Egg price forecasting of shelled eggs is a complex problem. Traditionally, future egg price has been predicted using a combination of regression analysis and experienced-based intuition to build a model, which is then fine-tuned to prevalent market conditions. Even after collecting reliable and expensive data, the subsequent analysis, in many cases, does not produce a high confidence to explain the variations in egg price. In the current project, a different approach using neural networks was used to forecast egg price. A neural network is a mathematical model of an information-processing structure that is loosely based on our present understanding of the working of human brain. An artificial neural network consists of a large number of simple processing elements connected to each other in a network. Urner Barry egg quotes from 1991 to 2002 as well as number of hens, egg storage capacity, and number of eggs placed for hatching from the USDA databases (1993 to 2000) were used to forecast egg price. Regression analysis explained only 37% of the variation in egg price due to the above-mentioned 3 factors. Neural networks, on the other hand, recognize the pattern in previous years' egg prices and then predict the future price more efficiently. The 3 networks used in this research (Ward, back-propagation, and general regression neural networks) fit the forecast line more tightly to the previous year's egg price line than did regression analysis. In the case of general regression neural networks, the R2 value was as high as 60%. Results suggest that neural networks may be a more reliable method of egg price forecasting than simple regression analysis if reliable data are collected and manipulated for such models.

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Test case generation based on Finite State Machines (FSMs) has been extensively investigated due to its accuracy and simplicity. Several test criteria have been proposed in the literature to generate test cases based on FSMs. One of the oldest criteria is the Switch Cover. As a main feature, the Switch Cover criterion defines that all transition pairs of an FSM must be covered. The classical Switch Cover algorithm converts the FSM into a graph (known as Dual Graph); this graph is balanced, and, finally, traversed based on an Eulerian Cycle algorithm. In this context, considering the stage where an FSM is converted into a graph, this study investigates other search algorithms on graphs, namely Depth-First Search (DFS) and Breadth-First Search (BFS), for generating test sets from a Dual Graph. We presented an experimental study that compares the DFS, BFS algorithms with the Eulerian Cycle. The study was conducted with a set of random and real-world machines, taking into account the number of test cases, the test suite size, the average length of sequences and generation time.

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