Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63543
Volume:
10 |
5 |
May 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master Of Computer Application
Bapuji Institute of Engineering and Technology
Master Of Computer Application
Bapuji Institute of Engineering and Technology
These modern software development trends, which prioritize flexibility and speed, do work well with some programming languages. But using problem statements to write code is a function that only requires knowledge of logical ideas, not programming jargon. Conventional software development processes are more expensive and time-consuming because they use manual programming techniques to implement common best practices.An intelligent synthesis system that can automatically produce executable source code in response to natural language commands related to many computer programming languages is made possible by this project. Furthermore, the development of software requirements has been translated into common language and is not constrained by grammar or language.The current trends in software development, which prioritize speed and variety, support certain programming languages. However, using problem statements to implement code is a function that simply requires an understanding of logical principles and programming terminology. Common procedures in the traditional software development process are carried out using manual programming techniques, which increases their expense and duration.Significantly, the project enables the use of an intelligent synthesis system that can independently generate executable source code in response to commands pertaining to several computer programming languages. It is possible to create software requirements that are converted into everyday English without any restrictions on syntax or language.
KEYWORDS
Accurate Natural Language Understanding The system can interpret both simple and complex natural language inputs with high accuracy and extract meaningful intent from user queries.Multi-Language Code Generation It consistently generates executable code in several programming languages, maintaining logical consistency across languages. Syntax and Logic Validation Built-in validation ensures that generated code is free from syntactic errors and meets expected execution outcomes. InteractiveRefinement Users can refine, modify, or re-generate code using natural language instructions, improving the flexibility and usability. Secure Execution Environment By using sandboxing safely executes user-generated code without compromising system security. Educational Utility The system line-by-line explanations of generated code, aiding students and beginners.Scalable Architecture A microservice-based backend employs a modular architecture, easily scalable to meet demand with more users, and is set for feature development in the future
1. Sommerville, I. (2016). Software Engineering (10th ed.). Pearson Education. — Standard reference for software testing, validation, and verification. 2. Myers, G. J., Sandler, C., & Badgett, T. (2011). The Art of Software Testing (3rd ed.). Wiley. — Core reference for testing strategies and quality assurance. 3. Chen, M., Tworek, J., Jun, H., et al. (2021). Evaluating Large Language Models Trained on Code. ArXiv preprint arXiv:2107.03374. — Relevant for AI-based code generation and evaluation. 4. Yin, P., & Neubig, G. (2017). A Syntactic Neural Model for General-Purpose Code Generation. Proceedings of ACL. — Focuses on NL-to-code synthesis models. 5. Ahmad, W. U., Chakraborty, S., Ray, B., & Chang, K. (2021). Unified Pre-training for Program Understanding and Generation.NAACL-HLT. — Useful for AI-driven program synthesis systems. 6. IEEE Computer Society. (2014). IEEE Standard for Software Testing (IEEE 829 / ISO/IEC/IEEE 29119). — Official testing documentation standard. 7. OpenAI. (2023). Code Generation and Function — Reference for modern AI-based code generation systems. 8. Pressman, R. S., & Maxim, B. R. (2019). Software Engineering: A Practitioner’s Approach. McGraw-Hill. — Covers testing in intelligent and complex syst
Publication Date
5/22/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
2
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...