3 research outputs found
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Test data generation from UML state machine diagrams using GAs
Automatic test data generation helps testers to validate
software against user requirements more easily. Test
data can be generated from many sources; for example,
experience of testers, source program, or software
specification. Selecting a proper test data set is a
decision making task. Testers have to decide what test
data that they should use, and a heuristic technique is
needed to solve this problem automatically. In this
paper, we propose a framework for generating test data
from software specifications. The selected specification
is Unified Modeling Language (UML) state machine
diagram. UML state machine diagram describes a
system in term of state which can be changed when
there is an action occurring in the system. The
generated test data is a sequence of these actions.
These sequences of action help testers to know how they
should test the system. The quality of generated test
data is measured by the number of transitions which is
fired using the test data. The more transitions test data
can fire, the better quality of test data is. The number of
coverage transitions is also used as a feedback for a
heuristic search for a better test set. Genetic algorithms
(GAs) are selected for searching the best test data. Our
experimental results show that the proposed GA-based
approach can work well for generating test data for
some types of UML state machine diagrams
Evolutionary hybrid approaches for generation scheduling in power systems
Generation scheduling (GS) in power systems is a tough optimisation problem which continues to present a challenge for efficient solution techniques. The solution is to define on/off decisions and generation levels for each electricity generator of a power system for each scheduling interval. The solution procedure requires simultaneous consideration of binary decision and continuous variables. In recent years researchers have focused much attention on developing new hybrid approaches using evolutionary and traditional exact methods for this type of mixed-integer problems. This paper investigates how the optimum or near optimum solution for the GS problem may be quickly identified. A design is proposed which uses a variety of metaheuristic, heuristics and mathematical programming techniques within a hybrid framework. The results obtained for two case studies are promising and show that the hybrid approach offers an effective alternative for solving the GS problems within a realistic timeframe