Amit Tyagi, Pramod Kumar, and Vikas Kumar
Shri Ram College, Muzaffarnagar, U. P., India,
Received: Oct 15, 2023/ Revised: Oct 30, 2023/Accepted: Nov 18, 2023
Abstract
Natural Language Processing (NLP) has made significant strides in recent years, revolutionizing various domains including software development. One such application is in code generation, where NLP techniques are leveraged to translate human-readable natural language specifications into executable code. This paper provides a comprehensive overview of the integration of NLP in code generation, exploring its techniques, challenges, implications, and future directions. Through an in-depth analysis of existing literature and case studies, this paper aims to elucidate the transformative potential of NLP in revolutionizing traditional coding paradigms. Natural Language Processing (NLP) has emerged as a transformative technology with applications spanning various domains, including software development. This paper presents a comprehensive overview of the integration of NLP techniques in code generation processes, elucidating its significance, methodologies, challenges, and future directions. By analyzing existing literature and case studies, the paper explores how NLP facilitates the translation of human-readable natural language requirements into executable code, thereby enhancing developer productivity and streamlining software development. Additionally, it discusses the implications of NLP-driven code generation on the software engineering landscape, including the role of machine learning and deep learning models in improving accuracy and efficiency. Furthermore, ethical considerations and potential challenges are addressed, highlighting the need for responsible deployment and mitigation of biases. This paper aims to unveil the transformative power of NLP in revolutionizing traditional coding paradigms and shaping the future of software engineering.
Keywords: Natural Language Processing, Code Generation, Machine Learning, Deep Learning, Software Engineering, Automation, Neural Language Models, Transformer Architectures, Ethical Considerations.
References
Bhattacharyya, (2012). “Natural Language Processing: A Perspective from Computation in Presence of Ambiguity, Resource Constraint and Multilinguality”, CSI Journal of Computing, 1(2).
Sohom Ghosh, (2019). Dwight Gunning, “Natural Language Processing Fundamentals’, Packt Publishing.
AbuShawar and E. Atwell, (2016). ‘Usefulness, localizability, humanness, and language-benefit: Additional evaluation criteria for natural language dialogue systems,’ International Journal of Speech Technology, vol. 19, no. 2, pp. 373–383.
Sourav Mandal, Sudip Kumar Naskar, (2017). ‘Natural Language Programing with Automatic Code Generation towards Solving Addition-Subtraction Word Problems”, Conference: 14th International Conference on Natural Language ProcessingAt: Jadavpur University.
Wermter, E. Riloff, and G. Scheler, (1996). Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing, Springer: Berlin.
Balgasem and L. Q. Zakaria, (2017). “A hybrid method of rule-based approach and statistical measures for recognizing narrators name in hadith,” in 2017 6th International Conference on Electrical Engineering and Informatics (ICEEI), IEEE, pp. 1–5.
Tyagi, A., Kumar, P. and Kumar, V. (2023). Unveiling the Power of Natural Language Processing in Code Generation: A Comprehensive Overview. Science Archives, Vol. 4(4), 313-323. https://doi.org/10.47587/SA.2023.4412
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