Development of a human error taxonomy for software requirements: A systematic literature review

Anu, Vaibhav; Hu, Wenhua; Carver, Jeffrey C.; Walia, Gursimran Singh; Bradshaw, Gary L.

doi:10.1016/j.infsof.2018.06.011

Cited by 45 publications

(27 citation statements)

References 11 publications

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“…In the last two decades, there has been a surge in the use of CVs in RE for improving the overall quality of both the development process and the final software product (system) [7][8][9][22][23][24][25]. For instance, the notion of ontologies has been used in RE, to improve the completeness and correctness of requirements specification, to assist in comprehending requirements, to help in modelling domain knowledge and to effectively manage requirements change [22].…”

Section: Background and Related Workmentioning

confidence: 99%

“…Anu et al [9] conducted an SLR study to identify, analyze and classify possible reasons of human errors that occur during requirements engineering and their impact on software quality. They only used an automated search strategy in electronic databases, conducted from 2006 to October 2014.…”

Section: Related Workmentioning

confidence: 99%

“…There are some previous studies that have investigated the use of CV in RE [7][8][9][10][11]. However, none of these secondary studies captures all aspects, impacts and evidence that interest us.…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Controlled Vocabularies on Requirements Engineering Activities: A Systematic Mapping Study

et al. 2020

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Context: The use of controlled vocabularies (CVs) aims to increase the quality of the specifications of the software requirements, by producing well-written documentation to reduce both ambiguities and complexity. Many studies suggest that defects introduced at the requirements engineering (RE) phase have a negative impact, significantly higher than defects in the later stages of the software development lifecycle. However, the knowledge we have about the impact of using CVs, in specific RE activities, is very scarce. Objective: To identify and classify the type of CVs, and the impact they have on the requirements engineering phase of software development. Method: A systematic mapping study, collecting empirical evidence that is published up to July 2019. Results: This work identified 2348 papers published pertinent to CVs and RE, but only 90 primary published papers were chosen as relevant. The process of data extraction revealed that 79 studies reported the use of ontologies, whereas the remaining 11 were focused on taxonomies. The activities of RE with greater empirical support were those of specification (29 studies) and elicitation (28 studies). Seventeen different impacts of the CVs on the RE activities were classified and ranked, being the two most cited: guidance and understanding (38%), and automation and tool support (22%). Conclusions: The evolution of the last 10 years in the number of published papers shows that interest in the use of CVs remains high. The research community has a broad representation, distributed across the five continents. Most of the research focuses on the application of ontologies and taxonomies, whereas the use of thesauri and folksonomies is less reported. The evidence demonstrates the usefulness of the CVs in all RE activities, especially during elicitation and specification, helping developers understand, facilitating the automation process and identifying defects, conflicts and ambiguities in the requirements. Collaboration in research between academic and industrial contexts is low and should be promoted.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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The Impact of Controlled Vocabularies on Requirements Engineering Activities: A Systematic Mapping Study

et al. 2020

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“…Defect management is a rather complex problem. One of the tasks is the system of defect taxonomy [6,7]. Different authors suggest various sets of "good taxonomy" indicators.…”

Section: Evaluating Resources For Defect Search and Eliminationmentioning

confidence: 99%

Providing Functional Safety of Hardware-Software Complexes as Mandatory Condition of Forming Digital Eco-Environment at Innovative Project Implementation

Gvozdev¹,

Levkov²,

Blinova³

et al. 2019

Proceedings of the 21st International Workshop on Computer Science and Information Technologies (CSIT 2019)

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A key part digital eco-environment plays at the implementation of innovative projects brings about qualitative changes of requirements towards the functional safety of hardware-software complexes. The paper dwells upon the issues of increasing the functional safety of hardware-software complexes by means of reasonable resource allocation for locating and eliminating defects. The purpose of the research is the development of the information support of planning resources for defect elimination in software modules. The scientific idea is the development of the method of building linear regression dependencies on the basis of common use of expert assessments and measuring data. The approach novelty consists in the transformation of expert assessments and measuring data towards a single form of the random value distribution law.

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“…e SLR method has been used successfully on diverse topics within the area of requirements engineering [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow

Ahmad

Feng

Khan

et al. 2020

Security and Communication Networks

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Context. The improvements made in the last couple of decades in the requirements engineering (RE) processes and methods have witnessed a rapid rise in effectively using diverse machine learning (ML) techniques to resolve several multifaceted RE issues. One such challenging issue is the effective identification and classification of the software requirements on Stack Overflow (SO) for building quality systems. The appropriateness of ML-based techniques to tackle this issue has revealed quite substantial results, much effective than those produced by the usual available natural language processing (NLP) techniques. Nonetheless, a complete, systematic, and detailed comprehension of these ML based techniques is considerably scarce. Objective. To identify or recognize and classify the kinds of ML algorithms used for software requirements identification primarily on SO. Method. This paper reports a systematic literature review (SLR) collecting empirical evidence published up to May 2020. Results. This SLR study found 2,484 published papers related to RE and SO. The data extraction process of the SLR showed that (1) Latent Dirichlet Allocation (LDA) topic modeling is among the widely used ML algorithm in the selected studies and (2) precision and recall are amongst the most commonly utilized evaluation methods for measuring the performance of these ML algorithms. Conclusion. Our SLR study revealed that while ML algorithms have phenomenal capabilities of identifying the software requirements on SO, they still are confronted with various open problems/issues that will eventually limit their practical applications and performances. Our SLR study calls for the need of close collaboration venture between the RE and ML communities/researchers to handle the open issues confronted in the development of some real world machine learning-based quality systems.

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Development of a human error taxonomy for software requirements: A systematic literature review

Cited by 45 publications

References 11 publications

The Impact of Controlled Vocabularies on Requirements Engineering Activities: A Systematic Mapping Study

The Impact of Controlled Vocabularies on Requirements Engineering Activities: A Systematic Mapping Study

Providing Functional Safety of Hardware-Software Complexes as Mandatory Condition of Forming Digital Eco-Environment at Innovative Project Implementation

A Systematic Literature Review on Using Machine Learning Algorithms for Software Requirements Identification on Stack Overflow

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