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The goal of software inspection and test is to reduce the expected cost of software failure over the life of a product. This paper extends the use of defect higgers, the eventsthat cause defects to be discovered, to help evaluate the effectiveness of inspections and test scenarios. In the case of inspections, the defect trigger is defined as a set of values that associate the shills of the inspector with the discovered defect. Similarly, for test scenarios, the defect trigger values embody the deferring strategies being used in creating these scenarios. The usefulness of triggers in evaluating the effectiveness of software inspections and tests is demonstrated by evaluating the inspection and test activities of some software products. These evaluations are used to point to both deficiencies in inspection and test strategies and to progress made in improving such strategies. The trigger distribution of the entire inspection or test series may then be used to highlight areas for further investigation, with the aim of improving the design, implementation, and test processes.

Software organizations are in need of methods for understanding, structuring, and improving the data they are collecting. This paper discusses an approach for use when a large number of diverse metrics are already being collected by a software organization. The approach combines two methods. One looks at an organization's measurement framework in a top-down fashion and the other looks at it in a bottom-up fashion. The top-down method, based on the goalquestion-metric (GQM) paradigm, is used to identify the measurement goals of data users. These goals are then mapped to the metrics being used by the organization, allowing us to: (1) identify which metrics are and are not useful to the organization, and (2) determine whether the goals of data user groups can be satisfied by the data that are being collected by the organization. The bottom-up method is based on a data mining technique called attribute focusing (AF). Our method uses this technique to identify useful information in the data that the data users were not aware of. We describe our experience in analyzing data from a software customer satisfaction survey at ISM to illustrate how the AF technique can be combined with the GQM paradigm to improve measurement and data use inside software organizations. T here are many different groups involved in the processes of developing, maintaining, and managing software. Those groups need to use measurement to characterize, control, predict, and improve those processes. We define a meusurement.fiumework

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