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Usefulness of a Human Error Identification Tool for Requirements Inspection: An Experience Report

Part of the Lecture Notes in Computer Science book series (LNPSE,volume 10153)

Abstract

Context and Motivation: Our recent work leverages Cognitive Psychology research on human errors to improve the standard fault-based requirements inspections. Question: The empirical study presented in this paper 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. Results: The results showed that the HEAA tool, though effective, presented challenges during the error abstraction process. Contribution: In this experience report, we present major challenges during the study execution and lessons learned for future replications.

Keywords

  • Human Error
  • Future Replication
  • Error Mechanism
  • Error Class
  • Inspection Technique

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Anu, V., Walia, G.S., Hu, W., Carver, J.C., Bradshaw, G.: Effectiveness of human error taxonomy during requirements inspection: an empirical investigation. In: Software Engineering and Knowledge Engineering, SEKE 2016 (2016)

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Acknowledgment

This work was supported by NSF Awards 1423279 and 1421006. The authors would like to thank the students of the Software Requirements course at North Dakota State University for participating in this study.

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Correspondence to Vaibhav Anu .

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Anu, V., Walia, G., Bradshaw, G., Hu, W., Carver, J.C. (2017). Usefulness of a Human Error Identification Tool for Requirements Inspection: An Experience Report. In: Grünbacher, P., Perini, A. (eds) Requirements Engineering: Foundation for Software Quality. REFSQ 2017. Lecture Notes in Computer Science(), vol 10153. Springer, Cham. https://doi.org/10.1007/978-3-319-54045-0_26

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  • DOI: https://doi.org/10.1007/978-3-319-54045-0_26

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