Software Quality Evaluation by Cocomo II With NN and SVM

Cost, time and quality projection are the crucial aspects in software development process. Incorrect estimations can cause losses which in turn may lead to irreversible damage. It is generally perceived that a imperfectly estimated project always results in a substandard quality due to the efforts being wrongly directed. Firstly Effort Estimation is calculated by actual effort and proposed Effort. That Effort evaluation of 500 NASA projects, after that evaluation is done by four parameters Standard Error, Standard Deviation, Mean Absolute Error, Root Mean Square Error. The author amalgamated the robustness of COCOMO-II with that of Neural Network NN and Support Vector Machine SVM .Quality Which we evaluate that is quality Evaluation of Semantic Web Application. In the last checks the majority of all four parameters for software quality assessment.

Novel Similarity Measures, Entropy of Intuitionistic Fuzzy Sets and their Application in Software Quality Evaluation

Intuitionistic fuzzy sets (IFSs), including member and nonmember functions, have many applications in managing uncertain information. The similarity measures of IFSs proposed to represent the similarity between different types of sensitive fuzzy information. However, some existing similarity measures do not meet the axioms of similarity. Moreover, in some cases, they could not be applied appropriately. In this study, we proposed some novel similarity measures of IFSs constructed by combining the exponential function of membership functions and the negative function of non-membership functions. In this paper, we also proposed a new entropy measure as a stepping stone to calculate the weights of the criteria in the proposed multi-criteria decision making (MCDM) model. The similarity measures used to rank alternatives in the model. Finally, we used this MCDM model to evaluate the quality of software projects.