Intrinsic Feature Sensitive Software Reusability Prediction Using Parallelized Feature Selection and Heterogeneous Ensemble Learning Method
Year: 2025
Type: article
Abstract: The rising global competitiveness in information technology industry has triggered industry to achieve and contribute efficient software solution even at the lower cost. With such a demand, the use of free open source software (FOSS) and component-reusability have become common in software industry. Though, software component reusability can be advantageous; improper reuse might even cause fault, smell, failure and eventual massive losses. Identifying a class with no-reusability, a developer can avoid it to inculcate in program that can improve software reliability. Unlike manual testing methods, which are often criticized for high cost and time-consumption, we propose a novel machine learning based automatic software reusability prediction that predicts each class as REUSABLE or NON-REUSABLE.
Author: Prakash V. Parande
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408124478