Abstract: Testing accounts for the major percentage of technical
contribution in the software development process. Typically, it
consumes more than 50 percent of the total cost of developing a
piece of software. The selection of software tests is a very important
activity within this process to ensure the software reliability
requirements are met. Generally tests are run to achieve maximum
coverage of the software code and very little attention is given to the
achieved reliability of the software. Using an existing methodology,
this paper describes how to use Bayesian Belief Networks (BBNs) to
select unit tests based on their contribution to the reliability of the
module under consideration. In particular the work examines how the
approach can enhance test-first development by assessing the quality
of test suites resulting from this development methodology and
providing insight into additional tests that can significantly reduce
the achieved reliability. In this way the method can produce an
optimal selection of inputs and the order in which the tests are
executed to maximize the software reliability. To illustrate this
approach, a belief network is constructed for a modern software
system incorporating the expert opinion, expressed through
probabilities of the relative quality of the elements of the software,
and the potential effectiveness of the software tests. The steps
involved in constructing the Bayesian Network are explained as is a
method to allow for the test suite resulting from test-driven
development.
Abstract: Software developed for a specific customer under contract
typically undergoes a period of testing by the customer before
acceptance. This is known as user acceptance testing and the process
can reveal both defects in the system and requests for changes to
the product. This paper uses nonhomogeneous Poisson processes to
model a real user acceptance data set from a recently developed
system. In particular a split Poisson process is shown to provide an
excellent fit to the data. The paper explains how this model can be
used to aid the allocation of resources through the accurate prediction
of occurrences both during the acceptance testing phase and before
this activity begins.