THE ROLE AND BENEFITS OF VERSION CONTROL SYSTEMS IN COLLABORATIVE SOFTWARE DEVELOPMENT
Main Article Content
Keywords
Version Control Systems, Git, Collaborative Development, Branching and Merging, Conflict Resolution, Code Tracking and History
Abstract
The paper propounds the use and benefit of VC systems particularly Git in ensemble software development. Version control has remained an important feature for the present software engineering that it guarantees the possible instruments for the tracking of the code change, branches, and others, in case of a conflict between a group of developers. The implementation demonstrates how Git also supports parallel development on a file while at the same time enhancing the code visibility of projects and organizing them within a single repository. In addition, the contribution history is used in fulfilling the accountability of a certain commit and debug process. In the case of pushing and pulling changes, employees’ work of all members of remote teams is properly set and configured to establish the required coordination and integration of their performances when managing a project and minimizing risks. Another hypothesis of this paper is that Git improves such aspects as collaboration teams, code quality, and integration of project deliverables in settings where parallel development is needed.
References
Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J. and Wang, H., 2023. Large language models for software engineering: A systematic literature review. ACM Transactions on Software Engineering and Methodology.
[2] Qian, C., Cong, X., Yang, C., Chen, W., Su, Y., Xu, J., Liu, Z. and Sun, M., 2023. Communicative agents for software development. arXiv preprint arXiv:2307.07924, 6.
[3] Belzner, L., Gabor, T. and Wirsing, M., 2023, October. Large language model assisted software engineering: prospects, challenges, and a case study. In International Conference on Bridging the Gap between AI and Reality (pp. 355-374). Cham: Springer Nature Switzerland.
[4] Fraiwan, M. and Khasawneh, N., 2023. A review of chatgpt applications in education, marketing, software engineering, and healthcare: Benefits, drawbacks, and research directions. arXiv preprint arXiv:2305.00237.
[5] Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S. and Zhang, J.M., 2023, May. Large language models for software engineering: Survey and open problems. In 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE) (pp. 31-53). IEEE.
[6] Van, L.P., Do Chi, K. and Duc, T.N., 2023. Review of hydrogen technologies based microgrid: Energy management systems, challenges and future recommendations. International Journal of Hydrogen Energy, 48(38), pp.14127-14148.
[7] Tajjour, S. and Chandel, S.S., 2023. A comprehensive review on sustainable energy management systems for optimal operation of future-generation of solar microgrids. Sustainable Energy Technologies and Assessments, 58, p.103377.
[8] Qian, C., Liu, W., Liu, H., Chen, N., Dang, Y., Li, J., Yang, C., Chen, W., Su, Y., Cong, X. and Xu, J., 2024, August. Chatdev: Communicative agents for software development. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 15174-15186).
[9] Gokarna, M. and Singh, R., 2021, February. DevOps: a historical review and future works. In 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 366-371). IEEE.
[10] Al-Saqqa, S., Sawalha, S. and AbdelNabi, H., 2020. Agile software development: Methodologies and trends. International Journal of Interactive Mobile Technologies, 14(11).
[11] Ford, D., Storey, M.A., Zimmermann, T., Bird, C., Jaffe, S., Maddila, C., Butler, J.L., Houck, B. and Nagappan, N., 2021. A tale of two cities: Software developers working from home during the covid-19 pandemic. ACM Transactions on Software Engineering and Methodology (TOSEM), 31(2), pp.1-37.
[12] Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J. and Wang, H., 2023. Large language models for software engineering: A systematic literature review. ACM Transactions on Software Engineering and Methodology.
[13] Dong, Y., Jiang, X., Jin, Z. and Li, G., 2024. Self-collaboration code generation via chatgpt. ACM Transactions on Software Engineering and Methodology, 33(7), pp.1-38.
[14] Rasul, T., Nair, S., Kalendra, D., Robin, M., de Oliveira Santini, F., Ladeira, W.J., Sun, M., Day, I., Rather, R.A. and Heathcote, L., 2023. The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching, 6(1), pp.41-56.
[15] Molnár, G., József, C. and Éva, K., 2023, January. Evaluation and technological solutions for a dynamic, unified cloud programming development environment: Ease of use and applicable system for uniformized practices and assessments. In 2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 000237-000240). IEEE.
