Peer Reviewed Article
Vol. 8 (2021)
Code Refactoring Strategies for Enhancing Robotics Software Maintenance
Software Engineer, Credit Risk, UBS, 1000 Harbor Blvd, Weehawken, NJ 07086, USA
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Submitted
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2 September 2024
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Published
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25-05-2021
Abstract
Code refactoring solutions for robotics software maintenance and optimization are examined in this paper. The critical goal is finding refactoring methods that increase code maintainability, performance, and real-time restrictions in robotics applications. Using secondary data, the research synthesizes the literature on robotics software restructuring, performance improvement, and maintenance difficulties. Research shows modular design, readability enhancements, and algorithmic changes increase program maintainability and performance. More explicit code, better debugging, and enhanced real-time performance are advantages. The report admits constraints, including longer development times and more significant bug risks. According to policy, structured refactoring, automated testing, and industry standards may reduce risks and improve maintenance. By combining these tactics, developers may keep robotics systems resilient, adaptive, and ready for new technology.
References
- Addimulam, S., Mohammed, M. A., Karanam, R. K., Ying, D., Pydipalli, R., Patel, B., Shajahan, M. A., Dhameliya, N., & Natakam, V. M. (2020). Deep Learning-Enhanced Image Segmentation for Medical Diagnostics. Malaysian Journal of Medical and Biological Research, 7(2), 145-152. https://mjmbr.my/index.php/mjmbr/article/view/687
- Ahmad, A., Pahl, C., Altamimi, A. B., Alreshidi, A. (2018). Mining Patterns from Change Logs to Support Reuse-Driven Evolution of Software Architectures. Journal of Computer Science and Technology, 33(6), 1278-1306. https://doi.org/10.1007/s11390-018-1887-3
- Anumandla, S. K. R., Yarlagadda, V. K., Vennapusa, S. C. R., & Kothapalli, K. R. V. (2020). Unveiling the Influence of Artificial Intelligence on Resource Management and Sustainable Development: A Comprehensive Investigation. Technology & Management Review, 5, 45-65. https://upright.pub/index.php/tmr/article/view/145
- Damouche, N., Martel, M., Chapoutot, A. (2017). Improving the Numerical Accuracy of Programs by Automatic Transformation. International Journal on Software Tools for Technology Transfer, 19(4), 427-448. https://doi.org/10.1007/s10009-016-0435-0
- Falotico, E., Vannucci, L., Ambrosano, A., Albanese, U., Ulbrich, S. (2017). Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform. Frontiers in Neurorobotics. https://doi.org/10.3389/fnbot.2017.00002
- Jyothi, V. E., Rao, K. N. (2011). Effective Implementation of Agile Practices - Ingenious and Organized Theoretical Framework. International Journal of Advanced Computer Science and Applications, 2(3). https://doi.org/10.14569/IJACSA.2011.020308
- Karanam, R. K., Natakam, V. M., Boinapalli, N. R., Sridharlakshmi, N. R. B., Allam, A. R., Gade, P. K., Venkata, S. G. N., Kommineni, H. P., & Manikyala, A. (2018). Neural Networks in Algorithmic Trading for Financial Markets. Asian Accounting and Auditing Advancement, 9(1), 115–126. https://4ajournal.com/article/view/95
- Kothapalli, K. R. V. (2019). Enhancing DevOps with Azure Cloud Continuous Integration and Deployment Solutions. Engineering International, 7(2), 179-192.
