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

@article{Anu2018DevelopmentOA,
  title={Development of a human error taxonomy for software requirements: A systematic literature review},
  author={Vaibhav Anu and Wenhua Hu and Jeffrey C. Carver and Gursimran Singh Walia and Gary L. Bradshaw},
  journal={Inf. Softw. Technol.},
  year={2018},
  volume={103},
  pages={112-124}
}
Abstract Background Human-centric software engineering activities, such as requirements engineering, are prone to error. These human errors manifest as faults. To improve software quality, developers need methods to prevent and detect faults and their sources. Aims Human error research from the field of cognitive psychology focuses on understanding and categorizing the fallibilities of human cognition. In this paper, we applied concepts from human error research to the problem of software… Expand
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The findings suggest that software practitioners’ personality characteristics should also be paid more attention to as they are important when conducting RE effectively. Expand
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References

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Defect Prevention in Requirements Using Human Error Information: An Empirical Study
TLDR
The results of this study show that a better understanding of human errors does lead developers to insert fewer problems into their own requirements documents, and indicate that different types of Human Error information have different impacts on fault prevention. Expand
Detection of Requirement Errors and Faults via a Human Error Taxonomy: A Feasibility Study
TLDR
The Human Error Taxonomy is effective for identifying and classifying requirements errors and faults, thereby helping to improve the overall quality of the SRS and the software. Expand
Effectiveness of Human Error Taxonomy during Requirements Inspection: An Empirical Investigation
TLDR
Results show that subjects using HET were not only more effective at detecting faults, but they found faults faster, and post-hoc analysis of HET revealed meaningful insights into the most commonly occurring human errors at different points during requirements development. Expand
Using a Cognitive Psychology Perspective on Errors to Improve Requirements Quality: An Empirical Investigation
TLDR
A newly developed Human Error Taxonomy (HET) and a formal Error-Abstraction and Inspection (EAI) process to improve fault detection performance of inspectors during the requirements inspection and provide useful insights into commonly occurring human errors that contributed to requirement faults are described. Expand
A systematic literature review to identify and classify software requirement errors
TLDR
A systematic literature review is presented to develop taxonomy of errors that may occur during the requirements phase of software lifecycle that is designed to aid developers during the requirement inspection process and to improve overall software quality. Expand
Using error abstraction and classification to improve requirement quality: conclusions from a family of four empirical studies
Achieving high software quality is a primary concern for software development organizations. Researchers have developed many quality improvement methods that help developers detect faults early inExpand
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TLDR
A process improvement approach is proposed, which applies the theories of human error to build an expert system for automated diagnosis of problems. Expand
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The identification of root causes is critical for software organizations to prevent defects and improve software quality. Human error is thought to be a leading cause of software defects inducements.Expand
Usefulness of a Human Error Identification Tool for Requirements Inspection: An Experience Report
TLDR
This empirical study investigates the effectiveness of a newly developed Human Error Abstraction Assist (HEAA) tool in helping inspectors identify human errors to guide the fault detection during the requirements inspection. Expand
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TLDR
An empirical study is presented whose main purpose is to investigate whether defect detection in requirements documents can be improved by focusing on the errors in a document rather than the individual faults that they cause. Expand
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