Recent Trends in Programming Languages: Navigating the Evolving Landscape

Year : 2024 | Volume : 11 | Issue : 03 | Page : 10 20
    By

    Ushaa Eswaran,

  1. Principal and Professor, Department of Electrical Communication Engineering, Mahalakshmi Tech Campus (Affiliated to Anna University), Chennai, Tamil Nadu, India

Abstract

The programming language landscape is constantly changing, driven by technological progress, evolving developer preferences, and the growing complexity of today’s applications. As software development becomes more sophisticated, the demand for programming languages that can accommodate various paradigms and streamline workflows has intensified. This paper explores recent trends in programming languages, focusing on significant innovations in language design, the emergence of new paradigms, and the transformative impact of technologies such as artificial intelligence and machine learning. We examine the rise of multi-paradigm languages that blend functional, object-oriented, and procedural programming styles, enabling developers to select the most effective approach for their specific projects. The paper also discusses the growing significance of domain-specific languages (DSLs) that cater to particular problem domains, facilitating more efficient and specialized development processes. We also emphasize the importance of artificial intelligence (AI) powered development tools that boost efficiency by automating routine coding tasks and offering smart recommendations. Through a detailed analysis of popular languages, their respective use cases, and the broader implications of these trends, we provide insights into the future directions of programming languages in software development. This exploration not only emphasizes the current landscape but also anticipates how ongoing innovations will shape the way developers create, maintain, and evolve software in an increasingly complex digital environment.

Keywords: Programming languages, software development, multi-paradigm languages, domain-specific languages, functional programming, artificial intelligence, language design, developer experience, technological advancements

[This article belongs to Recent Trends in Programming languages ]

How to cite this article:
Ushaa Eswaran. Recent Trends in Programming Languages: Navigating the Evolving Landscape. Recent Trends in Programming languages. 2024; 11(03):10-20.
How to cite this URL:
Ushaa Eswaran. Recent Trends in Programming Languages: Navigating the Evolving Landscape. Recent Trends in Programming languages. 2024; 11(03):10-20. Available from: https://journals.stmjournals.com/rtpl/article=2024/view=180927


References

  1. Rocha A, Sousa L, Alves M, Sousa A. The underlying potential of NLP for microcontroller programming education. Comput Appl Eng Educ. 2024;32:e22778. DOI: 10.1002/cae.22778.
  2. Joel S, Wu JJ, Fard FH. A survey on LLM-based code generation for low-resource and domain-specific programming languages. [Preprint] ArXiv. 2024;2410.03981. DOI: 10.48550/arXiv.2410.03981.
  3. Khaleel M, Jebrel A, Shwehdy DM. Artificial Intelligence in Computer Science. Int J Electr Eng Sustain. 2024;2(2):1–21. DOI: 10.5281/zenodo.10937515.
  4. Devineni SK. Version Control Systems (VCS): the pillars of modern software development: Analyzing the past, present, and anticipating future trends. Int J Sci Res. 2020 Dec;9(12):1816-1829. DOI: 10.21275/SR24127210817.
  5. Vovk O, Pashis L. Multi-paradigm cognition: philosophical foundations. Visn Cherkaskogo Natsionalnogo Universytetu Imena Bohdana Khmelnytskoho. Seriia Pedahohichni Nauky [Ukrainian]. 2024;5-11. DOI: 10.31651/2524-2660-2024-2-5-11.
  6. Elliott E. Composing Software: An Exploration of Functional Programming and Object Composition in JavaScript. Birmingham, United Kingdom: Packt Publishing Ltd.; 2024.
  7. Floor D. Code comprehension in the multi-paradigm environment Kotlin. [Master’s Thesis]. Department of Computer Science, University of Twente; 2024.
  8. Applis LH. Tool-Driven Quality Assurance for Functional Programming and Machine Learning. 2024. 206 p. DOI: 10.4233/uuid:4d048249-e59d-4a82-9e11-714b2b25163f.
  9. Tomov NR. Extensibility of domain-specific languages: A case study of an industrial DSL. [master’s thesis]. University of Twente; 2024.
  10. Daniel F, Matera M. Web technologies. In: Mashups: Concepts, Models and Architectures. Springer Berlin Heidelberg; 2014. p. 41–69. DOI: 10.1007/978-3-642-55049-2_3.
  11. Rabiser R, Thanhofer-Pilisch J, Vierhauser M, Grünbacher P, Egyed A. Developing and evolving a DSL-based approach for runtime monitoring of systems of systems. Autom Softw Eng. 2018;25:875–915. DOI: 10.1007/s10515-018-0241-x.
  12. Paczona M, Mayr HC, Prochart G. Increase development productivity by domain-specific conceptual modeling. Data Knowl Eng. 2024;150:102263. DOI: 10.1016/j.datak.2023.102263.
  13. Kumar J, Chimalakonda S. What do developers feel about fast-growing programming languages? An exploratory study. Proc 32nd IEEE/ACM Int Conf Program Comprehension. 2024;178–89. DOI: 10.1145/3643916.3644422.

Regular Issue Subscription Review Article
Volume 11
Issue 03
Received 23/10/2024
Accepted 26/10/2024
Published 05/11/2024


Login


My IP

PlumX Metrics