Recent Trends in Programming languages
Computer/IT ISSN 2455-1821 3 issues a year Hybrid open accessWhat does hybrid open access mean? This journal has two kinds of articles. Open access articles are free for anyone to read; the author pays an article processing charge for that. All other articles are for subscribers. Authors can also publish at no cost under the subscription-based option.
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About the journal
Journal metrics
Counted from this archive, not supplied by anyone.
- 187Articles published
- 10Published in 2026
- 449Authors
- 2Open access
Journal information
- Title
- Recent Trends in Programming languages
- Issues per year
- 3 issues
- ISSN
- 2455-1821
- Publisher
- STM Journals
- DOI
- 10.37591/RTPL
- Starting year
- 2014
- Subject
- Computer/IT
- Publication format
- Hybrid open access
- Language
- English
- Copyright policy
- CC BY-NC-ND 4.0
- Type
- Peer-reviewed journal (refereed)
- Article processing charge
- ₹10,000 in India · $400 outside India
- Subscription-based publication
- No cost. Author copy, DOI and certificate are chargeable.
Indexed in
Editorial board
-
Editor-in-Chief
Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, India
Latest articles
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Programming Languages for Robotics: A Comprehensive Review of Applications, Features, and System Integration
Abstract: The rapid advancement of industrial automation, artificial intelligence, and autonomous systems has increased the importance of effective programming approaches for modern robotic applications. Robot programming languages provide the computational foundation for controlling robotic motion, interacting with sensors and actuators, implementing autonomous operations, and developing simulation-based solutions. This paper presents a comparative review of major programming languages used in robotics, with particular emphasis on C/C++, Python, Java, C#/.NET, and MATLAB. The …
Published in Recent Trends in Programming languages · Vol. 13, Issue 2, 2026 · pp. 51–62 Read article
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AI-Assisted Programming, Memory Safety, Multi-Paradigm Design and Emerging Development Models
Abstract: As the field of artificial intelligence (AI) continues to evolve at a fast pace, and specifically with the rise of large language models (LLMs), the software design, implementation, testing, and maintenance landscape has undergone unprecedented transformation. Although programming languages are still the main language for writing computational logic, the way developers interact with programming languages is now increasingly mediated by AI-based tools. Current AI coding systems can help with code …
Published in Recent Trends in Programming languages · Vol. 13, Issue 2, 2026 · pp. 43–50 Read article
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LLM-Assisted Programming: Trends, Evaluation Gaps, and Software Assurance
Abstract: Large language models (LLMs) are increasingly used for code generation, software testing, program repair, and security analysis. However, their evaluation still relies heavily on functional benchmarks that often focus on small, isolated programming tasks. Such benchmarks provide limited insight into security, test quality, repository-level dependencies, and maintainability. This study examines how the evaluation of LLM-assisted programming has developed across these application areas. The literature shows a gradual shift from function-level …
Published in Recent Trends in Programming languages · Vol. 13, Issue 2, 2026 · pp. 28–42 Read article
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Bug Tracking and Resolution System: A Rule-Based and Python-Driven Approach
Abstract: The way things operate and how they will age from a performance and user interaction standpoint, what types of loads they can handle, and how maintenance will be needed in the future are all connected to how working out glitches affects the future and how it feels to the users. Many older systems record problems, track the steps taken to resolve them, and provide very little in the way of …
Published in Recent Trends in Programming languages · Vol. 13, Issue 2, 2026 · pp. 17–27 Read article
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AI-Native Programming Languages: Reconsidering Programming Language Selection in the Era of Generative AI and Autonomous Coding
Abstract: The role of programming languages is undergoing a significant transformation as generative artificial intelligence becomes integrated into everyday software development. Programming is gradually moving from an activity dominated by manual source code production toward a collaborative environment in which developers work with large language models, intelligent coding assistants, automated testing systems, and increasingly autonomous software agents. This transformation introduces a new dimension to programming language selection. Historically, programming languages have …
Published in Recent Trends in Programming languages · Vol. 13, Issue 2, 2026 · pp. 1–16 Read article
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A Comprehensive Study of Natural Language Processing Systems Using Modern Programming Languages: Techniques, Architectures, Experimental Evaluation and Applications
Abstract: Natural language processing is a key field of study within artificial intelligence that focuses on enabling machines to understand and work with human language. This is because there is much digital text data everywhere. Natural Language Processing is what this study is about. It looks at new ways of doing Natural Language Processing. The old ways are like machine learning, and the new ways are like learning. This study compares …
Published in Recent Trends in Programming languages · Vol. 13, Issue 1, 2026 · pp. 12–23 Read article
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Emerging Programming Languages Shaping Technology in 2026
Abstract: As technology keeps changing quickly, it’s more crucial than ever for developers, students, and tech fans to choose the correct programming language. This article talks about the best programming languages to learn in 2026, focusing on their most important features, how they can be used in the real world, and how much demand there will be for them in the job market in the future. Each language is looked at …
Published in Recent Trends in Programming languages · Vol. 13, Issue 1, 2026 · pp. 24–34 Read article
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Comparison of Models of Machine Learning and Hyperparameter Optimization Methods on Various Datasets
Abstract: The most likely phase in achieving powerful and robust machine learning models is probably the hyperparameter tuning step. The traditional exhaustive methods of search (grid search and others) ensure that the search space is covered, but are computationally inexpensive; random search is less expensive and can still miss good regions; and lastly, the modern model-based and population-based methods (Bayesian optimization, tree-structured Parzen estimator (TPE), genetic algorithms) are thought to provide …
Published in Recent Trends in Programming languages · Vol. 13, Issue 1, 2026 · pp. 35–42 Read article
Issue highlights
What is in our recent issues, in plain words, with a pointer to each paper.
- Programming in the Age of AI: Recent Trends in Programming Languages 13(2) Volume 13, Issue 2
Archive on its own page →
Volume 13 (2026) 10 articles
- Issue 2 5 articles Highlights
- Issue 1 5 articles