Research Article
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A formal and integrated approach to engineering machine learning processes: A method base for project management

Year 2025, Volume: 9 Issue: 1, 152 - 178, 20.01.2025

Murat Paşa Uysal

https://doi.org/10.31127/tuje.1527734

Abstract

Enhancing project management (PM) for machine learning (ML) requires structured acquisition and application of PM knowledge. However, significant differences exist between managing ML-enabled software products (MLESP) and traditional software products (TSP). In modern tool-centric ML environments, creating a method base to support team learning and knowledge management is challenging. Studies also show that a “one-size-fits-all” approach to PM can fail to meet diverse team and organizational requirements. Indeed, the main challenge is capturing, storing, and reusing tacit knowledge on PM methods, processes, tasks, and tools for ML. The experimental, data-driven nature of ML may often lead to ad hoc processes, complicating integration with traditional software lifecycles. Therefore, tailoring a PM method for MLESP becomes critical. This study uses a mixed research approach combining Design Science Research (DSR), PM, Method Engineering (ME), and Process Algebra (PA). Key outputs include an ME framework for PM, a method base for ML, and a hybrid ML PM method tailored for Baskent University Hospital Ankara (BUHA). A use case-based scenario analysis technique validated the requirements phase of the hybrid ML PM method in the context of BUHA. The proposed approach can offer comprehensive, yet pragmatic and adaptable solutions as it blends the strengths of ML, PM, ME, and PA knowledge domains. Moreover, PA contributes formal and mathematical foundations for specifying and validating PM methods and tailoring processes. This study has the potential to contribute not only to ML PM and BUHA but also to advancing process management within the mission and safety-critical domains like healthcare.

Keywords

Machine learning Project management Method engineering Process algebra Healthcare

Ethical Statement

This study was supported by Baskent University (Project title: “An enterprise architecture and artificial intelligence implementation roadmap for Baskent University Hospital Ankara”; Project ID: 2021-11-0032). The author confirms that he has no competing financial and other types of interests, or personal and organizational relationships that could have appeared to influence the study reported in this paper

