Abstract: In this paper, we present a model-based regression test
suite reducing approach that uses EFSM model dependence analysis
and probability-driven greedy algorithm to reduce software regression
test suites. The approach automatically identifies the difference
between the original model and the modified model as a set of
elementary model modifications. The EFSM dependence analysis is
performed for each elementary modification to reduce the regression
test suite, and then the probability-driven greedy algorithm is adopted
to select the minimum set of test cases from the reduced regression test
suite that cover all interaction patterns. Our initial experience shows
that the approach may significantly reduce the size of regression test
suites.
Abstract: The article describes a case study on one of Czech
Republic-s manufacturing middle size enterprises (ME), where due to
the European financial crisis, production lines had to be redesigned
and optimized in order to minimize the total costs of the production
of goods. It is considered an optimization problem of minimizing the
total cost of the work load, according to the costs of the possible
locations of the workplaces, with an application of the Greedy
algorithm and a partial analogy to a Set Packing Problem. The
displacement of working tables in a company should be as a one-toone
monotone increasing function in order for the total costs of
production of the goods to be at minimum. We use a heuristic
approach with greedy algorithm for solving this linear optimization
problem, regardless the possible greediness which may appear and
we apply it in a Czech ME.