Chiang Mai Journal of Science

Paper Type 

Title 

Author 

Email 

Software reusability is primary attribute of software quality. There are metrics for identifying the quality of reusable components but the function that makes use of these metrics to find reusability of software components is still not clear. These metrics if identified in the design phase or even in the coding phase can help us to reduce the rework by improving quality of reuse of the component and hence improve the productivity due to probabilistic increase in the reuse level. We have taken the dataset and framework of metrics, that make use of McCabe’s Cyclometric Complexity Measure for Complexity measurement, Regularity Metric, Halstead Software Science Indicator for Volume indication, Reuse Frequency metric and Coupling Metric values of the software component as input attributes and calculated reusability of the software component. In this work, Particle Swarm Optimization technique along with the four variants of Conjugate Gradient Algorithms is empirically explored to train a feed forward neural network for reusability dataset. The performance of the trained neural networks is tested to evaluate the reusability level of the procedure based software systems. The results are recorded in terms of Accuracy, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Keywords: software reusability, particle swarm optimization, neural network.

Start & End Page 

Received Date 

Revised Date 

Accepted Date 

Full Text 

Keyword 

Volume 

DOI 

Citation 

SDGs