RADIOELECTRONIC AND COMPUTER SYSTEMS

The subject matter of the article is the processes of designing and validating a self-evolving quality assurance (SEQA) framework for artificial intelligence (AI)-driven intelligent energy management software (IEMS). The goal is to develop a scalable and adaptive SEQA framework that enables continuous optimization and reliability- and trustworthiness-oriented quality assurance in dynamic, heterogeneous operational environments. The tasks to be solved are: to formalize a unified QA architecture integrating reinforcement learning for adaptive control and federated learning for distributed calibration; to develop a robust cross-domain adaptation mechanism ensuring energy-aware trust calibration; and to empirically validate the framework's performance against baseline models across multiple real-world energy datasets. The methods used are: reinforcement learning for policy-driven optimization, federated learning for privacy-preserving model aggregation, trust calibration techniques for reliability assessment, and experimental benchmarking on NASA, UCI, and OPSD datasets. The following results were obtained: the proposed SEQA framework successfully integrates reinforcement learning-based local adaptation with federated policy aggregation, achieving continuous self-evolution of QA performance across heterogeneous energy management scenarios; the cross-domain adaptation mechanism ensures robust generalization capability, with F1-scores exceeding 0.86 and reliability remaining above 0.91 under diverse operational conditions; experimental validation demonstrates consistent improvements in reliability (F1-score increases by 6–8%), calibration accuracy (Expected Calibration Error reduced to 0.024), and energy efficiency (up to 13%) compared to baseline QA models; the framework maintains stable performance under dynamic data distributions, with ablation studies confirming that each component—reinforcement learning, federated evolution, and continual replay—plays a critical role in enabling robust self-evolving quality assurance. Conclusions. The scientific novelty of the results obtained is as follows: 1) the proposed SEQA framework introduces a unified adaptive paradigm that synergistically combines reinforcement learning, federated calibration, and cross-domain adaptation, enabling autonomous, continuous quality evolution in IEMS; 2) the developed cross-domain mechanism achieves robust generalization and energy-aware performance balancing, addressing key limitations of static and single-domain QA approaches; 3) the extensive experimental validation demonstrates consistent improvements in reliability, calibration accuracy, and energy efficiency, confirming the framework's practical applicability for long-lifecycle industrial deployments; 4) the integration of adaptive aggregation intervals and policy pruning mechanisms minimizes redundancy during synchronization while maintaining near-linear scalability on distributed federated nodes, validating the framework's feasibility for deployment in real-world smart grid and industrial IoT environments

Artificial Intelligence; Energy Management Software; Self-Evolving Quality Assurance; Federated Learning; Reinforcement Learning; Reliability; Calibration

Hall, T., Beecham, S., Bowes, D., Gray, D., & Counsell, S. A Systematic Literature Review on Fault Prediction Performance in Software Engineering. IEEE Transactions on Software Engineering, 2012, vol. 38, no. 6, pp. 1276–1304. DOI: 10.1109/TSE.2011.103.

Jureczko, M., & Madeyski, L. Towards Identifying Software Project Clusters with Respect to Defect Prediction. Proceedings of the 6th International Conference on Predictive Models in Software Engineering, PROMISE, Timishoara, Romania, 2010, article no. 9. pp. 1–10. DOI: 10.1145/1868328.1868342.

Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. A survey on concept drift adaptation. ACM Computing Surveys, 2014, vol. 46, iss. 4, article no.44, pp.1–37. DOI: 10.1145/2523813.

Hendrycks, D., & Dietterich, T. Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. International Conference on Learning Representations (ICLR), New Orleans, Louisiana, United States, 2019. DOI: 10.48550/arXiv.1903.12261.

Kairouz, P., & McMahan, H. B. Advances and Open Problems in Federated Learning. Foundations and Trends in Machine Learning, 2021, vol. 14, iss. 1–2, pp. 1–210. DOI: 10.1561/2200000083.

Wei, T., Wang, Y., & Zhu, Q. Deep Reinforcement Learning for Building HVAC Control. Proceedings of the 4th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys, Austin, TX, USA, 2017, pp. 1–10. DOI: 10.1145/3061639.3062224.

Azuatalam, D., Lee, W.-L., de Nijs, F., & Liebman, A. Reinforcement Learning for Whole-building HVAC Control and Demand Response. Energy and AI, 2020, vol. 2, article no. 100020. DOI: 10.1016/j.egyai.2020.100020.

Pipattanasomporn, M., Kuzlu, M., & Rahman, S. An Algorithm for Intelligent Home Energy Management and Demand Response Analysis. IEEE Transactions on Smart Grid, 2012, vol. 3, iss. 4, pp. 2166–2173. DOI: 10.1109/TSG.2012.2201182.

Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y., & Zhang, Y. Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network. IEEE Transactions on Smart Grid, 2019, vol. 10, iss. 1, pp. 841–851. DOI: 10.1109/TSG.2017.2753802.

Lee, E. Cyber Physical Systems: Design Challenges. 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC), Orlando, FL, USA, 2008, pp. 363-369. DOI: 10.1109/ISORC.2008.25.

Gordieiev, O., Gordieieva, D., Rainer, A., Gorbenko, A., Tarasyuk, O. Quality Assessment of Artificial Intelligence Systems: A Metric-Based Approach. Electronics, 2026, vol. 5(3):691. DOI: 10.3390/electronics15030691.

Kharchenko, V., Fesenko, H., Illiashenko, O. Quality Models for Artificial Intelligence Systems: Characteristic-Based Approach, Development and Application. Sensors, 2022, vol. 22(13):4865. DOI: 10.3390/s22134865.

Felderer, M., & Ramler, R. Quality Assurance for AI-Based Systems: Overview and Challenges (Introduction to Interactive Session). Software Quality: Future Perspectives on Software Engineering Quality, 13th International Conference SWQD, Vienna, Austria, 2021, pp. 33-42. DOI: 10.1007/978-3-030-65854-0_3.

Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., Shahzad, M., Yang, W., Bamler, R., & Zhu, X. X. A Survey of Uncertainty in Deep Neural Networks. Artificial Intelligence Review, 2023, vol. 56 (suppl. 1), pp. 1513-1598. DOI: 10.1007/s10462-023-10562-9.

Chen, X., Dong, W., & Yang, Q. Robust optimal capacity planning of grid-connected microgrid considering energy management under multi-dimensional uncertainties. Applied Energy, 2022, vol. 323, article no. 119642. DOI: 10.1016/j.apenergy.2022.119642.

McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. Communication-efficient learning of deep networks from decentralized data. in Proc. 20th Int. Conf. Artificial Intelligence and Statistics (AISTATS), PMLR, 2017, vol. 54, pp. 1273-1282. Available at: https://proceedings.mlr.press/v54/mcmahan17a.html. (accessed 12.08.2025).

Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. Federated optimization: Distributed machine learning for on-device intelligence. arXiv preprint arXiv:1610.02527, 2016. DOI: 10.48550/arXiv.1610.02527.

Parisio, A., Rikos, E., & Glielmo, L. A model predictive control approach to microgrid operation optimization. IEEE Transactions on Control Systems Technology, 2014, vol. 22, no. 5, pp. 1813–1827. DOI: 10.1109/TCST.2013.2295737.

Pan, S. J., & Yang, Q. A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 2010, vol. 22, no. 10, pp. 1345-1359. DOI: 10.1109/TKDE.2009.191.

Nastoska, A., Jancheska, B., Rizinski, M., Trajanov, D. Evaluating Trustworthiness in AI: Risks, Metrics, and Applications Across Industries. Electronics, 2025, vol. 14(13):2717. DOI: 10.3390/electronics14132717.

Kingma, D. P., & Ba, J. Adam: A Method for Stochastic Optimization. 3rd International Conference for Learning Representations, ICLR, San Diego, 2015. DOI: 10.48550/arXiv.1412.6980.

Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., & Smith, V. Federated Optimization in Heterogeneous Networks. Proceedings of Machine Learning and Systems, MLSys, 2020, pp. 429-450. DOI: 10.48550/arXiv.1812.06127.

Candanedo, L. M., & Feldheim, V. Accurate Occupancy Detection of an Office Room from Light, Temperature, Humidity and CO2 Measurements Using Statistical Learning Models. Energy and Buildings, 2016, vol. 112, pp. 28–39. DOI: 10.1016/j.enbuild.2015.11.071.

Yu, L., Alégroth, E., Chatzipetrou, P., Gorschek, T. Measuring the Quality of Generative AI Systems: Mapping Metrics to Quality Characteristics—Snowballing Literature Review. Information and Software Technology, 2025, vol. 186:107802. DOI: 10.1016/j.infsof.2025.107802.

Ganin, Y., & Lempitsky, V. Unsupervised Domain Adaptation by Backpropagation. Proceedings of the 32nd International Conference on Machine Learning, ICML, 2015, vol. 37, pp. 1180–1189. Available at: https://proceedings.mlr.press/v37/ganin15.html. (accessed 10.08.2025).

Verlan, A. А., Zhihai, W., & Yunhai, Z. Enhancing Reliability of Energy Management Software Through Predictive Modeling and Automated Repair. Connectivity, 2025, vol. 6, pp. 95–102, DOI: 10.31673/2412-9070.2025.061212.

Verlan, A. A., Zhi Hai, W., & Chen, C. Modelling the quality assurance of AI based intelligent energy management software. Modern Information Security, 2025, vol. 3 (63), pp. 199–204. DOI: 10.31673/2409-7292.2025.030192.