Supporting Institution

Başkent University

Project Number

Project ID: 2021-11-0032

References

  • Bughin J. & Hazan E. (2017). Five management strategies for getting the most from AI. MIT Sloan Management Review. “https://sloanreview.mit.edu/ article/five-management-strategies-for-getting-the-most-from-ai”.
  • Khomh, F., Adams, B., Cheng, J., Fokaefs, M. & Antoniol G. (2018). Software engineering for machine-learning applications: The road ahead. IEEE Software, 35(5), 81-84.
  • Giray, G. (2021). A software engineering perspective on engineering machine learning systems: State of the art and challenges. The Journal of Systems & Software, 180, 1-35.
  • Oun T. A., Blackburn T. D., Olson B. A. & Blessner P., (2016). An enterprise-wide knowledge management approach to project management. Engineering Management Journal, 28(3), 179-192, DOI: 10.1080/10429247.2016.1203715.
  • Haumer, P. (2007). Eclipse process framework composer part 1: Key concepts & Part 2: Authoring method content and processes, Eclipse Foundation, “http://www.eclipse.org/epf/ general/ EPFComposerOverviewPart1.pdf”.
  • Uysal M.P. (2023). Toward a method engineering framework for project management and machine learning. In IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), Torino, Italy.
  • Saltz, J., Shamshurin, I. & Crowston K (2017), Comparing data science project management methodologies via a controlled experiment. In Proceedings of the 50th Hawaii International Conference on System Sciences, 1013-1022.
  • Uysal M.P. (2022). An integrated and multi-perspective approach to the requirements of machine learning. In Proceedings of IFIP International Conference on Industrial Information Integration (ICIIIE 2022), Bangkok, Thailand.
  • Saltz, J. (2015). The need for new processes, methodologies and tools to support big data teams and improve big data project effectiveness. In Proceedings of the IEEE International Conference on Big Data, 2066-2071.
  • Uysal M.P. (2021). Machine learning and data science project management from an agile perspective. In V. Naidoo and R. Verma (Eds), Methods and challenges in contemporary challenges for agile project management, IGI Global, NY, USA, 73-88.
  • Sellers, B.H. Ralyté, J., Agerfalk, P.J. & Rossi,M. (2014). Situational method engineering. Springer, NY, USA.
  • Amershi, S., Begel, A. C., Bird, R. DeLine, H. Gall, Kamar, E., Nagappan, N., Nushi B. & Zimmermann, T. (2019). Software engineering for machine learning: A case study, Microsoft Research, https://www.microsoft.com.
  • Campanelli, A.S., Parreiras & F.S., (2015). Agile methods tailoring-A systematic literature review, The Journal of Systems and Software, 110, 85-100.
  • Kumeno, F. (2019). Software engineering challenges for machine learning applications: A literature review. Intell. Decis. Technol., 13 (4), 463-476.
  • Bourque, P. & Richard, E. (2014). SWEBOK Version 3.0, IEEE, ISBN-10: 0-7695-5166-1.
  • Lwakatare, L.E., Raj, A., Crnkovic, I., Bosch J. & Olsson H.H. (2020). Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions. Information and Software Technology, 127, 106368.
  • Nascimento, E., Nguyen-Duc, A., Sundbø I. & Conte T. (2020). Software engineering for artificial intelligence and machine learning software: A systematic literature review. ArXiv, arXiv:2011.03751.
  • Fernández, S.M., Bogner, J. Franch, X., Oriol, M., Siebert J. A. Trendowicz, Vollmer, A.M. & Wagner, S. (2022). Software engineering for AI-Based systems: A survey. ACM Transactions on Software Engineering and Methodology, 31 (2), 1-59.
  • Sithambaram, E. (2018). Identifying pitfalls in machine learning implementation projects: A case study of four technology-intensive organizations. Unpublished MS thesis, Royal Institute of Technology, Sweden.
  • Rahman S., Rivera E., Khomh F., Guéhéneuc Y.G. & Lehnert B. (2019). Machine learning software engineering in practice: An industrial case study. ArXiv, https://arxiv.org/abs/1906.07154.
  • Ishikawa, F. & Yoshioka, N. (2019). How do engineers perceive difficulties in engineering of machine-learning systems? In Proceedings of IEEE/ACM 6th International Workshop on Software Engineering Research and Industrial Practice.
  • Gupte, A. (2018). Determining critical success factors for big data projects. Unpublished Doctoral Dissertation, Purdue University, USA.
  • Tsoy, M. & Staples, D.S. (2020). Exploring critical success factors in agile analytics projects. In Proceedings of 53rd Hawaii International Conference on System Sciences, 984-993.
  • Kuhrmann, M., Diebold, P., Münch, J., Tell, P. Garousi, V., Felderer, M. Trektere, K., McCaffery, F., Linssen, O., Hanser, E. & Prause, C.R. (2017). Hybrid software and system development in practice: Waterfall, Scrum, and beyond. In Proceedings of International Conference on Software System Process.
  • Gill, A.Q. Sellers, B.H. & Niazi, M. (2018). Scaling for agility: A reference model for hybrid traditional-agile software development methodologies. Inf. Syst. Front, 20, 315-341.
  • Papadakis, E. & Tsironis, L. (2020). Towards a hybrid project management framework: A systematic literature review on traditional, agile and hybrid techniques. The Journal of Modern Project Management, 8 (2) 124-139.
  • Conforto, E.C. & Amaral, D.C. (2016). Agile project management and stage-gate model. A hybrid framework for technology-based companies. Journal of Engineering and Technology Management, 40, 1-24.
  • Zasa, F.P. Patrucco, A. & Pellizzoni, E. (2021). Managing the hybrid organization: how can agile and traditional project management coexist? Research-Technology Management, 64 (1) 54-63.
  • Sithambaram, J., Nasir, M.H.N.B.M. & Ahmad R., (2021). Issues and challenges impacting the successful management of agile-hybrid projects: A grounded theory approach. International Journal of Project Management, “https://doi.org/ 10.1016/j.ijproman. 2021.03.002”.
