An empirical study of the effect of learning styles on the faults found during the software requirements inspection

Inspections aid software managers by early detection and removal of faults committed during the creation of requirements and design documents. This helps reduce the rework during the later stages of software development. While inspections are effective in practice, the evidence suggests that the effectiveness of inspectors varies widely. Cognitive psychologists have used Learning Style (LS) to show the improvement in student's score by considering their characteristic strength and preferences to acquire and process information. This concept of LS can cross over to software engineering as a means of increasing the inspection effectiveness. This paper investigates the effect of the LS of inspectors on fault detection abilities of inspection teams and individual inspectors. Using the inspection data with varying number of participants, we analyzed the effect of the LS of inspectors across various inspection team sizes on the inspection performance. We also analyzed the effect of LS categories on the individual inspection performance. The initial results show that the teams composed of inspectors with different LS preferences are more effective and efficient than the teams of inspectors who had similar LS's. The results also provide insights into the LS categories that favor requirements inspection.