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Journal articles on the topic 'Code generation softwares'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Monteiro, Cristiano Martins, Flavianne Braga Campos de Lima, and Carlos Renato Storck. "GERADOR DE CÓDIGOS PARA O DESENVOLVIMENTO DE APLICAÇÕES WEB A PARTIR DA MODELAGEM ENTIDADE-RELACIONAMENTO." e-xacta 9, no. 1 (2016): 37. http://dx.doi.org/10.18674/exacta.v9i1.1663.

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<p>A geração automática de código-fonte é uma prática adotada no desenvolvimento de softwares para agilizar, facilitar e padronizar a implementação dos projetos. Embora seja uma prática comum nas fábricas de software, não se conhece uma ferramenta que permita escolher o padrão de projeto a ser usado. O objetivo principal deste trabalho é apresentar um gerador de códigos para o desenvolvimento de sistemas Web a partir de uma modelagem entidade-relacionamento, uma linguagem de programação e um padrão de projeto determinados pelo usuário. Os objetivos específicos são propor uma arquitetura

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Quispe, Luis, and Wilfredo Ulloa. "Application of BIM Tools in Parametric and Generative Design for the Conception of Complex Structures." IOP Conference Series: Materials Science and Engineering 1203, no. 2 (2021): 022070. http://dx.doi.org/10.1088/1757-899x/1203/2/022070.

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Abstract The present research applies the Parametric Design (PD) and Generative Design (GD) for the generation of complex structures, through the BIM methodology, being implemented in design phase of a new modern proposal for Pavilion J1 of the National University of Engineering from Perú. The research aims to: Study the PD and GD considering the interoperability provided by BIM tools, propose procedures that help solve PD and GD problems, understand the benefits of process automation through generative and parametric algorithms. The conception and design phase of projects are developed in a t

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Alibasa, Muhammad Johan, Rizka Widyarini Purwanto, Yudi Priyadi, and Rosa Reska Riskiana. "Towards Generating Unit Test Codes Using Generative Adversarial Networks." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 2 (2022): 305–14. http://dx.doi.org/10.29207/resti.v6i2.3940.

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Unit testing is one of the important software development steps to ensure the software’s quality. Despite its importance, unit testing is often neglected since it requires a significant amount of time and effort from the software developers to write them. Existing automated testing generating systems from past research still have shortcomings due to the Genetic Algorithm (GA) limitations to generate the appropriate unit test codes. This study explores the feasibility of using Generative Adversarial Networks (GAN) models to generate unit test code with the ability of GAN to cover GA’s drawbacks

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Polovikova, O. N., V. V. Shiryaev, N. M. Oskorbin, and L. L. Smolyakova. "Features of Software Implementation of Logical Tasks in Prolog." Izvestiya of Altai State University, no. 1(117) (March 17, 2021): 116–20. http://dx.doi.org/10.14258/izvasu(2021)1-20.

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One of the promising areas for using Prolog-systems is to solve logical tasks. This study outlines a solution approach based on the state generation procedure and the verification procedure. A solution to a logical task is presented, which demonstrates in practice the proposed approach and method of specifying a procedure for generating states. In the proposed example, a bit chain is generated that defines the code of a particular letter in the solution of the applied problem. Building a solution by means of code generation with verification allows not storing in the knowledge base a binary tr

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Changsheng Du, Changsheng Du, Yong Li Changsheng Du, and Ming Wen Yong Li. "G-DCS: GCN-Based Deep Code Summary Generation Model." 網際網路技術學刊 24, no. 4 (2023): 965–73. http://dx.doi.org/10.53106/160792642023072404014.

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<p>In software engineering, software personnel faced many large-scale software and complex systems, these need programmers to quickly and accurately read and understand the code, and efficiently complete the tasks of software change or maintenance tasks. Code-NN is the first model to use deep learning to accomplish the task of code summary generation, but it is not used the structural information in the code itself. In the past five years, researchers have designed different code summarization systems based on neural networks. They generally use the end-to-end neural machine translation

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Mardi, Siswo Utomo, Sasongko Jati, Nur Wahyudi Eko, and Nurraharjo Eddy. "Comparative Study of Source Code Complexity in PHP Web Applications: Utilization of Commercial Code Generators and Manual Framework." Engineering and Technology Journal 9, no. 08 (2024): 4753–59. https://doi.org/10.5281/zenodo.13317647.

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This study examined the complexity of source code generated by commercial code generators (PHPMaker and PHPRunner) versus code written manually using the Laravel framework and the open-source code generator CakePHP. Code complexity is a critical metric in software development that influences maintenance, improvement, and responsiveness to changes. This study uses an empirical analysis approach to assess code complexity using cyclomatic and relative system complexity metrics. According to research findings, commercial code generators speed up the program writing process while producing code tha

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Kuhlen, David. "Contribution of code generation in order to save process costs in software development." International Journal of Research Publication and Reviews 5, no. 7 (2024): 2385–90. http://dx.doi.org/10.55248/gengpi.5.0724.1814.

