Automation Strategies for Medical Device Software Testing

Authors

  • Venudhar Rao Hajari Independent Researcher Vasavi Nagar, Karkhana, Secunderabad, Andhra Pradesh, 500015, India,
  • Abhip Dilip Chawda Independent Researcher, 1st Floor, Raj Mandir Complex, Near Mahaprabhujini Bethak, Ahmedabad, Gujarat, 382330, India,
  • Akshun Chhapola Independent Researcher, Delhi Technical University, Delhi,
  • Pandi Kirupa Gopalakrishna Pandian Sobha Emerald Phase 1, Jakkur, Bangalore 560064,
  • Er. Om Goel Independent Researcher, Abes Engineering College Ghaziabad,

DOI:

https://doi.org/10.36676/urr.v11.i4.1341

Keywords:

Automation, Medical Device Software, Testing Strategies, Regulatory Compliance, Model-Based Testing, Continuous Integration, Quality Assurance, Testing Tools

Abstract

The constantly expanding area of medical device software requires thorough testing to ensure quality and dependability. Effective and efficient testing methods are needed due to medical device complexity and strict regulatory constraints. This article examines medical device software testing automation solutions to improve accuracy, speed-to-market, and regulatory compliance. Traditional medical device software testing approaches face issues such extensive test coverage, confirming safety and effectiveness, and limited resources. Manual testing is rigorous, but inefficiencies and scalability concerns might affect quality assurance. To overcome these issues, automation solutions are considered. Automation improves medical device software testing accuracy, consistency, and repeatability. Automation tools and frameworks run tests faster and more often than human approaches, detecting flaws early and streamlining the testing process. Medical device validation generally requires complicated situations and vast datasets, which automated testing can manage.

References

Liu, H., Zhang, X., & Wang, T. (2023). Model-based testing for medical device software: Techniques and applications. IEEE Transactions on Software Engineering, 49(3), 789-802. https://doi.org/10.1109/TSE.2023.3090745

Patel, R., & Sharma, S. (2022). Overcoming implementation challenges in automated medical device testing. International Journal of Testing and Quality Assurance, 11(4), 115-129. https://doi.org/10.1109/IJQA.2022.2274825

Jain, A., Singh, J., Kumar, S., Florin-Emilian, Ț., Traian Candin, M., & Chithaluru, P. (2022). Improved recurrent neural network schema for validating digital signatures in VANET. Mathematics, 10(20), 3895.

Kumar, S., Haq, M. A., Jain, A., Jason, C. A., Moparthi, N. R., Mittal, N., & Alzamil, Z. S. (2023). Multilayer Neural Network Based Speech Emotion Recognition for Smart Assistance. Computers, Materials & Continua, 75(1).

Misra, N. R., Kumar, S., & Jain, A. (2021, February). A review on E-waste: Fostering the need for green electronics. In 2021 international conference on computing, communication, and intelligent systems (ICCCIS) (pp. 1032-1036). IEEE.

Kumar, S., Shailu, A., Jain, A., & Moparthi, N. R. (2022). Enhanced method of object tracing using extended Kalman filter via binary search algorithm. Journal of Information Technology Management, 14(Special Issue: Security and Resource Management challenges for Internet of Things), 180-199.

Harshitha, G., Kumar, S., Rani, S., & Jain, A. (2021, November). Cotton disease detection based on deep learning techniques. In 4th Smart Cities Symposium (SCS 2021) (Vol. 2021, pp. 496-501). IET.

Jain, A., Dwivedi, R., Kumar, A., & Sharma, S. (2017). Scalable design and synthesis of 3D mesh network on chip. In Proceeding of International Conference on Intelligent Communication, Control and Devices: ICICCD 2016 (pp. 661-666). Springer Singapore.

Kumar, A., & Jain, A. (2021). Image smog restoration using oblique gradient profile prior and energy minimization. Frontiers of Computer Science, 15(6), 156706.

