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BackGen—Backend Generator

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ICT Analysis and Applications (ICT4SD 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 782))

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Abstract

BackGen, Backend Generator, is a feature-rich software tool that automates the process of writing backend code for web applications. The purpose of this tool is to simplify and accelerate the development process, reducing the time and effort required to create a working backend code, while improving the consistency and maintainability of the resulting code. BackGen helps in creating a structure for data models and RESTful API endpoints, by generating the executable code for the same in Golang. The generation of backend code can be automated, freeing developers to concentrate more on other important features of their project. We test our approach by producing a backend for an application and contrasting the outcomes with manual implementation. This chapter will explore how BackGen works, as well as the potential applications of this tool in the field of web development.

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References

  1. Swagger Codegen (2023) Swagger-api/swagger-codegen: swagger-codegen contains a template-driven engine to generate documentation, API clients and server stubs in different languages by parsing your OpenAPI/Swagger definition. https://github.com/swagger-api/swagger-codegen

  2. Postman (2023). https://www.postman.com

  3. {{ mustache }}. Mustache. https://mustache.github.io

  4. Uyanik B, Şahin VH (2020) A template-based code generator for web applications. Turk J Electr Eng Comput Sci 28(3), Article 37. https://doi.org/10.3906/elk-1910-44

  5. Ullah I, Inayat I (2022) Template-based automatic code generation for web application and APIs using class diagram. In: 2022 International conference on frontiers of information technology (FIT). Islamabad, Pakistan, pp 332–337. https://doi.org/10.1109/FIT57066.2022.00067

  6. The Go Programming Language Specification—The Go Programming Language. Go. https://go.dev/ref/spec

  7. Akbulut A, Patlar Akbulut F, Köseokur H, Çatal Ç (2017) Design and implementation of an automatic code generation tool for end-user development. Dokuz Eylül Univ Faculty Eng J Sci Eng 19(55.1 Special Issue):76–88 (in Turkish with an abstract in English). https://doi.org/10.21205/deufmd.2017195532

  8. Hu K, Duan Z, Wang J, Gao L, Shang L (2019) Template-based AADL automatic code generation. Front Comp Sci 13(4):698–714. https://doi.org/10.1007/s11704-017-6477-y

    Article  Google Scholar 

  9. Jörges S (2013) The state of the art in code generation. In: Jörges S (ed) Construction and evolution of code generators: a model-driven and service-oriented approach. Springer, Berlin, Germany, pp 11–38

    Chapter  Google Scholar 

  10. Domínguez E, Pérez B, Rubio ÁL, Zapata MA (2012) A systematic review of code generation proposals from state machine specifications. Inf Softw Technol 54(10):1045–1066. https://doi.org/10.1016/j.infsof.2012.04.008

    Article  Google Scholar 

  11. Mehmood A, Jawawi DNA (2013) Aspect-oriented model-driven code generation: a systematic mapping study. Inf Softw Technol 55(2):395–411. https://doi.org/10.1016/j.infsof.2012.09.003

    Article  Google Scholar 

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Acknowledgements

We express our sincere appreciation and heartfelt thanks to Dr. Darshan Ingle for his invaluable guidance and assistance. We are deeply grateful for his mentorship and unwavering oversight, as well as for his provision of essential project information. Our principal, Dr. G.T. Thampi, also deserves our utmost gratitude for his encouragement and unwavering support. We take this chance to acknowledge the contributions of those who played a vital role in the successful completion of the project.

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Correspondence to Darshan Rander .

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Rander, D., Dani, P., Panjwani, D., Ingle, D. (2023). BackGen—Backend Generator. In: Fong, S., Dey, N., Joshi, A. (eds) ICT Analysis and Applications. ICT4SD 2023. Lecture Notes in Networks and Systems, vol 782. Springer, Singapore. https://doi.org/10.1007/978-981-99-6568-7_34

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