[16] Iwanaga, T., Usher, W. and Herman, J., 2022. Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses. Socio-Environmental Systems Modelling, 4, pp.18155-18155.
[17] Bordeleau, F., Combemale, B., Eramo, R., Van Den Brand, M. and Wimmer, M., 2020. Towards model-driven digital twin engineering: Current opportunities and future challenges. In Systems Modelling and Management: First International Conference, ICSMM 2020, Bergen, Norway, June 25–26, 2020, Proceedings 1 (pp. 43-54). Springer International Publishing.
[18] Sahay, A., Indamutsa, A., Di Ruscio, D. and Pierantonio, A., 2020, August. Supporting the understanding and comparison of low-code development platforms. In 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) (pp. 171-178). IEEE.
[19] Pelluru, K., 2021. Integrate security practices and compliance requirements into DevOps processes. MZ Computing Journal, 2(2), pp.1-19.
[20] Bello, S.A., Oyedele, L.O., Akinade, O.O., Bilal, M., Delgado, J.M.D., Akanbi, L.A., Ajayi, A.O. and Owolabi, H.A., 2021. Cloud computing in construction industry: Use cases, benefits and challenges. Automation in Construction, 122, p.103441.
[21] Ross, S.I., Martinez, F., Houde, S., Muller, M. and Weisz, J.D., 2023, March. The programmer’s assistant: Conversational interaction with a large language model for software development. In Proceedings of the 28th International Conference on Intelligent User Interfaces (pp. 491-514).
[22] Ahdida, C., Bozzato, D., Calzolari, D., Cerutti, F., Charitonidis, N., Cimmino, A., Coronetti, A., D’Alessandro, G.L., Donadon Servelle, A., Esposito, L.S. and Froeschl, R., 2022. New capabilities of the FLUKA multi-purpose code. Frontiers in Physics, 9, p.788253.
[23] Krauß, V., Boden, A., Oppermann, L. and Reiners, R., 2021, May. Current practices, challenges, and design implications for collaborative AR/VR application development. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-15).
[24] Anzt, H., Bach, F., Druskat, S., Löffler, F., Loewe, A., Renard, B.Y., Seemann, G., Struck, A., Achhammer, E., Aggarwal, P. and Appel, F., 2021. An environment for sustainable research software in Germany and beyond: current state, open challenges, and call for action. F1000Research, 9, p.295.
[25] Al-Heety, O.S., Zakaria, Z., Ismail, M., Shakir, M.M., Alani, S. and Alsariera, H., 2020. A comprehensive survey: Benefits, services, recent works, challenges, security, and use cases for sdn-vanet. IEEE Access, 8, pp.91028-91047.
[26] Dusdal, J. and Powell, J.J., 2021. Benefits, motivations, and challenges of international collaborative research: A sociology of science case study. Science and Public Policy, 48(2), pp.235-245.
[27] Marion, T.J. and Fixson, S.K., 2021. The transformation of the innovation process: How digital tools are changing work, collaboration, and organizations in new product development. Journal of Product Innovation Management, 38(1), pp.192-215.
[28] Wang, X., Sun, Y. and Ding, D., 2022. Adaptive dynamic programming for networked control systems under communication constraints: A survey of trends and techniques. International Journal of Network Dynamics and Intelligence, pp.85-98.
[29] Dusdal, J. and Powell, J.J., 2021. Benefits, motivations, and challenges of international collaborative research: A sociology of science case study. Science and Public Policy, 48(2), pp.235-245.
[30] Anthony, C., Bechky, B.A. and Fayard, A.L., 2023. “Collaborating” with AI: Taking a system view to explore the future of work. Organization Science, 34(5), pp.1672-1694.
[31] Wang, X., Sun, Y. and Ding, D., 2022. Adaptive dynamic programming for networked control systems under communication constraints: A survey of trends and techniques. International Journal of Network Dynamics and Intelligence, pp.85-98.
[32] Macenski, S., Foote, T., Gerkey, B., Lalancette, C. and Woodall, W., 2022. Robot operating system 2: Design, architecture, and uses in the wild. Science robotics, 7(66), p.eabm6074.
[33] Niso, G., Botvinik-Nezer, R., Appelhoff, S., De La Vega, A., Esteban, O., Etzel, J.A., Finc, K., Ganz, M., Gau, R., Halchenko, Y.O. and Herholz, P., 2022. Open and reproducible neuroimaging: From study inception to publication. NeuroImage, 263, p.119623.
