Effectiveness of human error taxonomy during requirements inspection

An empirical investigation

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Software inspections are an effective method for achieving high quality software. We hypothesize that inspections focused on identifying errors (i.e., root cause of faults) are better at finding requirements faults when compared to inspection methods that rely on checklists created using lessons-learned from historical fault-data. Our previous work verified that, error based inspections guided by an initial requirements errors taxonomy (RET) performed significantly better than standard fault-based inspections. However, RET lacked an underlying human information processing model grounded in Cognitive Psychology research. The current research reports results from a systematic literature review (SLR) of Software Engineering and Cognitive Science literature - Human Error Taxonomy (HET) that contains requirements phase human errors. The major contribution of this paper is a report of control group study that compared the fault detection effectiveness and usefulness of HET with the previously validated RET. Results of this study show that subjects using HET were not only more effective at detecting faults, but they found faults faster. Post-hoc analysis of HET also revealed meaningful insights into the most commonly occurring human errors at different points during requirements development. The results provide motivation and feedback for further refining HET and creating formal inspection tools based on HET.

Original languageEnglish
Title of host publicationProceedings - SEKE 2016
Subtitle of host publication28th International Conference on Software Engineering and Knowledge Engineering
PublisherKnowledge Systems Institute Graduate School
Pages531-536
Number of pages6
ISBN (Electronic)189170639X, 9781891706394
DOIs
StatePublished - 1 Jan 2016
Event28th International Conference on Software Engineering and Knowledge Engineering, SEKE 2016 - Redwood City, United States
Duration: 1 Jul 20163 Jul 2016

Publication series

NameProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
Volume2016-January
ISSN (Print)2325-9000
ISSN (Electronic)2325-9086

Conference

Conference28th International Conference on Software Engineering and Knowledge Engineering, SEKE 2016
CountryUnited States
CityRedwood City
Period1/07/163/07/16

Fingerprint

Taxonomies
Inspection
Fault detection
Refining
Software engineering

Keywords

  • Empirical study
  • Human error
  • Requirements inspection
  • Taxonomy

Cite this

Anu, V., Walia, G., Hu, W., Carver, J. C., & Bradshaw, G. (2016). Effectiveness of human error taxonomy during requirements inspection: An empirical investigation. In Proceedings - SEKE 2016: 28th International Conference on Software Engineering and Knowledge Engineering (pp. 531-536). (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE; Vol. 2016-January). Knowledge Systems Institute Graduate School. https://doi.org/10.18293/SEKE2016-177
Anu, Vaibhav ; Walia, Gursimran ; Hu, Wenhua ; Carver, Jeffrey C. ; Bradshaw, Gary. / Effectiveness of human error taxonomy during requirements inspection : An empirical investigation. Proceedings - SEKE 2016: 28th International Conference on Software Engineering and Knowledge Engineering. Knowledge Systems Institute Graduate School, 2016. pp. 531-536 (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE).
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Anu, V, Walia, G, Hu, W, Carver, JC & Bradshaw, G 2016, Effectiveness of human error taxonomy during requirements inspection: An empirical investigation. in Proceedings - SEKE 2016: 28th International Conference on Software Engineering and Knowledge Engineering. Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE, vol. 2016-January, Knowledge Systems Institute Graduate School, pp. 531-536, 28th International Conference on Software Engineering and Knowledge Engineering, SEKE 2016, Redwood City, United States, 1/07/16. https://doi.org/10.18293/SEKE2016-177

Effectiveness of human error taxonomy during requirements inspection : An empirical investigation. / Anu, Vaibhav; Walia, Gursimran; Hu, Wenhua; Carver, Jeffrey C.; Bradshaw, Gary.

Proceedings - SEKE 2016: 28th International Conference on Software Engineering and Knowledge Engineering. Knowledge Systems Institute Graduate School, 2016. p. 531-536 (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE; Vol. 2016-January).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

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Anu V, Walia G, Hu W, Carver JC, Bradshaw G. Effectiveness of human error taxonomy during requirements inspection: An empirical investigation. In Proceedings - SEKE 2016: 28th International Conference on Software Engineering and Knowledge Engineering. Knowledge Systems Institute Graduate School. 2016. p. 531-536. (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE). https://doi.org/10.18293/SEKE2016-177