Effectiveness of Human Error Taxonomy during Requirements Inspection: An Empirical Investigation
International Conference on Software Engineering and Knowledge Engineering
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.
MSU Digital Commons Citation
Anu, Vaibhav and Walia, Gursimran, "Effectiveness of Human Error Taxonomy during Requirements Inspection: An Empirical Investigation" (2016). Department of Computer Science Faculty Scholarship and Creative Works. 1.
Anu, V., Walia, G., Hu, W., Carver, J., and Bradshaw, G. “Effectiveness of Human Error Taxonomy during Requirements Inspection: An Empirical Investigation”, 2016 International Conference on Software Engineering and Knowledge Engineering. SEKE 2016. San Francisco, California, USA.