Integrating SQA into the Robotic Software Development Lifecycle
DOI:
https://doi.org/10.18034/abcjar.v12i1.763Keywords:
Software Quality Assurance (SQA), Robotic Software, Development Lifecycle, Automation, Testing Strategies, Software Reliability, Quality ManagementAbstract
Software Quality Assurance (SQA) is integrated into the robotic software development lifecycle to improve robotic system dependability, safety, and performance in this research. The main goals are finding gaps in existing SQA procedures, presenting a specialized SQA integration architecture, and solving robotics difficulties, including hardware-software Integration, real-time processing, and machine learning validation; the research evaluates current SQA methodologies and proposes changes using secondary data from the literature, industry reports, and technical publications. Due to their intricate interconnections, hardware-in-the-loop (HIL) testing, real-time performance assessments, and automated Testing are crucial to the robotic system SQA. The report also notes resource requirements for extensive testing and simulation fidelity. Policy implications include standardizing testing techniques, investing in new simulation technology, and establishing safety and compliance regulations. The suggested paradigm addresses these difficulties to help design more dependable and competent robotic systems, improving robotics and its applications.
Metrics
Downloads
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
Ahmed, Z. (2015). Essential Design Modeling for Scientific Software Development. PeerJ PrePrints. https://doi.org/10.7287/peerj.preprints.1423v1 DOI: https://doi.org/10.7287/peerj.preprints.1423
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
Deming, C., Pasam, P., Allam, A. R., Mohammed, R., Venkata, S. G. N., & Kothapalli, K. R. V. (2021). Real-Time Scheduling for Energy Optimization: Smart Grid Integration with Renewable Energy. Asia Pacific Journal of Energy and Environment, 8(2), 77-88. https://doi.org/10.18034/apjee.v8i2.762 DOI: https://doi.org/10.18034/apjee.v8i2.762
Deniz, C., Cakir, M. (2018). In-line Stereo-camera Assisted Robotic Spot Welding Quality Control System. The Industrial Robot, 45(1), 54-63. https://doi.org/10.1108/IR-06-2017-0117 DOI: https://doi.org/10.1108/IR-06-2017-0117
Fadziso, T., Mohammed, R., Kothapalli, K. R. V., Mohammed, M. A., Karanam, R. K. (2022). Deep Learning Approaches for Signal and Image Processing: State-of-the-Art and Future Directions. Silicon Valley Tech Review, 1(1), 14-34.
Gómez-Sanz, J. J., Fuentes-Fernández, R. (2015). Understanding Agent-Oriented Software Engineering Methodologies. The Knowledge Engineering Review, suppl. Challenges in Agent-Oriented Software Engineering, 30(4), 375-393. https://doi.org/10.1017/S0269888915000053 DOI: https://doi.org/10.1017/S0269888915000053
Gresse von Wangenheim, C., von Wangenheim, A., McCaffery, F., Hauck, J. C. R., Buglione, L. (2013). Tailoring Software Process Capability/maturity Models for the Health Domain. Health and Technology, 3(1), 11-28. https://doi.org/10.1007/s12553-013-0038-7 DOI: https://doi.org/10.1007/s12553-013-0038-7
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
Kazadzis, S., Kouremeti, N., Nyeki, S., Gröbner, J., Wehrli, C. (2018). The World Optical Depth Research and Calibration Center (WORCC) Quality Assurance and Quality Control of GAW-PFR AOD Measurements. Geoscientific Instrumentation, Methods and Data Systems, 7(1), 39-53. https://doi.org/10.5194/gi-7-39-2018 DOI: https://doi.org/10.5194/gi-7-39-2018
Kothapalli, K. R. V. (2019). Enhancing DevOps with Azure Cloud Continuous Integration and Deployment Solutions. Engineering International, 7(2), 179-192. DOI: https://doi.org/10.18034/ei.v7i2.721
Kothapalli, K. R. V. (2022). Exploring the Impact of Digital Transformation on Business Operations and Customer Experience. Global Disclosure of Economics and Business, 11(2), 103-114. https://doi.org/10.18034/gdeb.v11i2.760 DOI: https://doi.org/10.18034/gdeb.v11i2.760
Kothapalli, K. R. V., Tejani, J. G., Rajani Pydipalli, R. (2021). Artificial Intelligence for Microbial Rubber Modification: Bridging IT and Biotechnology. Journal of Fareast International University, 4(1), 7-16.
Kumudha, P., Venkatesan, R. (2016). Cost-Sensitive Radial Basis Function Neural Network Classifier for Software Defect Prediction. The Scientific World Journal, 2016. https://doi.org/10.1155/2016/2401496 DOI: https://doi.org/10.1155/2016/2401496
Mohammed, M. A., Kothapalli, K. R. V., Mohammed, R., Pasam, P., Sachani, D. K., & Richardson, N. (2017). 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 DOI: 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. (2021). Code Refactoring Strategies for Enhancing Robotics Software Maintenance. International Journal of Reciprocal Symmetry and Theoretical Physics, 8, 41-50. https://upright.pub/index.php/ijrstp/article/view/152
Mohammed, R. (2022). Artificial Intelligence-Driven Robotics for Autonomous Vehicle Navigation and Safety. NEXG AI Review of America, 3(1), 21-47.
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
Mohan, M., Shrimali, T. (2017). Hybrid Data Approach For Selecting Effective Test Cases During The Regression Testing. International Journal on Smart Sensing and Intelligent Systems, 10(5), 1-24. https://doi.org/10.21307/ijssis-2017-233 DOI: https://doi.org/10.21307/ijssis-2017-233
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 DOI: https://doi.org/10.18034/ajhal.v6i2.751
Rana, S., Bennouna, J., Jebaseelan Samuel, E. J., Gutierrez, A. N. (2019). Development and Long-term Stability of a Comprehensive Daily QA Program for a Modern Pencil Beam Scanning ( PBS ) Proton Therapy Delivery System. Journal of Applied Clinical Medical Physics, 20(4), 29-44. https://doi.org/10.1002/acm2.12556 DOI: https://doi.org/10.1002/acm2.12556
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
Stetter, R., Simundsson, A. (2015). Control and Diagnosis in Integrated Product Development - Observations during the Development of an AGV. Journal of Physics: Conference Series, 659(1). https://doi.org/10.1088/1742-6596/659/1/012056 DOI: https://doi.org/10.1088/1742-6596/659/1/012056
Wang, X., Yan, H., Li, J. (2018). An Improved Supervised Learning Defect Prediction Model Based on Cat Swarm Algorithm. Journal of Physics: Conference Series, 1087(2). https://doi.org/10.1088/1742-6596/1087/2/022005 DOI: https://doi.org/10.1088/1742-6596/1087/2/022005
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
Ying, D., Pasam, P., Addimulam, S., & Natakam, V. M. (2022). The Role of Polymer Blends in Enhancing the Properties of Recycled Rubber. ABC Journal of Advanced Research, 11(2), 115-126. https://doi.org/10.18034/abcjar.v11i2.757 DOI: https://doi.org/10.18034/abcjar.v11i2.757
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Rahimoddin Mohammed
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
