SOFTWARE QUALITY MEASUREMENT BASED ON FAULT-DETECTION DATA

In this paper, 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 detecte d 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 Hoadley in 1986. The proposed procedure is especially useful in situations where one has o nly very little data from the latest release. We present details of im plementation of solutions to a class of models on the distribution of fault detection times. The conditions under which the families, 1) exp onential, 2) Weibull, or 3) 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 a n example involving three releases of a software product, where the fa ult detection times are exponentially distributed. Another example for a situation where the exponential fit is not good enough is also cons idered.