  • Azenha, C.F., Reis, D.A. & Fleury A.L. (2021). The role and characteristics of hybrid approaches to project management in the development of technology-based products and services. Project Management Journal, 52 (1), 90-110.
  • Uysal M.P., (2022). Machine learning-enabled healthcare information systems in view of industrial information integration engineering. Journal of Industrial Information Integration, 30, (1), 100382.
  • Sellers, B. H., France R., Georg, G. & Reddy R. (2007). A method engineering approach to developing aspect-oriented modelling processes based on the OPEN process framework. Information and Software Technology, 49, 761-773.
  • SPEM (2008). Software & Systems Process Engineering Meta-Model Specification v 2.0. OMG Technical Document, USA.
  • EPF (2023). Eclipse process framework project, Eclipse Foundation, “https://projects.eclipse. org/projects/ technology.epf”.
  • Haumer P. (2007). Eclipse process framework composer Part 1: Key concepts & Part 2: Authoring method content and processes, Eclipse Foundation, “http://www.eclipse.org/epf/ general/ EPFComposerOverviewPart1.pdf”.
  • Bergstra, J.A., Ponse, A. & Smolka, S.A. (2001). Handbook of process algebra. Elsevier Science, Netherlands.
  • Baeten, J.C.M (2004). A brief history of process algebra. Theoretical Computer Science, 335, 131-146.
  • Bergstra, J.A. & Klop, J.W. (1985). Algebra of communicating processes with abstraction. Theoretical Computer Science, 37, 77-121.
  • Luttik, B. (2006). What is algebraic in process theory? Electronic Notes in Theoretical Computer Science, 162, 227-231.
  • Fokkink, W. (2007). Introduction to Process Algebra, Springer, USA.
  • CRISP-DM (2000). CRISP-DM 1.0: Step-by-step data mining guide. The CRISP-DM consortium, SPSS Publication, USA.
  • V. Plotnikova, M. Dumas & F.P. Milani (2022) Applying the CRISP-DM data mining process in the financial services industry: Elicitation of adaptation requirements. Data & Knowledge Engineering, 139, 102013.
  • TDSP, (2024). What is the team data science process? “https://docs.mcrosoft.com”.
  • Sutherland, J. & Schwaber K., (2017). The Scrum Guide, “http://www.scrumguides.org”.
  • Hammarberg, M. & Sunden, J. (2014). Kanban in action. Manning Publications, USA.
  • DDS, (2023). Data driven scrum framework, “https://www.datascience-pm.com/data-driven- scrum”.
  • Gemino, A., Reich, B.H. & Serrador, P.M (2021). Agile, traditional, and hybrid approaches to project success: Is hybrid a poor second choice? Project Management Journal, 52 (2), 161-175.
  • Hevner, A. March, S., Park, J. & Ram S. (2004). Design science in information systems research. MIS Quarterly, 28 (1), 75-105.
  • S. Shafiee, Y. Wautelet, S. Poelmans & S. Heng (2023). An empirical evaluation of scrum training’s suitability for the model-driven development of knowledge-intensive software systems. Data & Knowledge Engineering, 146, 102195.
  • Conboy, K. & Fitsgerald, B (2010). Method and developer characteristics for effective agile method tailoring: A study of XP expert opinion. ACM Transactions on Software Engineering and Methodology, 20 (2), 1-30.
  • Haakman, M., Cruz, L., Huijgens, H. & Deursen A. (2021). AI lifecycle models need to be revised: An exploratory study in Fintech. Empirical Software Engineering, 26 (95), 1-29.
  • Ramasamy, D., Sarasua, C., Bacchelli, A. & Bernstein, A. (2023). Workflow analysis of data science code in public GitHub repositories. Empirical Software Engineering, 28 (7), 1-47.
  • Gill, A.Q., Sellers, B.H. & Niazi, M. (2018). Scaling for agility: A reference model for hybrid traditional-agile software development methodologies. Inf. Syst. Front., 20, 315-341.
  • Abro, A.A., Siddique, W.A., Talpur, M.S.H., Jumani, A.K. & Yaşar E. (2023). A combined approach of base and meta learners for hybrid system. Turkish Journal of Engineering, 7(1), 25-32.
  • Maza, D., Ojo, J. O., & Akinlade, G. O. (2024). A predictive machine learning framework for diabetes. Turkish Journal of Engineering, 8(3), 583-592, “https://doi.org/10.31127/tuje.1434305”.
  • Sinap, V. (2024). Comparative analysis of machine learning techniques for credit card fraud detection: Dealing with imbalanced datasets. Turkish Journal of Engineering, 8(2), 196-208, “https://doi.org/ 10.31127/tuje.1386127”.
  • Leka, B., & Hoxha, K. (2024). Software engineering methodologies in programming companies in Albania. Engineering Applications, 3(1), 85–91, “https://publish.mersin.edu.tr/index.php/enap/article/view/1506”.
  • Kayıran, H. F. (2022). The function of artificial intelligence and its sub-branches in the field of health. Engineering Applications, 1(2), 99–107, “https://publish.mersin.edu.tr/index.php/enap/article/view/328”.
  • Dirik, M. (2023). Machine learning-based lung cancer diagnosis. Turkish Journal of Engineering, 7(4), 322-330, “https://doi.org/10.31127/tuje.1180931”.
  • Juraev, D. A., & Bozorov, M. N. (2024). The role of algebra and its application in modern sciences. Engineering Applications, 3(1), 59–67, “https://publish.mersin.edu.tr/index.php/enap/article/view/1499”.
  • İncekara, Ç. Ö. (2023). Industrial internet of things (IIoT) in energy sector. Advanced Engineering Science, 3, 21–30. “https://publish.mersin.edu.tr/ index.php/ades/article/view/839”.
  • Mema, B. & Basholli , F. (2023). Internet of Things in the development of future businesses in Albania. Advanced Engineering Science, 3, 196–205. “https://publish.mersin.edu.tr/index.php/ades/article/view/1325”.
  • Grant, D., & Mergen, A. E. (2013). Using SPC in conjunction with APC. Quality Engineering, 23 (4),360-364.
  • Grant, D., & Mergen, A. E. (2009). Towards the use of Six Sigma in software development. Total Quality Management & Business Excellence, 20(7), 705–712.

