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
- G. Hemamalini, V. Maniraj, Enhanced otpmization based support vector machine classification approach for the detection of knee arthritis , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- M. Ragul, A. Aloysius, V. Arul Kumar, Enhancing IoT blockchain scalability through the eepos consensus algorithm , The Scientific Temper: Vol. 16 No. 02 (2025): The Scientific Temper
- Thangatharani T, M. Subalakshmi, Development of an adaptive machine learning framework for real-time anomaly detection in cybersecurity , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- M. Prabhu, A. Chandrabose, Optimization based energy aware scheduling in wireless sensor networks , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Engida Admassu, Classifying enset based on their disease tolerance using deep learning , The Scientific Temper: Vol. 14 No. 03 (2023): The Scientific Temper
- A. Basheer Ahamed, M. Mohamed Surputheen, M. Rajakumar, Quantitative transfer learning- based students sports interest prediction using deep spectral multi-perceptron neural network , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- M. Menaha, J. Lavanya, Crop yield prediction in diverse environmental conditions using ensemble learning , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- Aarthi Monalisa M, Anli Suresh, Adoptive bancassurance models transforming patronization among the insured , The Scientific Temper: Vol. 16 No. Spl-2 (2025): The Scientific Temper
- Jayalakshmi K., M. Prabakaran, Feature selection in HR analytics: A hybrid optimization approach with PSO and GSO , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Santhanalakshmi M, Ms Lakshana K, Ms Shahitya G M, Enhanced AES-256 cipher round algorithm for IoT applications , The Scientific Temper: Vol. 14 No. 01 (2023): The Scientific Temper
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.