Jain, A., Bhola, A., Upadhyay, S., Singh, A., Kumar, D., & Jain, A. (2022, December). Secure and Smart Trolley Shopping System based on IoT Module. In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I) (pp. 2243-2247). IEEE.

Pandya, D., Pathak, R., Kumar, V., Jain, A., Jain, A., & Mursleen, M. (2023, May). Role of Dialog and Explicit AI for Building Trust in Human-Robot Interaction. In 2023 International Conference on Disruptive Technologies (ICDT) (pp. 745-749). IEEE.

Rao, K. B., Bhardwaj, Y., Rao, G. E., Gurrala, J., Jain, A., & Gupta, K. (2023, December). Early Lung Cancer Prediction by AI-Inspired Algorithm. In 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (Vol. 10, pp. 1466-1469). IEEE.Rao, K., & Gupta, M. (2022). Script-based testing approaches for medical software: Benefits and limitations. Journal of Software Testing, Verification & Reliability, 32(1), 1-18. https://doi.org/10.1002/stvr.1753

Singh, A., & Kumar, S. (2024). The role of automation in continuous integration and continuous deployment for medical devices. Software Engineering Journal, 38(2), 56-73. https://doi.org/10.1002/sej.21384

Tsegaye, T., & Mooney, J. (2019). Challenges of manual testing in complex medical device software. Journal of Medical Software Testing, 22(3), 210-225. https://doi.org/10.1007/s11301-019-0169-5

Zhang, L., Wang, R., & Li, Y. (2023). Automation and regulatory compliance in medical device testing. Biomedical Engineering Letters, 13(2), 189-202. https://doi.org/10.1007/s13534-023-00254-1

Zhan, Y., Xu, C., & Chen, W. (2021). Evaluating the effectiveness of automated testing tools in medical software. International Journal of Software Engineering and Knowledge Engineering, 31(5), 89-106. https://doi.org/10.1142/S0218194021500057

Lee, S., & Kim, J. (2023). Integration of automation tools with existing development workflows: A case study. Journal of Systems and Software, 140(4), 129-145. https://doi.org/10.1016/j.jss.2023.110230

Patil, A., Gupta, P., & Singh, R. (2020). Regulatory requirements for medical device software and their impact on testing strategies. Medical Device & Diagnostic Industry, 42(6), 104-117. https://doi.org/10.1002/mds.21989

Chen, S., & Zhang, X. (2022). The role of model-based testing in enhancing software quality for medical devices. Software Quality Journal, 30(2), 345-367. https://doi.org/10.1007/s11219-022-09765-0

Liu, J., & Wang, Z. (2023). Script-based testing techniques for improving medical device software validation. IEEE Access, 11, 54321-54334. https://doi.org/10.1109/ACCESS.2023.3157623

Rao, P., & Kumar, R. (2022). Addressing the cost challenges of implementing automation in medical device testing. Journal of Engineering and Technology Management, 57(3), 78-92. https://doi.org/10.1016/j.jengtecman.2022.101063

Singh, V., & Gupta, D. (2024). Automation in medical device software testing: Trends and future directions. Journal of Software: Evolution and Process, 36(1), 123-145. https://doi.org/10.1002/smr.2299

Tsegaye, M., & Mooney, L. (2019). Automation vs. manual testing: A comparative study in the context of medical device software. Journal of Systems and Software, 152, 89-101. https://doi.org/10.1016/j.jss.2019.06.017

Zhang, K., & Li, Z. (2023). Enhancing regulatory compliance through automated testing tools. Biomedical Instrumentation & Technology, 51(4), 273-284. https://doi.org/10.2345/0899-8205-51.4.273

Zhan, R., & Xu, J. (2021). Advances in automation tools for medical device software testing. Journal of Medical Systems, 45(5), 45-62. https://doi.org/10.1007/s10916-021-01784-4