[34] Popo-Olaniyan, O., James, O.O., Udeh, C.A., Daraojimba, R.E. and Ogedengbe, D.E., 2022. Review of advancing US innovation through collaborative hr ecosystems: a sector-wide perspective. International Journal of Management & Entrepreneurship Research, 4(12), pp.623-640.
[35] Keshvarparast, A., Battini, D., Battaia, O. and Pirayesh, A., 2024. Collaborative robots in manufacturing and assembly systems: literature review and future research agenda. Journal of Intelligent Manufacturing, 35(5), pp.2065-2118.
[36] Criollo-C, S., Guerrero-Arias, A., Jaramillo-Alcázar, Á. and Luján-Mora, S., 2021. Mobile learning technologies for education: Benefits and pending issues. Applied Sciences, 11(9), p.4111.
[37] Javed, A.R., Sarwar, M.U., Beg, M.O., Asim, M., Baker, T. and Tawfik, H., 2020. A collaborative healthcare framework for shared healthcare plan with ambient intelligence. Human-centric Computing and Information Sciences, 10(1), p.40.
[38] Alzahrani, N.M., 2020. Augmented reality: A systematic review of its benefits and challenges in e-learning contexts. Applied Sciences, 10(16), p.5660.
[39] Jaskó, S., Skrop, A., Holczinger, T., Chován, T. and Abonyi, J., 2020. Development of manufacturing execution systems in accordance with Industry 4.0 requirements: A review of standard-and ontology-based methodologies and tools. Computers in industry, 123, p.103300.
[40] Hosen, M.S., Islam, R., Naeem, Z., Folorunso, E.O., Chu, T.S., Al Mamun, M.A. and Orunbon, N.O., 2024. Data-Driven Decision Making: Advanced Database Systems for Business Intelligence. Nanotechnology Perceptions, pp.687-704.
[41] Salam, M. and Farooq, M.S., 2020. Does sociability quality of web-based collaborative learning information system influence students’ satisfaction and system usage?. International Journal of Educational Technology in Higher Education, 17(1), p.26.
[42] Yu, Y., Zhang, J.Z., Cao, Y. and Kazancoglu, Y., 2021. Intelligent transformation of the manufacturing industry for Industry 4.0: Seizing financial benefits from supply chain relationship capital through enterprise green management. Technological Forecasting and Social Change, 172, p.120999.
[43] Rossoni, A.L., de Vasconcellos, E.P.G. and de Castilho Rossoni, R.L., 2024. Barriers and facilitators of university-industry collaboration for research, development and innovation: a systematic review. Management Review Quarterly, 74(3), pp.1841-1877.
[44] Vacca, A., Di Sorbo, A., Visaggio, C.A. and Canfora, G., 2021. A systematic literature review of blockchain and smart contract development: Techniques, tools, and open challenges. Journal of Systems and Software, 174, p.110891.
[45] Zhang, A.X., Muller, M. and Wang, D., 2020. How do data science workers collaborate? roles, workflows, and tools. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW1), pp.1-23.
[46] Dolgui, A. and Ivanov, D., 2022. 5G in digital supply chain and operations management: fostering flexibility, end-to-end connectivity and real-time visibility through internet-of-everything. International Journal of Production Research, 60(2), pp.442-451.
[47] Agomuo, O.C., Jnr, O.W.B. and Muzamal, J.H., 2024, July. Energy-Aware AI-based Optimal Cloud Infra Allocation for Provisioning of Resources. In 2024 IEEE/ACIS 27th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 269-274). IEEE.
[48] Xia, X., Chen, F., He, Q., Grundy, J., Abdelrazek, M. and Jin, H., 2020. Online collaborative data caching in edge computing. IEEE Transactions on Parallel and Distributed Systems, 32(2), pp.281-294.
[49] Bradley, V.M., 2021. Learning Management System (LMS) use with online instruction. International Journal of Technology in Education, 4(1), pp.68-92.
[50] Maddikunta, P.K.R., Pham, Q.V., Prabadevi, B., Deepa, N., Dev, K., Gadekallu, T.R., Ruby, R. and Liyanage, M., 2022. Industry 5.0: A survey on enabling technologies and potential applications. Journal of industrial information integration, 26, p.100257.
