Anurag Goswami

Using Learning Styles of Software Professionals to Improve Their Inspection Team Performance

Inspections of software artifacts during early software development aids managers to detect early faults that may be hard to find and fix later. Results showed inspection ability does not depend on educational background and technical knowledge. This paper presents the results from an industrial empirical study, wherein the Learning Styles (i.e. ability to perceive and process information) of individual inspectors were manipulated to measure its impact on the fault detection effectiveness of inspection teams. Using inspection data from professional developers, we developed virtual teams with varying LS’s of individual inspectors and analyzed the team performance. The results from the current study show that teams of inspectors with diverse LS’s are significantly more effective at detecting faults as compared to teams of inspectors with similar LS’s. Therefore, LS’s can aid software managers to create high performance inspection team(s) and manage software quality.

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 students 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 LSs. The results also provide insights into the LS categories that favor requirements inspection.

Validating Requirements Reviews by Introducing Fault-Type Level Granularity: A Machine Learning Approach

Inspections are a proven approach for improving software requirements quality. Owing to the fact that inspectors report both faults and non-faults (i.e., false-positives) in their inspection reports, a major chunk of work falls on the person who is responsible for consolidating the reports received from multiple inspectors. We aim at automation of fault-consolidation step by using supervised machine learning algorithms that can effectively isolate faults from non-faults. Three different inspection studies were conducted in controlled environments to obtain real inspection data from inspectors belonging to both industry and from academic backgrounds. Next, we devised a methodology to separate faults from non-faults by first using ten individual classifiers from five different classification families to categorize different fault-types (e.g., omission, incorrectness, and inconsistencies). Based on the individual performance of classifiers for each fault-type, we created targeted ensembles that are suitable for identification of each fault-type. Our analysis showed that our selected ensemble classifiers were able to separate faults from non-faults with very high accuracy (as high as 85-89% for some fault-types), with a notable result being that in some cases, individual classifiers performed better than ensembles. In general, our approach can significantly reduce effort required to isolate faults from false-positives during the fault consolidation step of requirements inspections. Our approach also discusses the percentage possibility of correctly classifying each fault-type.