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
- Panda Aditi Ambarish, Kaushik Trivedi, Immersive learning: A virtual reality teaching model for enhancing english speaking skills , The Scientific Temper: Vol. 15 No. spl-2 (2024): The Scientific Temper
- Rita Ganguly, Dharmpal Singh, Rajesh Bose, The next frontier of explainable artificial intelligence (XAI) in healthcare services: A study on PIMA diabetes dataset , The Scientific Temper: Vol. 16 No. 05 (2025): The Scientific Temper
- A. Jabeen, A. R. M. Shanavas, Hazard regressive multipoint elitist spiral search optimization for resource efficient task scheduling in cloud computing , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Vinodini R, Ritha W, Sasitharan Nagapan, An inventory model on the impact of green investment with deteriorating items and planned back orders for economic efficiency and environmental sustainability , The Scientific Temper: Vol. 16 No. 08 (2025): The Scientific Temper
- Sreenath M.V. Reddy, D. Annapurna, Anand Narasimhamurthy, Influence node analysis based on neighborhood influence vote rank method in social network , The Scientific Temper: Vol. 14 No. 04 (2023): The Scientific Temper
- Roopesh K R, Jyothi Y, Manisha Bihani, Chandini C H, Nishanth D R, Maheshkumar Hondale, Sairashmi Samanta, Karthik G, Anu M, Neuroprotective effect of alcoholic extract of Selaginella bryopteris leaves in experimental models of epilepsy , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- G. Vijayalakshmi, M. V. Srinath, Student’s Academic Performance Improvement Using Adaptive Ensemble Learning Method , The Scientific Temper: Vol. 16 No. 11 (2025): The Scientific Temper
- A. Kalaiselvi, A. Chandrabose, Fuzzy logic-driven scheduling for cloud computing operations: a dynamic and adaptive approach , The Scientific Temper: Vol. 15 No. spl-1 (2024): The Scientific Temper
- G. Tripathi, R. Deora, FAUNA – ASSISTED LITTER DECOMPOSITION AND ITS IMPACT ON CHEMICAL AND BIOLOGICAL HEALTH OF BALANITES AEGYPTIACA BASED SILVIPASTURE SYSTEM , The Scientific Temper: Vol. 1 No. 01 (2010): The Scientific Temper
- R. Kalaiselvi, P. Meenakshi Sundaram, Machine learning-based ERA model for detecting Sybil attacks on mobile ad hoc networks , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
<< < 17 18 19 20 21 22 23 24 25 26 > >>
You may also start an advanced similarity search for this article.

