{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T04:23:13Z","timestamp":1774585393670,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T00:00:00Z","timestamp":1750377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Machine Learning (ML) is increasingly applied across various domains, addressing tasks such as predictive analytics, anomaly detection, and decision-making. Many of these applications share similar underlying tasks, offering potential for systematic reuse. However, existing reuse in ML is often fragmented, small-scale, and ad hoc, focusing on isolated components such as pretrained models or datasets without a cohesive framework. Product Line Engineering (PLE) is a well-established approach for achieving large-scale systematic reuse in traditional engineering. It enables efficient management of core assets like requirements, models, and code across product families. However, traditional PLE is not designed to accommodate ML-specific assets\u2014such as datasets, feature pipelines, and hyperparameters\u2014and is not aligned with the iterative, data-driven workflows of ML systems. To address this gap, we propose Machine Learning Product Line Engineering (ML PLE), a framework that adapts PLE principles for ML systems. In contrast to conventional ML reuse methods such as transfer learning or fine-tuning, our framework introduces a systematic, variability-aware reuse approach that spans the entire lifecycle of ML development, including datasets, pipelines, models, and configuration assets. The proposed framework introduces the key requirements for ML PLE and the lifecycle process tailored to machine-learning-intensive systems. We illustrate the approach using an industrial case study in the context of space systems, where ML PLE is applied for data analytics of satellite missions.<\/jats:p>","DOI":"10.3390\/make7030058","type":"journal-article","created":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T05:17:42Z","timestamp":1750396662000},"page":"58","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine Learning Product Line Engineering: A Systematic Reuse Framework"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8538-7261","authenticated-orcid":false,"given":"Bedir","family":"Tekinerdogan","sequence":"first","affiliation":[{"name":"Information Technology Group, Wageningen University & Research, 6700 EW Wageningen, The Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,20]]},"reference":[{"key":"ref_1","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_2","unstructured":"Russell, S., and Norvig, P. (2021). Artificial Intelligence: A Modern Approach, Pearson. [4th ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.infsof.2014.12.007","article-title":"Empirical evaluation of a decision support model for adopting software product line engineering","volume":"60","author":"Tekinerdogan","year":"2015","journal-title":"Inf. Softw. Technol."},{"key":"ref_4","unstructured":"Clements, P., and Northrop, L. (2002). Software Product Lines: Practices and Patterns, Addison-Wesley."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pohl, K., B\u00f6ckle, G.F., and van der Linden, F. (2005). Software Product Line Engineering\u2014Foundations, Principles, and Techniques, Springer.","DOI":"10.1007\/3-540-28901-1"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Amershi, S., Begel, A., Bird, C., DeLine, R., Gall, H., Kamar, E., Nagappan, N., Nushi, B., and Zimmermann, T. (2019, January 25\u201331). Software Engineering for Machine Learning: A Case Study. Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), Montreal, QC, Canada.","DOI":"10.1109\/ICSE-SEIP.2019.00042"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tekinerdogan, B. (2024, January 23\u201326). AI-SoS: A strategic framework for integrating artificial intelligence in system of systems. Proceedings of the 19th Annual System of Systems Engineering Conference (SoSE), Tacoma, WA, USA.","DOI":"10.1109\/SOSE62659.2024.10620943"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1007\/s10796-023-10388-4","article-title":"Data Sensitivity and Domain Specificity in Reuse of Machine Learning Applications","volume":"26","author":"Rutschi","year":"2024","journal-title":"Inf. Syst. Front."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nature Machine Intelligence (2024). The Rewards of Reusable Machine Learning Code. Nat. Mach. Intell., 6, 369.","DOI":"10.1038\/s42256-024-00835-5"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_11","unstructured":"Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., and Dennison, D. (2015, January 7\u201312). Hidden Technical Debt in Machine Learning Systems. Proceedings of the 29th International Conference on Neural Information Processing Systems\u2014Volume 2, Montreal, QC, Canada."},{"key":"ref_12","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). TensorFlow: A System for Large-Scale Machine Learning. Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, Savannah, GA, USA."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"AlShehhi, M., and Wang, D. (2020, January 2\u20134). Machine Learning Pipeline for Reusing Pretrained Models. Proceedings of the 12th International Conference on Management of Digital EcoSystems, Virtual Event, United Arab Emirates.","DOI":"10.1145\/3415958.3433054"},{"key":"ref_14","unstructured":"Devlin, J., Chang, M., Lee, K., and Toutanova, K. (2019, January 2\u20137). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), Minneapolis, MN, USA."},{"key":"ref_15","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"Volume 25","author":"Pereira","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_16","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., and Clark, J. (2021, January 18\u201324). Learning Transferable Visual Models from Natural Language Supervision. Proceedings of the 38th International Conference on Machine Learning (ICML), Virtual."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4230","DOI":"10.14778\/3625054.3625060","article-title":"Optimizing Data Pipelines for Machine Learning in Feature Stores","volume":"16","author":"Liu","year":"2023","journal-title":"Proc. VLDB Endow."},{"key":"ref_18","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019, January 4\u20138). Optuna: A Next-generation Hyperparameter Optimization Framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330701"},{"key":"ref_20","unstructured":"Coleman, C., Narayanan, D., Kang, D., Zhao, T., Zhang, J., Nardi, L., Bailis, P., Olukotun, K., R\u00e9, C., and Zaharia, M. (2017, January 14\u201315). DAWNBench: An End-to-End Deep Learning Benchmark and Competition. Proceedings of the 2017 Conference on Systems and Machine Learning, Noida, India."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chen, A., Chow, A., Davidson, A., DCunha, A., Ghodsi, A., Hong, S.A., Konwinski, A., Mewald, C., Murching, S., and Nykodym, T. (2020, January 14). Developments in MLflow: A System to Accelerate the Machine Learning Lifecycle. Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning, Portland, OR, USA. Article No. 5.","DOI":"10.1145\/3399579.3399867"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1816","DOI":"10.1109\/TAES.2018.2876586","article-title":"Machine Learning Methods for Spacecraft Telemetry Mining","volume":"55","author":"Ibrahim","year":"2019","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_23","first-page":"1","article-title":"Exploration of Data Analytics for Ground Segment in Space Systems","volume":"Volume 391","author":"Shishkov","year":"2020","journal-title":"Business Modeling and Software Design, Proceedings of the 10th International Symposium, BMSD 2020, Berlin, Germany, 6\u20138 July 2020"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Murphy, J., Ward, J.E., and Mac Namee, B. (2023, January 2\u20136). An Overview of Machine Learning Techniques for Onboard Anomaly Detection in Satellite Telemetry. Proceedings of the 2023 European Data Handling & Data Processing Conference (EDHPC), Juan Les Pins, France.","DOI":"10.23919\/EDHPC59100.2023.10396403"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"109855","DOI":"10.1016\/j.compag.2024.109855","article-title":"Implementing FAIR Principles in Data Management Systems: A Multi-Case Study in Precision Farming","volume":"230","author":"Krisnawijaya","year":"2025","journal-title":"Comput. Electron. 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