[2] Qian, C., Cong, X., Yang, C., Chen, W., Su, Y., Xu, J., Liu, Z. and Sun, M., 2023. Communicative agents for software development. arXiv preprint arXiv:2307.07924, 6.
[3] Belzner, L., Gabor, T. and Wirsing, M., 2023, October. Large language model assisted software engineering: prospects, challenges, and a case study. In International Conference on Bridging the Gap between AI and Reality (pp. 355-374). Cham: Springer Nature Switzerland.
[4] Fraiwan, M. and Khasawneh, N., 2023. A review of chatgpt applications in education, marketing, software engineering, and healthcare: Benefits, drawbacks, and research directions. arXiv preprint arXiv:2305.00237.
[5] Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S. and Zhang, J.M., 2023, May. Large language models for software engineering: Survey and open problems. In 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE) (pp. 31-53). IEEE.
[6] Van, L.P., Do Chi, K. and Duc, T.N., 2023. Review of hydrogen technologies based microgrid: Energy management systems, challenges and future recommendations. International Journal of Hydrogen Energy, 48(38), pp.14127-14148.
[7] Tajjour, S. and Chandel, S.S., 2023. A comprehensive review on sustainable energy management systems for optimal operation of future-generation of solar microgrids. Sustainable Energy Technologies and Assessments, 58, p.103377.
[8] Qian, C., Liu, W., Liu, H., Chen, N., Dang, Y., Li, J., Yang, C., Chen, W., Su, Y., Cong, X. and Xu, J., 2024, August. Chatdev: Communicative agents for software development. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 15174-15186).
[9] Gokarna, M. and Singh, R., 2021, February. DevOps: a historical review and future works. In 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) (pp. 366-371). IEEE.
[10] Al-Saqqa, S., Sawalha, S. and AbdelNabi, H., 2020. Agile software development: Methodologies and trends. International Journal of Interactive Mobile Technologies, 14(11).
[11] Ford, D., Storey, M.A., Zimmermann, T., Bird, C., Jaffe, S., Maddila, C., Butler, J.L., Houck, B. and Nagappan, N., 2021. A tale of two cities: Software developers working from home during the covid-19 pandemic. ACM Transactions on Software Engineering and Methodology (TOSEM), 31(2), pp.1-37.
[12] Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J. and Wang, H., 2023. Large language models for software engineering: A systematic literature review. ACM Transactions on Software Engineering and Methodology.
[13] Dong, Y., Jiang, X., Jin, Z. and Li, G., 2024. Self-collaboration code generation via chatgpt. ACM Transactions on Software Engineering and Methodology, 33(7), pp.1-38.
[14] Rasul, T., Nair, S., Kalendra, D., Robin, M., de Oliveira Santini, F., Ladeira, W.J., Sun, M., Day, I., Rather, R.A. and Heathcote, L., 2023. The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching, 6(1), pp.41-56.
[15] Molnár, G., József, C. and Éva, K., 2023, January. Evaluation and technological solutions for a dynamic, unified cloud programming development environment: Ease of use and applicable system for uniformized practices and assessments. In 2023 IEEE 21st World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 000237-000240). IEEE.
[16] Iwanaga, T., Usher, W. and Herman, J., 2022. Toward SALib 2.0: Advancing the accessibility and interpretability of global sensitivity analyses. Socio-Environmental Systems Modelling, 4, pp.18155-18155.
[17] Bordeleau, F., Combemale, B., Eramo, R., Van Den Brand, M. and Wimmer, M., 2020. Towards model-driven digital twin engineering: Current opportunities and future challenges. In Systems Modelling and Management: First International Conference, ICSMM 2020, Bergen, Norway, June 25–26, 2020, Proceedings 1 (pp. 43-54). Springer International Publishing.
[18] Sahay, A., Indamutsa, A., Di Ruscio, D. and Pierantonio, A., 2020, August. Supporting the understanding and comparison of low-code development platforms. In 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) (pp. 171-178). IEEE.
[19] Pelluru, K., 2021. Integrate security practices and compliance requirements into DevOps processes. MZ Computing Journal, 2(2), pp.1-19.
