MaramaAIC: tool support for consistency management and validation of requirements

Kamalrudin, Massila; Hosking, John; Grundy, John

doi:10.1007/s10515-016-0192-z

Cited by 23 publications

(8 citation statements)

References 34 publications

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“…Kamalrudin et al [49] developed MaramaAIC, a tool for identifying inconsistencies in natural language requirements. By extracting essential use case patterns, this approach identifies inconsistencies but is constrained by its reliance on a pre-defined interaction library.…”

Section: A Requirements Translation Directly To Formal Languagesmentioning

confidence: 99%

BERDD: A Behaviour Engineering-Based Approach for Requirements Defects Detection

Anwer,

Wen,

Hassan

et al. 2024

IEEE Access

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Detecting software requirements defects is crucial in reducing the risk of software project failures. Existing methods for automatic detection, especially during requirements changes, are limited in coverage and often lack robust tool support. Addressing this gap, we define the four most common types of requirements defects (incompleteness, inconsistency, redundancy, and ambiguity) and present algorithms for their detection. We propose a novel behaviour engineeringbased approach, translating software requirements into behaviour trees and then into the Web Ontology Language (OWL). We developed 'requirements defects identifier', a tool that queries the OWL knowledge base to identify potential defects during requirements analysis and change. Validated on three final-year student projects, our approach demonstrated success in detecting all four defect types, offering broader coverage compared to existing tools. A real-world case study has been used to validate the applicability of the proposed approach. Our experiments demonstrate that the tool can successfully detect all four different types of requirement defects during both requirements analysis and requirements change.

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Section: A Requirements Translation Directly To Formal Languagesmentioning

confidence: 99%

BERDD: A Behaviour Engineering-Based Approach for Requirements Defects Detection

Anwer,

Wen,

Hassan

et al. 2024

IEEE Access

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“…In [39] and [40], NLP-based extraction approaches are proposed for transforming NL-requirements into extracting goal-use-cases and requirements key elements proposed in [41] respectively. Alternatively, in [42], [43], use case models are extracted from NL-requirements through matching the input requirements against a simple set of regular expressions.…”

Section: Formality Levels Transformationsmentioning

confidence: 99%

Requirements Formality Levels Analysis and Transformation of Formal Notations into Semi-formal and Informal Notations (S)

Zaki¹

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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It is pivotal to have well-specified requirements to eliminate errors at an early stage of the system development life cycle. Some quality standards recommend the use of formal methods -mandate requirements to be expressed in formal notations -to detect errors. However, formal notations are not suitable for non-experts and may not be understood by all the stakeholder. To fix this, bidirectional transformations among requirement representation levels are required to maintain traceability and facilitate the communication of requirements among all the involved parties. This paper reflects on the different formality levels of requirements specifications including: informal, semi-formal, and formal notations. In addition, an automated multi-layer transformation approach is proposed to enable bi-directional transformation among requirements levels.

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“…However, failure to develop product engineering to consider a product's software development life cycle (SDLC) in its software engineering phase may lead to failure in producing good quality products. Thus, the product quality received by stakeholders will not be up to standard, and will lead error-prone equipment [1]. Furthermore, most application-lead areas of IoT technology are insufficiently developed and difficult to approach due to limited resources [2].…”

Section: Introductionmentioning

confidence: 99%

Development of Arduino applications for IoT applications in software engineering education: a systematic literature review

Yusop,

Moketar,

Sadikan

2024

Bulletin EEI

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The continuous development of software applications is a necessary step in producing high-quality products that will consistently meet end-user expectations and stakeholder needs. Development of Arduino applications, embedded in a product’s hardware, can and should be considered at the software engineering phase itself, even though current practice dictates it be handled by product engineers. The method used in this investigation was based on a systematic literature review (SLR). Therefore, this paper depicts a gap that currently exists within the body of literature surrounding the development of Arduino applications for ‘internet of things’ (IoT) applications in software engineering education in commercial and research fields. The result of this study are two findings investigates: i) relevant Arduino application development used in software engineering and ii) method for applying software engineering for Arduino applications. The limitations and constraints of each technique in respect to Arduino apps were also examined in order to provide a better understanding of each body of study's weaknesses and strengths. We realise that these studies are still insufficient and need to be evaluated and improved further.

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MaramaAIC: tool support for consistency management and validation of requirements

Cited by 23 publications

References 34 publications

BERDD: A Behaviour Engineering-Based Approach for Requirements Defects Detection

BERDD: A Behaviour Engineering-Based Approach for Requirements Defects Detection

Requirements Formality Levels Analysis and Transformation of Formal Notations into Semi-formal and Informal Notations (S)

Development of Arduino applications for IoT applications in software engineering education: a systematic literature review

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