Effectiveness of Human Error Taxonomy during Requirements Inspection: An Empirical Investigation

Abstract

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.

Modell zur Übertragbarkeit der Ergebnisse aus der Feldstudie auf größere Regionen

“…Ganz nach der requirements-error-taxonomy-Theorie von Anu et al (2016) Das Modell selbst wird im Geoinformationssystem ArcGIS Pro abgebildet, und auch die Berechnungen werden dort durchgeführt. Es basiert auf den beschriebenen geographisch referenzierten Daten, mit denen Netzwerke modelliert und Routen geplant werden können.…”

Efficiency improvement by navigated safety inspection involving visual clutter based on the random search model

“…Zhang et al [15] interviewed several experienced experts in construction industry, and the interviewed experts acclaimed the prototype safety management tools used in mobile safety management applications. Anu et al [12] also asked their study subjects to provide feedback of the designed human error taxonomy and the result proved that the authors' idea received excellent users' reviews. Despite the validity of these approaches, these assessments are still relatively subjective and full of uncertainties.…”

“…Their experiment demonstrated that correct risk detection increased for those superintendents that were trained on a virtual construction environment; this was attributed to improved cognitive learning. Anu et al [12] argued that the previous hazard taxonomy according to the symptoms of errors is inefficient and extremely time-consuming. They proposed that human error taxonomy should refer to the cause of problems, for improving software inspection performance.…”

“…Perlman et al [8] examined risk detection accuracy of inspectors trained on paper documents or on a virtual environment, and they found that training on the virtual environment increased the number of correctly detected risks. Similarly, Anu et al [12] analyzed the two indexes of effectiveness and efficiency as the quantitative assessment for software inspection. Briefly, effectiveness refers to the number of identified targets, while efficiency refers to the ratio of the number of identified targets to the time consumed.…”

Incorporating Human Error Education into Software Engineering Courses via Error-based Inspections

“…The Human Error Taxonomy (HET) [1] was developed to support the error abstraction (EA) leg of error-based inspections. HET provides a structured list of the most commonly occurring requirements phase human errors.…”