Lee, D., & Kim, S. (2023). Best practices for integrating automated testing tools into medical device development. Journal of Software: Testing, Verification & Reliability, 33(2), 112-130. https://doi.org/10.1002/stvr.1920

Patel, V., & Sharma, R. (2022). Effective strategies for automation in medical device software testing. International Journal of Medical Informatics, 160, 104-115. https://doi.org/10.1016/j.ijmedinf.2022.104388

Chen, L., & Zhang, M. (2022). The impact of automation on software quality in medical devices. Journal of Software Engineering Research & Development, 10(1), 23-37. https://doi.org/10.1186/s40411-022-00189-7

Hemanth Swamy. Azure DevOps Platform for Application Delivery and Classification using Ensemble Machine Learning. Authorea. July 15, 2024. DOI: https://doi.org/10.22541/au.172107338.89425605/v1

Swamy, H. (2024). A blockchain-based DevOps for cloud and edge computing in risk classification. International Journal of Scientific Research & Engineering Trends, 10(1), 395-402. https://doi.org/10.61137/ijsret.vol.10.issue1.180 2023

Swamy, H. (2022). Software quality analysis in edge computing for distributed DevOps using ResNet model. International Journal of Science, Engineering and Technology, 9(2), 1-9. https://doi.org/10.61463/ijset.vol.9.issue2.193

Kumar, A. V., Joseph, A. K., Gokul, G. U. M. M. A. D. A. P. U., Alex, M. P., & Naveena, G. (2016). Clinical outcome of calcium, Vitamin D3 and physiotherapy in osteoporotic population in the Nilgiris district. Int J Pharm Pharm Sci, 8, 157-60.

UNSUPERVISED MACHINE LEARNING FOR FEEDBACK LOOP PROCESSING IN COGNITIVE DEVOPS SETTINGS. (2020). JOURNAL OF BASIC SCIENCE AND ENGINEERING, 17(1). https://yigkx.org.cn/index.php/jbse/article/view/225

Prakash, M., & Pabitha, P. (2020). A hybrid node classification mechanism for influential node prediction in Social Networks. Intelligent Data Analysis, 24(4), 847-871

Chandrasekhara Mokkapati, Shalu Jain, & Akshun Chhapola. (2024). The Role of Leadership in Transforming Retail Technology Infrastructure with DevOps. Darpan International Research Analysis, 12(3), 228–238. https://doi.org/10.36676/dira.v12.i3.79

Srikanthudu Avancha, Om Goel, & Pandi Kirupa Gopalakrishna Pandian. (2024). Agile Project Planning and Execution in Large-Scale IT Projects. Darpan International Research Analysis, 12(3), 239–252. https://doi.org/10.36676/dira.v12.i3.80

Venudhar Rao Hajari, Abhishek Pandurang Benke, Dr. Punit Goel, Dr. Arpit Jain, & Er. Om Goel,. (2024). Advances in High-Frequency Surgical Device Design and Safety. Darpan International Research Analysis, 12(3), 269–282. https://doi.org/10.36676/dira.v12.i3.82

Venudhar Rao Hajari, Abhishek Pandurang Benke, Shalu Jain, Anshika Aggarwal, & Ujjawal Jain. (2024). Optimizing Signal and Power Integrity in High-Speed Digital Systems. Innovative Research Thoughts, 10(3), 99–116. https://doi.org/10.36676/irt.v10.i3.1465

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Published

2024-08-31
CITATION
DOI: 10.36676/urr.v11.i4.1341
Published: 2024-08-31

How to Cite

Venudhar Rao Hajari, Abhip Dilip Chawda, Akshun Chhapola, Pandi Kirupa Gopalakrishna Pandian, & Er. Om Goel. (2024). Automation Strategies for Medical Device Software Testing. Universal Research Reports, 11(4), 145–158. https://doi.org/10.36676/urr.v11.i4.1341

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Original Research Article

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