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<p>We develop a methodology to measure the quality levels of a number of releases of a software product in its evolution process. The proposed quality measurement plan is based on the faults detected in field operation of the software. We describe how fault discovery data can be analyzed and reported in a framework very similar to that of the QMP (quality measurement plan) proposed by B. Hoadley (1986). The proposed procedure is especially useful in situations where one has only very little data from the latest release. We present details of implementation of solutions to a class of models on the distribution of fault detection times. The conditions under which the families: exponential, Weibull, or Pareto distributions might be appropriate for fault detection times are discussed. In a variety of typical data sets that we investigated one of these families was found to provide a good fit for the data. The proposed methodology is illustrated with an example involving three releases of a software product, where the fault detection times are exponentially distributed. Another example for a situation where the exponential fit is not good enough is also considered.</p>
software quality; software metrics; software reliability; software quality measurement; fault-detection data; quality levels; software product; quality measurement plan; field operation; fault discovery data; QMP; exponential; Weibull; Pareto distribution; data sets

S. Weerahandi and R. Hausman, "Software Quality Measurement Based on Fault-Detection Data," in IEEE Transactions on Software Engineering, vol. 20, no. , pp. 665-676, 1994.
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