[20] Bello, S.A., Oyedele, L.O., Akinade, O.O., Bilal, M., Delgado, J.M.D., Akanbi, L.A., Ajayi, A.O. and Owolabi, H.A., 2021. Cloud computing in construction industry: Use cases, benefits and challenges. Automation in Construction, 122, p.103441.
[21] Ross, S.I., Martinez, F., Houde, S., Muller, M. and Weisz, J.D., 2023, March. The programmer’s assistant: Conversational interaction with a large language model for software development. In Proceedings of the 28th International Conference on Intelligent User Interfaces (pp. 491-514).
[22] Ahdida, C., Bozzato, D., Calzolari, D., Cerutti, F., Charitonidis, N., Cimmino, A., Coronetti, A., D’Alessandro, G.L., Donadon Servelle, A., Esposito, L.S. and Froeschl, R., 2022. New capabilities of the FLUKA multi-purpose code. Frontiers in Physics, 9, p.788253.
[23] Krauß, V., Boden, A., Oppermann, L. and Reiners, R., 2021, May. Current practices, challenges, and design implications for collaborative AR/VR application development. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-15).
[24] Anzt, H., Bach, F., Druskat, S., Löffler, F., Loewe, A., Renard, B.Y., Seemann, G., Struck, A., Achhammer, E., Aggarwal, P. and Appel, F., 2021. An environment for sustainable research software in Germany and beyond: current state, open challenges, and call for action. F1000Research, 9, p.295.
[25] Al-Heety, O.S., Zakaria, Z., Ismail, M., Shakir, M.M., Alani, S. and Alsariera, H., 2020. A comprehensive survey: Benefits, services, recent works, challenges, security, and use cases for sdn-vanet. IEEE Access, 8, pp.91028-91047.
[26] Dusdal, J. and Powell, J.J., 2021. Benefits, motivations, and challenges of international collaborative research: A sociology of science case study. Science and Public Policy, 48(2), pp.235-245.
[27] Marion, T.J. and Fixson, S.K., 2021. The transformation of the innovation process: How digital tools are changing work, collaboration, and organizations in new product development. Journal of Product Innovation Management, 38(1), pp.192-215.
[28] Wang, X., Sun, Y. and Ding, D., 2022. Adaptive dynamic programming for networked control systems under communication constraints: A survey of trends and techniques. International Journal of Network Dynamics and Intelligence, pp.85-98.
[29] Dusdal, J. and Powell, J.J., 2021. Benefits, motivations, and challenges of international collaborative research: A sociology of science case study. Science and Public Policy, 48(2), pp.235-245.
[30] Anthony, C., Bechky, B.A. and Fayard, A.L., 2023. “Collaborating” with AI: Taking a system view to explore the future of work. Organization Science, 34(5), pp.1672-1694.
[31] Wang, X., Sun, Y. and Ding, D., 2022. Adaptive dynamic programming for networked control systems under communication constraints: A survey of trends and techniques. International Journal of Network Dynamics and Intelligence, pp.85-98.
[32] Macenski, S., Foote, T., Gerkey, B., Lalancette, C. and Woodall, W., 2022. Robot operating system 2: Design, architecture, and uses in the wild. Science robotics, 7(66), p.eabm6074.
[33] Niso, G., Botvinik-Nezer, R., Appelhoff, S., De La Vega, A., Esteban, O., Etzel, J.A., Finc, K., Ganz, M., Gau, R., Halchenko, Y.O. and Herholz, P., 2022. Open and reproducible neuroimaging: From study inception to publication. NeuroImage, 263, p.119623.
[34] Popo-Olaniyan, O., James, O.O., Udeh, C.A., Daraojimba, R.E. and Ogedengbe, D.E., 2022. Review of advancing US innovation through collaborative hr ecosystems: a sector-wide perspective. International Journal of Management & Entrepreneurship Research, 4(12), pp.623-640.
[35] Keshvarparast, A., Battini, D., Battaia, O. and Pirayesh, A., 2024. Collaborative robots in manufacturing and assembly systems: literature review and future research agenda. Journal of Intelligent Manufacturing, 35(5), pp.2065-2118.
[36] Criollo-C, S., Guerrero-Arias, A., Jaramillo-Alcázar, Á. and Luján-Mora, S., 2021. Mobile learning technologies for education: Benefits and pending issues. Applied Sciences, 11(9), p.4111.
