A systematic approach for reducing the cost of regression testing and fault localization through the use of incremental data flow analysis is described. Incremental data flow analysis is used to identify that portion of a program affected by a change so that testing efforts can be focused accordingly. The analysis allows the partitioning of existing test cases into relevant, nonrelevant, and invalid classes. This greatly reduces the effort associated with validating a program following modification.>

A major portion of the effort expended in developing commercial software today is associated with program testing. Schedule and/ or resource constraints frequently require that testing be conducted so as to uncover the greatest number of errors possible in the time allowed. In this paper we describe a study undertaken to assess the potential usefulness of various product-and process-related measures in identifying error-prone software. Our goal was to establish an empirical basis for the efficient utilization of limited testing resources using objective, measurable criteria. Through a detailed analysis of three software products and their error discovery histories, we have found simple metrics related to the amount of data and the structural complexity of programs to be of value for this purpose.