Year 2025, Volume: 9 Issue: 1, 152 - 178, 20.01.2025

Murat Paşa Uysal

https://doi.org/10.31127/tuje.1527734

Abstract

Project Number

Project ID: 2021-11-0032

References

  • Bughin J. & Hazan E. (2017). Five management strategies for getting the most from AI. MIT Sloan Management Review. “https://sloanreview.mit.edu/ article/five-management-strategies-for-getting-the-most-from-ai”.
  • Khomh, F., Adams, B., Cheng, J., Fokaefs, M. & Antoniol G. (2018). Software engineering for machine-learning applications: The road ahead. IEEE Software, 35(5), 81-84.
  • Giray, G. (2021). A software engineering perspective on engineering machine learning systems: State of the art and challenges. The Journal of Systems & Software, 180, 1-35.
  • Oun T. A., Blackburn T. D., Olson B. A. & Blessner P., (2016). An enterprise-wide knowledge management approach to project management. Engineering Management Journal, 28(3), 179-192, DOI: 10.1080/10429247.2016.1203715.
  • Haumer, P. (2007). Eclipse process framework composer part 1: Key concepts & Part 2: Authoring method content and processes, Eclipse Foundation, “http://www.eclipse.org/epf/ general/ EPFComposerOverviewPart1.pdf”.
  • Uysal M.P. (2023). Toward a method engineering framework for project management and machine learning. In IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC), Torino, Italy.
  • Saltz, J., Shamshurin, I. & Crowston K (2017), Comparing data science project management methodologies via a controlled experiment. In Proceedings of the 50th Hawaii International Conference on System Sciences, 1013-1022.
  • Uysal M.P. (2022). An integrated and multi-perspective approach to the requirements of machine learning. In Proceedings of IFIP International Conference on Industrial Information Integration (ICIIIE 2022), Bangkok, Thailand.
  • Saltz, J. (2015). The need for new processes, methodologies and tools to support big data teams and improve big data project effectiveness. In Proceedings of the IEEE International Conference on Big Data, 2066-2071.
  • Uysal M.P. (2021). Machine learning and data science project management from an agile perspective. In V. Naidoo and R. Verma (Eds), Methods and challenges in contemporary challenges for agile project management, IGI Global, NY, USA, 73-88.
  • Sellers, B.H. Ralyté, J., Agerfalk, P.J. & Rossi,M. (2014). Situational method engineering. Springer, NY, USA.
  • Amershi, S., Begel, A. C., Bird, R. DeLine, H. Gall, Kamar, E., Nagappan, N., Nushi B. & Zimmermann, T. (2019). Software engineering for machine learning: A case study, Microsoft Research, https://www.microsoft.com.
  • Campanelli, A.S., Parreiras & F.S., (2015). Agile methods tailoring-A systematic literature review, The Journal of Systems and Software, 110, 85-100.
  • Kumeno, F. (2019). Software engineering challenges for machine learning applications: A literature review. Intell. Decis. Technol., 13 (4), 463-476.
  • Bourque, P. & Richard, E. (2014). SWEBOK Version 3.0, IEEE, ISBN-10: 0-7695-5166-1.
  • Lwakatare, L.E., Raj, A., Crnkovic, I., Bosch J. & Olsson H.H. (2020). Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions. Information and Software Technology, 127, 106368.
  • Nascimento, E., Nguyen-Duc, A., Sundbø I. & Conte T. (2020). Software engineering for artificial intelligence and machine learning software: A systematic literature review. ArXiv, arXiv:2011.03751.
  • Fernández, S.M., Bogner, J. Franch, X., Oriol, M., Siebert J. A. Trendowicz, Vollmer, A.M. & Wagner, S. (2022). Software engineering for AI-Based systems: A survey. ACM Transactions on Software Engineering and Methodology, 31 (2), 1-59.
  • Sithambaram, E. (2018). Identifying pitfalls in machine learning implementation projects: A case study of four technology-intensive organizations. Unpublished MS thesis, Royal Institute of Technology, Sweden.
