Improving Software Reliability Forecasting
نویسندگان
چکیده
Software reliability deals with behaviour of a software system in operation and it is defined as the probability of working without failure for a specified interval of time. In a number of recent disasters the computer has been wholly or partially to blame. In Ref. [1] an alarm signal is given by the following message: "Sometime soon, software reliability is going to become a highly visible and important field. Unfortunately, given human nature, its thrust into prominence will only happen once we experience the software equivalent of the Chernobyl, Bhopal, or space shuttle Challenger disasters. Such a disaster is likely to happen in the next few years." Therefore, software reliability forecasting is a problem of increasing importance for many critical applications. Selection of a particular model is very important in software reliability predictions. In spite of much research effort [2], there is no universally applicable software reliability growth model (SRGM) which can be trusted to give accurate predictions of reliability in all circumstances. This paper suggests some "ways" of obtaining more accurate predictions by building a supermodel. A comparison is made between this approach and the general method for predicting the medians using actual data sets from different software projects. We are also investigating the application of autoregressive moving average processes (ARMA) to software reliability forecasting. Our analysis shows that accuracy of the predictions in different applications can be judged using the proposed methods.
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