Innovative Techniques for Software Verification in Medical Devices

Venudhar Rao Hajari; Abhishek Pandurang Benke; Er. Om Goel; Pandi Kirupa Gopalakrishna Pandian; Dr. Punit Goel; Akshun Chhapola,

doi:10.36676/jrps.v15.i3.1488

Authors

Venudhar Rao Hajari Independent Researcher, Vasavi Nagar, Karkhana, Secunderabad, Andhra Pradesh, 500015, India,
Abhishek Pandurang Benke Independent Researcher, G T Arcade, Opp Uday Baug, B T Kawade Road, Ghorpadi, Pune, Maharashtra, 411028, India,
Er. Om Goel Independent Researcher, Abes Engineering College Ghaziabad,
Pandi Kirupa Gopalakrishna Pandian Sobha Emerald Phase 1, Jakkur, Bangalore 560064,
Dr. Punit Goel Research Supervisor , Maharaja Agrasen Himalayan Garhwal University, Uttarakhand,
Akshun Chhapola, Independent Researcher, Delhi Technical University, Delhi,

DOI:

https://doi.org/10.36676/jrps.v15.i3.1488

Keywords:

Software verification, medical devices, formal methods, model-based testing, automated verification tools, cybersecurity

Abstract

Medical device software verification is essential for safety, effectiveness, and dependability. Traditional verification methods must adapt to complex software systems and regulatory requirements as technology evolves. This abstract discusses novel medical device software verification methods that improve accuracy, efficiency, and regulatory compliance. Medical device software verification requires confirming that the program works as intended in various settings and circumstances. Manual testing and static analysis typically fail to handle contemporary software's dynamic nature and high risks. Recent advances have provided novel methods to address these restrictions. Formal approaches, model-based testing, and automated verification tools each handle medical device software verification difficulties and provide advantages. Formal approaches use mathematical models to validate algorithms and implementations for rigorous software verification. This method detects tiny problems that traditional testing may miss. However, model-based testing generates complete test cases and scenarios by representing the system's behavior using models. This method finds edge situations and validates the system's unexpected circumstance response. Automated verification tools are another industry breakthrough. These technologies scan massive amounts of code using machine learning and artificial intelligence to find bugs faster and more accurately than human techniques. Automation tools may also monitor and check software performance throughout the development lifecycle, delivering real-time feedback and early problem discovery. Simulating and emulating real-world settings to test software is another novel approach. Physical prototypes are expensive and time-consuming, yet these conditions enable extended testing. Cybersecurity advances have led to verification procedures that ensure medical device software is cyber-resistant. In medical device software verification, regulatory compliance is crucial. FDA and ISO criteria must be met when integrating these revolutionary methods. Therefore, knowing and applying these standards with new verification methodologies is essential for device certification and market acceptance. In conclusion, emerging methods that improve accuracy, efficiency, and compliance are fast changing medical device software verification. Modern medical device software complexity is addressed via formal methodologies, model-based testing, automated tools, and simulation environments. Maintaining high standards for medical device software verification requires continual study and development in these areas as technology advances.

References

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. DOI: https://doi.org/10.3390/math10203895

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). DOI: https://doi.org/10.32604/cmc.2023.028631

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. DOI: https://doi.org/10.1109/ICCCIS51004.2021.9397191

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. DOI: https://doi.org/10.1049/icp.2022.0393

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. DOI: https://doi.org/10.1007/978-981-10-1708-7_75

Kumar, A., & Jain, A. (2021). Image smog restoration using oblique gradient profile prior and energy minimization. Frontiers of Computer Science, 15(6), 156706. DOI: https://doi.org/10.1007/s11704-020-9305-8

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. DOI: https://doi.org/10.1109/IC3I56241.2022.10073159

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. DOI: https://doi.org/10.1109/ICDT57929.2023.10150652

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.Ames, A. (2018). Software verification and validation for medical devices. Springer. DOI: https://doi.org/10.1109/UPCON59197.2023.10434702

Anderson, T., & O’Shea, J. (2020). Formal methods in software engineering: A practitioner's guide. Wiley.

Bertolino, A. (2018). Software testing and analysis: Process, principles, and techniques. Wiley.

Bertolino, A., & Nuzzo, P. (2019). Model-based testing and verification of software systems. Springer.

Brown, D., & Waller, S. (2021). Automated tools for software testing: Applications and techniques. IEEE Press.

Clarke, L., & R. P. (2020). Simulation and emulation techniques for software verification. ACM Computing Surveys, 53(4), 1-35. DOI: https://doi.org/10.1145/3376917

Cook, B., & Jacky, J. (2021). The role of formal methods in software reliability. In Handbook of Software Reliability Engineering (pp. 123-145). CRC Press.

Davis, A. (2019). Requirements engineering and management for software projects. Springer.

Gertner, M. (2022). AI and machine learning in software verification. In Advances in Artificial Intelligence (pp. 45-67). Elsevier.

Gómez, M., & Gracia, A. (2019). Model-based testing: A comprehensive review. Journal of Software Engineering and Applications, 12(3), 123-156.

Gupta, S., & Patel, K. (2020). Software testing: Techniques and applications. Wiley.

Heath, C., & Kincaid, S. (2021). Regulatory compliance in medical device software. In Medical Device Software Validation and Verification (pp. 78-92). Springer.

Hodges, C., & He, X. (2019). Verification and validation of medical device software: Challenges and solutions. IEEE Transactions on Biomedical Engineering, 66(6), 1342-1351.

Jain, R., & Kumar, P. (2020). Integration of verification techniques in DevOps. Software Quality Journal, 28(2), 311-334.

Kaur, R., & Sharma, A. (2022). Simulation and emulation for software testing. Journal of Systems and Software, 185, 111-127.

Kumar, R., & Singh, S. (2021). Automated verification tools: A practical guide. Software Engineering Journal, 39(3), 209-222.

Miller, J., & S. P. (2021). Cybersecurity considerations in medical device software. In Handbook of Medical Device Cybersecurity (pp. 202-220). CRC Press.

Nielsen, J., & Johnson, K. (2020). User-centric verification approaches for medical devices. Human-Centric Computing and Information Sciences, 10(1), 15-29.

Sharma, V., & Rao, P. (2022). Advances in formal methods for software verification. Journal of Computing and Information Science, 14(4), 56-74.

Singh, A., & Sinha, D. (2019). Challenges in model-based testing for medical devices. Software Testing & Verification, 59(7), 789-802.

Ayyalasomayajula, Madan Mohan Tito, et al. "Implementing Convolutional Neural Networks for Automated Disease Diagnosis in Telemedicine." 2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2024.

Singla, A., & Dr. Meenu. (2024). The Impact of E-Commerce on Consumer Behaviour: A Comparative Analysis of Traditional and Online Shopping Patterns. Shodh Sagar Journal of Commerce and Economics, 1(1), 24–28. https://doi.org/10.36676/ssjce.v1.i1.05 DOI: https://doi.org/10.36676/ssjce.v1.i1.05

Hasan, M. R. (2024). AI and Machine Learning for Optimal Crop Yield Optimization in the USA. Journal of Computer Science and Technology Studies, 6(2), 48-61. DOI: https://doi.org/10.32996/jcsts.2024.6.2.6

Alam, S. (2023). PMTRS: A Personalized Multimodal Treatment Response System Framework for Personalized Healthcare. International Journal of Applied Health Care Analytics, 8(6), 18–28. Retrieved from https://norislab.com/index.php/IJAHA/article/view/77