  • Rahman S., Rivera E., Khomh F., Guéhéneuc Y.G. & Lehnert B. (2019). Machine learning software engineering in practice: An industrial case study. ArXiv, https://arxiv.org/abs/1906.07154.
  • Ishikawa, F. & Yoshioka, N. (2019). How do engineers perceive difficulties in engineering of machine-learning systems? In Proceedings of IEEE/ACM 6th International Workshop on Software Engineering Research and Industrial Practice.
  • Gupte, A. (2018). Determining critical success factors for big data projects. Unpublished Doctoral Dissertation, Purdue University, USA.
  • Tsoy, M. & Staples, D.S. (2020). Exploring critical success factors in agile analytics projects. In Proceedings of 53rd Hawaii International Conference on System Sciences, 984-993.
  • Kuhrmann, M., Diebold, P., Münch, J., Tell, P. Garousi, V., Felderer, M. Trektere, K., McCaffery, F., Linssen, O., Hanser, E. & Prause, C.R. (2017). Hybrid software and system development in practice: Waterfall, Scrum, and beyond. In Proceedings of International Conference on Software System Process.
  • Gill, A.Q. Sellers, B.H. & Niazi, M. (2018). Scaling for agility: A reference model for hybrid traditional-agile software development methodologies. Inf. Syst. Front, 20, 315-341.
  • Papadakis, E. & Tsironis, L. (2020). Towards a hybrid project management framework: A systematic literature review on traditional, agile and hybrid techniques. The Journal of Modern Project Management, 8 (2) 124-139.
  • Conforto, E.C. & Amaral, D.C. (2016). Agile project management and stage-gate model. A hybrid framework for technology-based companies. Journal of Engineering and Technology Management, 40, 1-24.
  • Zasa, F.P. Patrucco, A. & Pellizzoni, E. (2021). Managing the hybrid organization: how can agile and traditional project management coexist? Research-Technology Management, 64 (1) 54-63.
  • Sithambaram, J., Nasir, M.H.N.B.M. & Ahmad R., (2021). Issues and challenges impacting the successful management of agile-hybrid projects: A grounded theory approach. International Journal of Project Management, “https://doi.org/ 10.1016/j.ijproman. 2021.03.002”.
  • Azenha, C.F., Reis, D.A. & Fleury A.L. (2021). The role and characteristics of hybrid approaches to project management in the development of technology-based products and services. Project Management Journal, 52 (1), 90-110.
  • Uysal M.P., (2022). Machine learning-enabled healthcare information systems in view of industrial information integration engineering. Journal of Industrial Information Integration, 30, (1), 100382.
  • Sellers, B. H., France R., Georg, G. & Reddy R. (2007). A method engineering approach to developing aspect-oriented modelling processes based on the OPEN process framework. Information and Software Technology, 49, 761-773.
  • SPEM (2008). Software & Systems Process Engineering Meta-Model Specification v 2.0. OMG Technical Document, USA.
  • EPF (2023). Eclipse process framework project, Eclipse Foundation, “https://projects.eclipse. org/projects/ technology.epf”.
  • Haumer P. (2007). Eclipse process framework composer Part 1: Key concepts & Part 2: Authoring method content and processes, Eclipse Foundation, “http://www.eclipse.org/epf/ general/ EPFComposerOverviewPart1.pdf”.
  • Bergstra, J.A., Ponse, A. & Smolka, S.A. (2001). Handbook of process algebra. Elsevier Science, Netherlands.
  • Baeten, J.C.M (2004). A brief history of process algebra. Theoretical Computer Science, 335, 131-146.
  • Bergstra, J.A. & Klop, J.W. (1985). Algebra of communicating processes with abstraction. Theoretical Computer Science, 37, 77-121.
  • Luttik, B. (2006). What is algebraic in process theory? Electronic Notes in Theoretical Computer Science, 162, 227-231.
  • Fokkink, W. (2007). Introduction to Process Algebra, Springer, USA.
  • CRISP-DM (2000). CRISP-DM 1.0: Step-by-step data mining guide. The CRISP-DM consortium, SPSS Publication, USA.
  • V. Plotnikova, M. Dumas & F.P. Milani (2022) Applying the CRISP-DM data mining process in the financial services industry: Elicitation of adaptation requirements. Data & Knowledge Engineering, 139, 102013.
