Reliable Automated Software Testing Through Hybrid Optimization Algorithm

Software testing is an important process in product development of software companies to ensure the product quality. The developed application must satisfy the customer needs and meets the industrial standards without any compromise. Thus, verification of products through manual test engineer and validate the product once it meets all the necessary requirements. The issues in manual testing is its high computation and analysis time, accuracy and reliability. In order to reduce the issues in manual testing automatic testing is introduced. Though it is also an application which requires parameters to test the given product. Efficient tuning of the product could be achieved through automatic testing. This proposed research work provides an optimized automatic software testing model through differential evolution and ant colony optimization as a hybrid model to achieve improved accuracy and reliability in software testing. Conventional models like artificial neural network and particle swarm optimization are compared with proposed model to validate the reliability of proposed model.

Review on Software Testing Model Approach for Efficient Bug Finding With Yin-Yang Testing Theory

Model Selection for Software testing is very important prospective in various product accuracy. Through research on software testing model selection, seeking the most appropriate testing method to achieve most reasonable testing volume and optimal testing result. In our Research we will focus on improving this model description by adding more user end experience in acceptance testing. Various experiences from industrial companies will be fetched and will be implemented according to the optimized Yin-Yan Theory for software testing. We will also try to introduce more testing dependencies. In User testing, we will add offline and online dependencies test which will be helpful in finding issues in overall acceptance testing phase. Software testing will be done by various tools and will link to proposed theory. Test model selection and test volume evaluation method will be applied to the software testing work of Industrial applications and will compared with traditional method.

Software Testing Model for Quality

Software testing plays an important role during the software developing period. Any saving on software test will significantly reduce the total cost of software. The key point of this paper is to build a software testing process with the cost control management and to make tradeoffs between the software quality and the software cost. Typical software testing model are analysed and compared. A Quality controlled 3-D testing model is introduced. The model combined with software test process, software quality cost and the testing type. The computational formula is defined to calculate software quality cost. A software quality balance law is introduced to balance control and failure cost. By adjusting software testing method and strategy, organization can reach the objective of the best balance between software quality and the software cost. The result shows an optimistic point that will prove the accurate of the model and the law introduced.

A GENERAL SOFTWARE TESTING MODEL INVOLVING OPERATIONAL PROFILES

An operational profile describes, in a probabilistic way, how a software is utilized by its users. It makes the testing procedure more realistic and efficient. We consider a model where the software is tested sequentially in all of the operations that it is designed to perform. The stochastic and deterministic model parameters involving costs and failures all depend on the operations. In particular, the failure process generated by each fault is quite general and debugging is not necessarily perfect. Our aim is to find the optimal testing durations in all of the operations in order to minimize the total expected cost. This problem leads to an interesting nonlinear programming formulation that can be solved using well-known procedures in convex optimization.