[37] Javed, A.R., Sarwar, M.U., Beg, M.O., Asim, M., Baker, T. and Tawfik, H., 2020. A collaborative healthcare framework for shared healthcare plan with ambient intelligence. Human-centric Computing and Information Sciences, 10(1), p.40.
[38] Alzahrani, N.M., 2020. Augmented reality: A systematic review of its benefits and challenges in e-learning contexts. Applied Sciences, 10(16), p.5660.
[39] Jaskó, S., Skrop, A., Holczinger, T., Chován, T. and Abonyi, J., 2020. Development of manufacturing execution systems in accordance with Industry 4.0 requirements: A review of standard-and ontology-based methodologies and tools. Computers in industry, 123, p.103300.
[40] Hosen, M.S., Islam, R., Naeem, Z., Folorunso, E.O., Chu, T.S., Al Mamun, M.A. and Orunbon, N.O., 2024. Data-Driven Decision Making: Advanced Database Systems for Business Intelligence. Nanotechnology Perceptions, pp.687-704.
[41] Salam, M. and Farooq, M.S., 2020. Does sociability quality of web-based collaborative learning information system influence students’ satisfaction and system usage?. International Journal of Educational Technology in Higher Education, 17(1), p.26.
[42] Yu, Y., Zhang, J.Z., Cao, Y. and Kazancoglu, Y., 2021. Intelligent transformation of the manufacturing industry for Industry 4.0: Seizing financial benefits from supply chain relationship capital through enterprise green management. Technological Forecasting and Social Change, 172, p.120999.
[43] Rossoni, A.L., de Vasconcellos, E.P.G. and de Castilho Rossoni, R.L., 2024. Barriers and facilitators of university-industry collaboration for research, development and innovation: a systematic review. Management Review Quarterly, 74(3), pp.1841-1877.
[44] Vacca, A., Di Sorbo, A., Visaggio, C.A. and Canfora, G., 2021. A systematic literature review of blockchain and smart contract development: Techniques, tools, and open challenges. Journal of Systems and Software, 174, p.110891.
[45] Zhang, A.X., Muller, M. and Wang, D., 2020. How do data science workers collaborate? roles, workflows, and tools. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW1), pp.1-23.
[46] Dolgui, A. and Ivanov, D., 2022. 5G in digital supply chain and operations management: fostering flexibility, end-to-end connectivity and real-time visibility through internet-of-everything. International Journal of Production Research, 60(2), pp.442-451.
[47] Agomuo, O.C., Jnr, O.W.B. and Muzamal, J.H., 2024, July. Energy-Aware AI-based Optimal Cloud Infra Allocation for Provisioning of Resources. In 2024 IEEE/ACIS 27th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 269-274). IEEE.
[48] Xia, X., Chen, F., He, Q., Grundy, J., Abdelrazek, M. and Jin, H., 2020. Online collaborative data caching in edge computing. IEEE Transactions on Parallel and Distributed Systems, 32(2), pp.281-294.
[49] Bradley, V.M., 2021. Learning Management System (LMS) use with online instruction. International Journal of Technology in Education, 4(1), pp.68-92.
[50] Maddikunta, P.K.R., Pham, Q.V., Prabadevi, B., Deepa, N., Dev, K., Gadekallu, T.R., Ruby, R. and Liyanage, M., 2022. Industry 5.0: A survey on enabling technologies and potential applications. Journal of industrial information integration, 26, p.100257.
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Sep 15, 2019
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Sagar Vishnubhai Sheta. (2019). THE ROLE AND BENEFITS OF VERSION CONTROL SYSTEMS IN COLLABORATIVE SOFTWARE DEVELOPMENT. Journal of Population Therapeutics and Clinical Pharmacology, 26(3), 61-76. https://doi.org/10.53555/hxn1xq28
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Author Biography
Sagar Vishnubhai Sheta
Software Developer, 3 Desire Network & solutions india pvt. Ltd, india
How to Cite
Sagar Vishnubhai Sheta. (2019). THE ROLE AND BENEFITS OF VERSION CONTROL SYSTEMS IN COLLABORATIVE SOFTWARE DEVELOPMENT. Journal of Population Therapeutics and Clinical Pharmacology, 26(3), 61-76. https://doi.org/10.53555/hxn1xq28