  • TDSP, (2024). What is the team data science process? “https://docs.mcrosoft.com”.
  • Sutherland, J. & Schwaber K., (2017). The Scrum Guide, “http://www.scrumguides.org”.
  • Hammarberg, M. & Sunden, J. (2014). Kanban in action. Manning Publications, USA.
  • DDS, (2023). Data driven scrum framework, “https://www.datascience-pm.com/data-driven- scrum”.
  • Gemino, A., Reich, B.H. & Serrador, P.M (2021). Agile, traditional, and hybrid approaches to project success: Is hybrid a poor second choice? Project Management Journal, 52 (2), 161-175.
  • Hevner, A. March, S., Park, J. & Ram S. (2004). Design science in information systems research. MIS Quarterly, 28 (1), 75-105.
  • S. Shafiee, Y. Wautelet, S. Poelmans & S. Heng (2023). An empirical evaluation of scrum training’s suitability for the model-driven development of knowledge-intensive software systems. Data & Knowledge Engineering, 146, 102195.
  • Conboy, K. & Fitsgerald, B (2010). Method and developer characteristics for effective agile method tailoring: A study of XP expert opinion. ACM Transactions on Software Engineering and Methodology, 20 (2), 1-30.
  • Haakman, M., Cruz, L., Huijgens, H. & Deursen A. (2021). AI lifecycle models need to be revised: An exploratory study in Fintech. Empirical Software Engineering, 26 (95), 1-29.
  • Ramasamy, D., Sarasua, C., Bacchelli, A. & Bernstein, A. (2023). Workflow analysis of data science code in public GitHub repositories. Empirical Software Engineering, 28 (7), 1-47.
  • Gill, A.Q., Sellers, B.H. & Niazi, M. (2018). Scaling for agility: A reference model for hybrid traditional-agile software development methodologies. Inf. Syst. Front., 20, 315-341.
  • Abro, A.A., Siddique, W.A., Talpur, M.S.H., Jumani, A.K. & Yaşar E. (2023). A combined approach of base and meta learners for hybrid system. Turkish Journal of Engineering, 7(1), 25-32.
  • Maza, D., Ojo, J. O., & Akinlade, G. O. (2024). A predictive machine learning framework for diabetes. Turkish Journal of Engineering, 8(3), 583-592, “https://doi.org/10.31127/tuje.1434305”.
  • Sinap, V. (2024). Comparative analysis of machine learning techniques for credit card fraud detection: Dealing with imbalanced datasets. Turkish Journal of Engineering, 8(2), 196-208, “https://doi.org/ 10.31127/tuje.1386127”.
  • Leka, B., & Hoxha, K. (2024). Software engineering methodologies in programming companies in Albania. Engineering Applications, 3(1), 85–91, “https://publish.mersin.edu.tr/index.php/enap/article/view/1506”.
  • Kayıran, H. F. (2022). The function of artificial intelligence and its sub-branches in the field of health. Engineering Applications, 1(2), 99–107, “https://publish.mersin.edu.tr/index.php/enap/article/view/328”.
  • Dirik, M. (2023). Machine learning-based lung cancer diagnosis. Turkish Journal of Engineering, 7(4), 322-330, “https://doi.org/10.31127/tuje.1180931”.
  • Juraev, D. A., & Bozorov, M. N. (2024). The role of algebra and its application in modern sciences. Engineering Applications, 3(1), 59–67, “https://publish.mersin.edu.tr/index.php/enap/article/view/1499”.
  • İncekara, Ç. Ö. (2023). Industrial internet of things (IIoT) in energy sector. Advanced Engineering Science, 3, 21–30. “https://publish.mersin.edu.tr/ index.php/ades/article/view/839”.
  • Mema, B. & Basholli , F. (2023). Internet of Things in the development of future businesses in Albania. Advanced Engineering Science, 3, 196–205. “https://publish.mersin.edu.tr/index.php/ades/article/view/1325”.
  • Grant, D., & Mergen, A. E. (2013). Using SPC in conjunction with APC. Quality Engineering, 23 (4),360-364.
  • Grant, D., & Mergen, A. E. (2009). Towards the use of Six Sigma in software development. Total Quality Management & Business Excellence, 20(7), 705–712.
There are 64 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Articles
Authors

