ROBUST integrates a number of different software reliability techniques and methods into one cross-platform tool. The following screen shots come from the Solaris version of the program.
ROBUST provides a wide range of support for traditional software reliability growth models. It is capable of working with failure data in either a cumulative time to failure or failure interval format. It can apply this data to the exponential, logarithmic, power, and delayed-S shape models, providing fitted models in both graphical and textual formats. Based on these models ROBUST can make predictions and projections for based on desired failure count, failure intensity, or additional testing time. To increase the accuracy of these models, ROBUST supports relicalibration, stabilization, and data smoothing techniques which are applicable to all models, and which can be used separately or in conjuncture.
Software reliability growth models where created to help managers and other quality assurance people make estimates as to the time and resources need to bring a project to a desired level of reliability. Unfortunately, such estimates are often needed before testing begins, when there is no failure data to apply a model to. Fortunately, interpretations for the parameters of the logarithmic and exponential SRGMs in terms of defect density are available. ROBUST uses the Malaiya et al. static defect density model to provide a priori estimates of model parameters when they are most needed, before the start of testing.
Defect Density is an important measure of software quality, but traditional methods of defect density estimation are prone to error or often suffer sampling difficulties. Coverage based testing provides an objective estimate of testing effectiveness, and thus often a more reliable means of defect density estimation. ROBUST makes us of the Malaiya et al. coverage model to predict the initial number of defects in a program, based on available coverage and failure data.
To aid the user in making full use of all that ROBUST has to
offer, ROBUST includes online help, to explain the practical use and theory
behind the techniques it implements.