Performance Analysis of Software Test Effort Estimation using Genetic Algorithm and Neural Network

In present scenario, the software companies are frequently involving test effort estimation to allocate resources efficiently during development process. Different machine learning models developed estimate total that would be required before product could delivered. These computational used use past data efforts. current studies, for is predicted using Genetic algorithm and Neural Network. The attributes selected similarity measure between attribute values has been computed Cosine Similarity measure. simulation experiments were done PROMISE Kaggle repository implementation was MATLAB software. performance metrics namely, precision, recall, accuracy evaluate against existing techniques. of proposed model 91.3% results improved by 8.9% in comparison technique made superiority predict development.