Murat Paşa Uysal 0000-0002-8349-9403

Project Number Project ID: 2021-11-0032
Early Pub Date January 17, 2025
Publication Date January 20, 2025
Submission Date August 3, 2024
Acceptance Date November 7, 2024
Published in Issue Year 2025 Volume: 9 Issue: 1

Cite

APA Uysal, M. P. (2025). A formal and integrated approach to engineering machine learning processes: A method base for project management. Turkish Journal of Engineering, 9(1), 152-178. https://doi.org/10.31127/tuje.1527734
AMA Uysal MP. A formal and integrated approach to engineering machine learning processes: A method base for project management. TUJE. January 2025;9(1):152-178. doi:10.31127/tuje.1527734
Chicago Uysal, Murat Paşa. “A Formal and Integrated Approach to Engineering Machine Learning Processes: A Method Base for Project Management”. Turkish Journal of Engineering 9, no. 1 (January 2025): 152-78. https://doi.org/10.31127/tuje.1527734.
EndNote Uysal MP (January 1, 2025) A formal and integrated approach to engineering machine learning processes: A method base for project management. Turkish Journal of Engineering 9 1 152–178.
IEEE M. P. Uysal, “A formal and integrated approach to engineering machine learning processes: A method base for project management”, TUJE, vol. 9, no. 1, pp. 152–178, 2025, doi: 10.31127/tuje.1527734.
ISNAD Uysal, Murat Paşa. “A Formal and Integrated Approach to Engineering Machine Learning Processes: A Method Base for Project Management”. Turkish Journal of Engineering 9/1 (January 2025), 152-178. https://doi.org/10.31127/tuje.1527734.
JAMA Uysal MP. A formal and integrated approach to engineering machine learning processes: A method base for project management. TUJE. 2025;9:152–178.
MLA Uysal, Murat Paşa. “A Formal and Integrated Approach to Engineering Machine Learning Processes: A Method Base for Project Management”. Turkish Journal of Engineering, vol. 9, no. 1, 2025, pp. 152-78, doi:10.31127/tuje.1527734.
Vancouver Uysal MP. A formal and integrated approach to engineering machine learning processes: A method base for project management. TUJE. 2025;9(1):152-78.
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