Test Effort Estimation is an important activity in software development. The test effort can be calculated on the basis of effort cost and time required for testing. Several studies have been done for developing test effort estimation models but to some extent only, most of these models result in erroneous results. So there is a strong need to optimize the efforts estimated. Meta heuristic techniques can be used for this purpose, to optimize a problem by iteratively trying to improve a solution, using some computational methods. In this paper, we have implemented meta-heuristic based search algorithm namely PSO. The particle swarm optimization algorithm is used for improving testing effort estimation. The particle swarm optimization algorithm (PSO) is applied on use case point (UCP) and results led us to the conclusion that test effort estimation can be optimized by applying PSO. The PSO optimization can also be applied for estimating efforts of software development. This implementation increases the accuracy of testing effort estimation. KeywordsSoftware testing, particle swarm optimization (PSO) and use case point (UCP).

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Software testing is the process of evaluating a system or its modules in the intent to find that the software is acquiring the efficient requirements or not. In simple words testing is the execution of the system in order to find their gaps, ambiguity and inconsistency. Software testing comprises into three factors: test case generation, test cases execution and test cases evaluation. This paper implemented a semantic approach for the generation of test cases on UML model i.e., Activity Diagram. In this approach an Activity diagram is created then it automatically generated a Activity Dependency table (ADT) from Activity diagram. From the ADT an Activity Dependency Graph (ADG) is introduced. Finally After the automatic generation of ADG a consistent test case are generated. This approach includes the validation of the test cases by their consistency and efficiency. This approach saves the cost, time, efforts and increases the quality of generated test cases.