Detection of Requirement Errors and Faults Via a Human Error Taxonomy: A Feasibility Study
Background: Developing correct software requirements is important for overall software quality. Most existing quality improvement approaches focus on detection and removal of faults (i.e. problems recorded in a document) as opposed identifying the underlying errors that produced those faults. Accordingly, developers are likely to make the same errors in the future and fail to recognize other existing faults with the same origins. Therefore, we have created a Human Error Taxonomy (HET) to help software engineers improve their software requirement specification (SRS) documents. Aims: The goal of this paper is to analyze whether the HET is useful for classifying errors and for guiding developers to find additional faults. Methods: We conducted a empirical study in a classroom setting to evaluate the usefulness and feasibility of the HET. Results: First, software developers were able to employ error categories in the HET to identify and classify the underlying sources of faults identified during the inspection of SRS documents. Second, developers were able to use that information to detect additional faults that had gone unnoticed during the initial inspection. Finally, the participants had a positive impression about the usefulness of the HET. Conclusions: The HET is effective for identifying and classifying requirements errors and faults, thereby helping to improve the overall quality of the SRS and the software.
10.1145/2961111.2962596Final published version Open
MSU Digital Commons Citation
Hu, Wenhua; Carver, Jeffrey C.; Anu, Vaibhav; Walia, Gursimran S.; and Bradshaw, Gary, "Detection of Requirement Errors and Faults Via a Human Error Taxonomy: A Feasibility Study" (2016). Department of Computer Science Faculty Scholarship and Creative Works. 215.