1. “Literature Review of Nature-Inspired Computing Based Search & Optimization Approaches”
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I’m Anik Acharjee, a seasoned professional with over 7 years of hands-on experience in the dynamic realms of technical training and web development. My journey has been defined by a fervent passion for technology and a commitment to empowering others with the knowledge to thrive in the digital landscape. In my role, I’ve had the privilege of shaping the next generation of tech enthusiasts. I specialize in translating complex concepts into digestible, actionable insights. Whether it’s diving into programming languages, exploring the intricacies of web development, or unraveling the mysteries of emerging technologies, my training sessions are tailored to foster a deep understanding and hands-on proficiency. In the ever-evolving tech landscape, I firmly believe in the power of continuous learning. I stay abreast of the latest industry trends, ensuring that my training content and development projects reflect cutting-edge best practices.
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Published in the International Journal of Research in Electronics and Computer Engineering (IJRECE) in 2018.
This comprehensive literature review provides a critical analysis of nature-inspired computing-based search and optimization approaches. By synthesizing existing literature and identifying key trends and advancements, the study offers valuable insights into the theoretical foundations and practical applications of nature-inspired algorithms in optimization problems.
Published in the Journal of the Gujarat Research Society in 2019.
Building upon previous research, this paper presents an automated approach to test case generation from Unified Modeling Language (UML) diagrams. By automating the translation of UML representations into executable test cases, the study contributes to streamlining the software testing life cycle and reducing manual effort.
Published in the Journal of Emerging Technologies and Innovative Research (JETIR) in 2018.
This research extends the investigation into automated test case generation techniques, focusing on their application within the broader context of software testing. By leveraging automated algorithms and methodologies, the study aims to enhance test coverage and accuracy while minimizing human intervention.
Published in the Journal of Emerging Technologies and Innovative Research (JETIR) in 2018.
This seminal work investigates the application of the artificial bee colony algorithm in the domain of software testing. By harnessing the collective intelligence of artificial bees, the study aims to optimize test case generation processes, improving the efficiency and effectiveness of software testing methodologies.
Published in the Think India Journal in 2019.
Drawing upon the findings of previous research, this paper presents a comparative analysis of various test case generation techniques. By evaluating the strengths and limitations of different methodologies, the study aims to inform practitioners and researchers about the most effective approaches for test case generation in software testing environments.
Issued by Advances in Science, Technology and Engineering Systems Journal (ASTESJ) · May 2020
Issued by International Journal of Swarm Intelligence Research (IJSIR) · Sep 2019
Issued by 4th International Conference on Computing Sciences (ICCS) - Feynman - 100 · Aug 2018
