Modified-multi objective firefly optimization algorithm for object oriented applications test suites optimization
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2023.14.3.44Keywords:
Firefly algorithm, Light intensity, Model-based testing, Multi-objective test suites optimizationDimensions Badge
Issue
Section
License
Copyright (c) 2023 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Model-based testing is a crucial but challenging stage of the software development process. The process of model-based testing needsto be optimized, which is a difficult task. In this article, we present an approach for selecting minimum test suites that is based on themeta-heuristic firefly algorithm. We modify the firefly algorithm and define the suitable multi-objective function to optimize the testsuites. The suggested approach uses firefly behavior to address the current issue. The modified approach chooses the best test suitesthat quickly find the maximum coverage in less time.Abstract
How to Cite
Downloads
Similar Articles
- Merlin Sofia S, D. Ravindran, G. Arockia Sahaya Sheela, Clean Balance-Ensemble CHD: A Balanced Ensemble Learning Framework for Accurate Coronary Heart Disease Prediction , The Scientific Temper: Vol. 16 No. 10 (2025): The Scientific Temper
- Somalee Mahapatra, Manoranjan Dash, Subhashis Mohanty, Adoption of artificial intelligence and the internet of things in dental biomedical waste management , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Nithya R, Kokilavani T, Joseph Charles P, Multi-objective nature inspired hybrid optimization algorithm to improve prediction accuracy on imbalance medical datasets , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Rama Shankar Dubey, M.A. Naidu, Ajay Kumar Shukla, Awadhesh Kumar Shukla, Manish Kumar, Sonia Verma, Pramod Kumar Mourya, Application of Bioactive Molecules in the Treatment and Management of Type-1 Diabetic Disease , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- Akshay J., G. Mahesh Kumar, B. H. Manjunath, Optimizing durability of the thin white topping applying Taguchi method using desirability function , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Purnendu B. Acharjee, Bhupaesh Ghai, Muniyandy Elangovan, S. Bhuvaneshwari, Ravi Rastogi, P. Rajkumar, Exploring AI-driven approaches to drug discovery and development , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- S. Nagarani, Amalraj P., Lakshay Phor, Nishank S. Pimple, Banashree Sen, Ramaprasad Maiti, Vikas S. Jadhav, Innovative technological advancements in solving real quadratic equations: Pioneering the frontier of mathematical innovation , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Amit Maru, Dhaval Vyas, Hybrid deep learning approach for pre-flood and post-flood classification of remote sensed data , The Scientific Temper: Vol. 16 No. Spl-1 (2025): The Scientific Temper
- Ayalew Ali, Sitotaw Wodajio, The effect of risk management on the bank’s financial stability in the emerging economy , The Scientific Temper: Vol. 16 No. 04 (2025): The Scientific Temper
- Merla Agnes Mary, Britto Ramesh Kumar, Hybrid GAN with KNN - SMOTE Approach for Class-Imbalance in Non-Invasive Fetal ECG Monitoring , The Scientific Temper: Vol. 16 No. 09 (2025): The Scientific Temper
<< < 12 13 14 15 16 17 18 19 20 21 > >>
You may also start an advanced similarity search for this article.

