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
- A. Anand, A. Nisha Jebaseeli, A comparative analysis of virtual machines and containers using queuing models , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- Christina Parmar, Dipak Makwana, Nita Vaghela, Professional Social Work Interventions in Healthcare: Safeguarding Patient Rights and Strengthening Grievance Redressal Systems , The Scientific Temper: Vol. 17 No. 03 (2026): The Scientific Temper
- Vijay Shankar Pandey, Abdul Kalam, PATHOGENICITY OF THE BACTERIAL ISOLATE AEROMONAS HYDROPHILA TO FISHES IN LABORATORY CONDITIONS , The Scientific Temper: Vol. 8 No. 1&2 (2017): The Scientific Temper
- Surender Singh, Rachna Thakur, Suchitra Devi, Globalization and Indian Negotiation on Agriculture , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- S Prabhakaran, Yugeshkrishnan M, Santhiya M, Danush Kumar S M, Smart Dustbin using IOT , The Scientific Temper: Vol. 14 No. 02 (2023): The Scientific Temper
- Tursunova N. Isroilovna, Dilbar M. Almuradova, Orifjon A. Talipov, Features of diagnosing ovarian tumors in women of pre- and postmenopausal age , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- P. L. Parmar, P. M. George, Study and optimization of process parameters for deformation machining stretching mode , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Monalisha Paul, Chaitali Kundu, Rudranil Bhowmik, Sanmoy Karmakar, Sandip K. Sinha, Nilanjana Chatterjee, The potential impression of fructo-oligosaccharides and zinc oxide nano composite against nicotine influenced cardiovascular changes , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
- K. S. Deepika, Ajay Massand, Influence of Social Media Marketing on Purchase Intention of Gen Z , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- M. Jayakandan, A. Chandrabose, An ensemble-based approach for sentiment analysis of covid-19 Twitter data using machine learning and deep learning techniques , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
<< < 51 52 53 54 55 56 57 58 59 > >>
You may also start an advanced similarity search for this article.

