structure and

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Section: Existing Classifications Of Uncertaintymentioning

confidence: 99%

Section: Inclusion and Exclusion Criteriamentioning

confidence: 99%

Uncertainty representation in software models: a survey

et al. 2021

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“…L. M. Marinho et al 2015) propose and evaluate techniques to distinguish risks and uncertainties to reduce the latter in software projects. Salih et al (Salih et al 2017) provide an overview of existing work on uncertainty involved in requirements engineering via a categorisation of relevant sources, while several questions are left open. Measurement uncertainty was studied in depth by da Silva Hack et al (da Silva Hack & ten Caten 2012), resulting in a classification of approaches and a list of methods for calculating uncertainty.…”

Section: Related Workmentioning

confidence: 99%

Tolerance in Model-Driven Engineering: A Systematic Literature Review with Model-Driven Tool Support

Weidmann¹,

Kannan²,

Anjorin³

2021

Preprint

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Managing models in a consistent manner is an important task in the field of Model-Driven Engineering (MDE).Although restoring and maintaining consistency is desired in general, recent work has pointed out that always strictly enforcing consistency at any point of time is often not feasible in real-world scenarios, and sometimes even contrary to what a user expects from a trustworthy MDE tool. The challenge of tolerating inconsistencies has been discussed from different viewpoints within and outside the modelling community, but there exists no structured overview of existing and current work in this regard.In this paper, we provide such an overview to help join forces tackling the unresolved problems of tolerating inconsistencies in MDE. We follow the standard process of a Systematic Literature Review (SLR) to point out what tolerance means, how it relates to uncertainty, which examples for tolerant software systems have already been discussed, and which benefits and drawbacks tolerating inconsistencies entails. Furthermore, we propose a tool-chain that helps conducting SLRs in computer science and also eases the reproduction of results. Relevant meta-data of the collected sources is uniformly described in a textual modelling language and exported to the graph database Neo4j to query aggregated information.

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Post-Incident Action Items: Crossroads of Requirements Engineering and Software Evolution

Pope,

Sillito

2024

Proceedings of the 1st IEEE/ACM Workshop on Multi-Disciplinary, Open, and RElevant Requirements Engineering

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Understanding Uncertainty of Software Requirements Engineering: A Systematic Literature Review Protocol

Cited by 3 publications

References 9 publications

Uncertainty representation in software models: a survey

Uncertainty representation in software models: a survey

Tolerance in Model-Driven Engineering: A Systematic Literature Review with Model-Driven Tool Support

Post-Incident Action Items: Crossroads of Requirements Engineering and Software Evolution

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