Validation of an approach for improving existing measurement frameworks
Software organizations are in need of methods to understand, structure, and improve the data they are collecting. We have developed an approach for use when a large number of diverse metrics are already being collected by a software organization [1], [2]. The approach combines two methods. One looks at an organization's measurement framework in a top-down goal-oriented fashion and the other looks at it in a bottom-up data-driven fashion. The top-down method is based on a measurement paradigm called Goal-Question-Metric (GQM). The bottom-up method is based on a data mining technique called Attribute Focusing (AF). A case study was executed to validate this approach and to assess its usefulness in an industrial environment. The top-down and bottom-up methods were applied in the customer satisfaction measurement framework at the IBM Toronto Laboratory. The top-down method was applied to improve the customer satisfaction (CUSTSAT) measurement from the point of view of three data user groups. It identified several new metrics for the interviewed groups, and also contributed to better understanding the data user needs. The bottom-up method was used to gain new insights into the existing CUSTSAT data. Unexpected associations between key variables prompted new business insights, and revealed problems with the process used to collect and analyze the CUSTSAT data. This paper uses the case study and its results to qualitatively compare our approach against current ad hoc practices used to improve existing measurement frameworks.
gqm data mining af case study software metrics goal-oriented measurement knowledge discovery experimental validation method evaluation