- Kothapalli, S., Manikyala, A., Kommineni, H. P., Venkata, S. G. N., Gade, P. K., Allam, A. R., Sridharlakshmi, N. R. B., Boinapalli, N. R., Onteddu, A. R., & Kundavaram, R. R. (2019). Code Refactoring Strategies for DevOps: Improving Software Maintainability and Scalability. ABC Research Alert, 7(3), 193–204. https://doi.org/10.18034/ra.v7i3.663
- Laursen, J. S., Ellekilde, L-P., Schultz, U. P. (2018). Modelling Reversible Execution of Robotic Assembly. Robotica, 36(5), 625-654. https://doi.org/10.1017/S0263574717000613
- Mohammed, M. A., Kothapalli, K. R. V., Mohammed, R., Pasam, P., Sachani, D. K., & Richardson, N. (2017a). Machine Learning-Based Real-Time Fraud Detection in Financial Transactions. Asian Accounting and Auditing Advancement, 8(1), 67–76. https://4ajournal.com/article/view/93
- Mohammed, M. A., Mohammed, R., Pasam, P., & Addimulam, S. (2018). Robot-Assisted Quality Control in the United States Rubber Industry: Challenges and Opportunities. ABC Journal of Advanced Research, 7(2), 151-162. https://doi.org/10.18034/abcjar.v7i2.755
- Mohammed, R. & Pasam, P. (2020). Autonomous Drones for Advanced Surveillance and Security Applications in the USA. NEXG AI Review of America, 1(1), 32-53.
- Mohammed, R., Addimulam, S., Mohammed, M. A., Karanam, R. K., Maddula, S. S., Pasam, P., & Natakam, V. M. (2017). Optimizing Web Performance: Front End Development Strategies for the Aviation Sector. International Journal of Reciprocal Symmetry and Theoretical Physics, 4, 38-45. https://upright.pub/index.php/ijrstp/article/view/142
- Nizamuddin, M., Natakam, V. M., Sachani, D. K., Vennapusa, S. C. R., Addimulam, S., & Mullangi, K. (2019). The Paradox of Retail Automation: How Self-Checkout Convenience Contrasts with Loyalty to Human Cashiers. Asian Journal of Humanity, Art and Literature, 6(2), 219-232. https://doi.org/10.18034/ajhal.v6i2.751
- Pissanetzky, S., Lanzalaco, F. (2013). Black-box Brain Experiments, Causal Mathematical Logic, and the Thermodynamics of Intelligence. Journal of Artificial General Intelligence, 4(3), 10-43. https://doi.org/10.2478/jagi-2013-0005
- Roberts, C., Kundavaram, R. R., Onteddu, A. R., Kothapalli, S., Tuli, F. A., Miah, M. S. (2020). Chatbots and Virtual Assistants in HRM: Exploring Their Role in Employee Engagement and Support. NEXG AI Review of America, 1(1), 16-31.
- Rodriguez, M., Mohammed, M. A., Mohammed, R., Pasam, P., Karanam, R. K., Vennapusa, S. C. R., & Boinapalli, N. R. (2019). Oracle EBS and Digital Transformation: Aligning Technology with Business Goals. Technology & Management Review, 4, 49-63. https://upright.pub/index.php/tmr/article/view/151
- Rodríguez-Gracia, D., Piedra-Fernández, J. A., Iribarne, L., Criado, J., Ayala, R. (2019). Microservices and Machine Learning Algorithms for Adaptive Green Buildings. Sustainability, 11(16), 4320. https://doi.org/10.3390/su11164320
- Sun, Y., Gray, J., White, J. (2015). A Demonstration-based Model Transformation Approach to Automate Model Scalability. Software and Systems Modeling, 14(3), 1245-1271. https://doi.org/10.1007/s10270-013-0374-0
- White, A. (2014). An Agile Project System Dynamics Simulation Model. International Journal of Information Technologies and Systems Approach, 7(1), 55-79. https://doi.org/10.4018/ijitsa.2014010104
- Wielgosz, M., Karwatowski, M. (2019). Mapping Neural Networks to FPGA-Based IoT Devices for Ultra-Low Latency Processing. Sensors, 19(13). https://doi.org/10.3390/s19132981
- Ying, D., Kothapalli, K. R. V., Mohammed, M. A., Mohammed, R., & Pasam, P. (2018). Building Secure and Scalable Applications on Azure Cloud: Design Principles and Architectures. Technology & Management Review, 3, 63-76. https://upright.pub/index.php/tmr/article/view/149
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