Open AccessSoftware Quality Evaluation by Cocomo II With NN and SVM
Author(s) -
Sandip Kumar Goyal Naveen Malik
Publication year - 2022
Publication title -
the philippine statistician (quezon city)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.101
H-Index - 2
ISSN - 2094-0343
DOI - 10.17762/msea.v71i1.34
Subject(s) - cocomo , computer science , robustness (evolution) , support vector machine , software , mean squared error , quality (philosophy) , software quality , artificial neural network , standard deviation , data mining , reliability engineering , machine learning , artificial intelligence , statistics , software development , engineering , mathematics , software construction , biochemistry , chemistry , philosophy , epistemology , gene , programming language
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.