A Rule-Based Method for Detecting Discrepancies in Software Project Productivity Analysis

This paper examines traditional data envelopment analysis (DEA), cross efficiency (CE), and game efficiency (GE) models for software productivity analysis and ranking. Additionally, for CE models, secondary objectives of aggressive and benevolent formulations are considered. An entropy criterion is used to identify the best-performing model. Experiments are conducted using the ISBSG dataset. The results show that aggressive CE model formulations have the lowest entropy values and produce unique project rankings. The GE model is computationally intensive and does not provide sufficient benefits to justify the extra effort. A rule-based framework is introduced to identify discrepancies in project rankings across models. This framework helps managers pinpoint truly efficient projects.