Abstract
Software testing is a significant stage in software development lifecycle. There are different sorts of' structural software testing methodologies that may be generally utilized and moved forward through enhancing the traverse of all of the conceivable code software paths. The interest for automating data testing is growing; however, manual testing strategies utilization would be expensive and costly. Heuristic measure is being applied to; detect how better the result might be (solution fitness); result development mechanism; and suitableness criteria with stop search mechanism depending on wither a result is found or not. Testing experience could be exploited for finding a solution to the optimization problem by utilizing Meta heuristic procedures. The presented approach might have been tested for five programs to demonstrate the distinctive tests issues. This paper proposes an automatic test data generation approach that use artificial bee colony algorithm for software structural testing, particularly, path testing. This is brought on moving the centralization of data generation testing, as opposed to the automation of the whole testing operation. It executes artificial bee colony algorithm by creating testing data for the criteria of path coverage testing, and then applying the strategy to a group of test programs.
Highlights
Optimization can be characterized as the best probability search and expected solutions that can be provided to solve a problem
The fitness value is a numerical value that expresses individual quality compared with current local solution in order to search for the optimal least fitness value
Result with lowest fitness value will be the optimal solution from the programs
Summary
Optimization can be characterized as the best probability search and expected solutions that can be provided to solve a problem. There would be number of optimization mechanisms, that are both conventional metaheuristics. Those conventional techniques are gradient based that are exactly quicker over convergence; they are not appropriate for non differentiable and unpredictable multimodal functions. These mechanisms have confinements for finding the global optimal be it get stuck into local optimal value as they begin with only one point. There would be a lot of search techniques to solve this problem but they are slower and need exponential time (Alauddin, 2016).
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