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Thangaraj, Jagadeeswaran, and Senthilkumaran Ulaganathan. "A Comparative Study on Transformation of UML/OCL to Other Specifications." Recent Advances in Computer Science and Communications 13, no. 2 (2020): 256–64. http://dx.doi.org/10.2174/2213275912666190129121059.

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Background: Static verification is a sound programming methodology that permits automated reasoning about the correctness of an implementation with respect to its formal specification before its execution. Unified Modelling Language is most commonly used modelling language which describes the client’s requirement. Object Constraint Language is a formal language which allows users to express textual constraints regarding the UML model. Therefore, UML/OCL express formal specification and helps the developers to implement the code according to the client’s requirement through software design. Obj

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Ganage, Pankaj, Shrutika Mahajan, Sachin Jadhav, Samarth Gupta, Mrs Jayashri Dhere, and Ms Abhilasha Bhagat. "Codex comment: lexical and syntactic enhanced code comment generation." International Journal of Advanced Mathematical Sciences 11, no. 1 (2025): 49–54. https://doi.org/10.14419/s00a4t09.

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Insufficient or poor-quality code comments are the result of inexperience or intentional omission, which can ‎hinder software comprehension and pose challenges, particularly in the context of maintenance within a ‎team-programming environment. Here, the smart tools for a more detailed understanding, such as ‎automatic generation of comprehensible code comments, can give insights about functionality and purpose of ‎every element of the code. During the last few years, studies were conducted about this problem since code ‎comments are the only way for human constructively to code is to be convey

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Mahmood, Hina, Atif Aftab Jilani, and Abdul Rauf. "Code Swarm: A Code Generation Tool based on the Automatic Derivation of Transformation Rule Set." International Journal of Software Engineering & Applications 14, no. 6 (2023): 01–11. http://dx.doi.org/10.5121/ijsea.2023.14601.

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Automatic generation of software code from system design models remains an actively explored research area for the past several years. A number of tools are currently available to facilitate and automate the task of generating code from software models. To the best of our knowledge, existing software tools rely on an explicitly defined transformation rule set to perform the model-to-code transformation process. In this paper, we introduce a novel tool named Code Swarm, abbreviated as CodS, that automatically generates implementation code from system design models by utilizing a swarm-based app

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Boopathi, T*1 &. Selvaraj P. A2. "EFFECTIVE ERROR CODE DETECTION FOR SOFTWARE TESTING USING DUAL DRIVEN SOFTWARE TOOL." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 3 (2019): 12–21. https://doi.org/10.5281/zenodo.2584316.

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Source code of large systems is iteratively refined, restructured and evolved due to many reasons such as correcting errors in design, modifying a design to accommodate changes in requirements, and modifying a design to enhance features. Many studies reported that these software maintenance activities consume up to 90 percent of the total cost of a typical software project. This paper considers code-smells detection as a distributed optimization problem. The idea is that different methods are combined in parallel during the optimization process to find a consensus regarding the detection of co

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Erizo, Juan jacob. "AI-Augmented Code Generation." Jurnal Sistem Informasi dan Teknik Informatika (JAFOTIK) 3, no. 1 (2025): 19–24. https://doi.org/10.70356/jafotik.v3i1.53.

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AI-augmented code generation is transforming software development by enhancing productivity, reducing repetitive tasks, and improving code quality. Tools like GitHub Copilot, OpenAI Codex, and IntelliCode assist developers by providing real-time code suggestions, generating functions from natural language prompts, and detecting potential errors. This technology simplifies coding workflows, allowing programmers to focus on complex problem-solving rather than routine coding tasks.AI-powered tools rely on deep learning models trained on vast code repositories to understand context and generate re

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EMMART, NIALL, and CHARLES WEEMS. "SEARCH-BASED AUTOMATIC CODE GENERATION FOR MULTIPRECISION MODULAR EXPONENTIATION ON MULTIPLE GENERATIONS OF GPU." Parallel Processing Letters 23, no. 04 (2013): 1340009. http://dx.doi.org/10.1142/s0129626413400094.

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Multiprecision modular exponentiation has a variety of uses, including cryptography, prime testing and computational number theory. It is also a very costly operation to compute. GPU parallelism can be used to accelerate these computations, but to use the GPU efficiently, a problem must involve many simultaneous exponentiation operations. Handling a large number of TLS/SSL encrypted sessions in a data center is an important problem that fits this profile. We are developing a framework that enables generation of highly efficient implementations of exponentiation operations for different NVIDIA

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Vidhya, K., and A. Brunda. "Automatic Code Generation and Code Reusability through Software Clone Repositories." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 6 (2017): 532–36. http://dx.doi.org/10.23956/ijarcsse/v7i6/0115.

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Frutos Alfaro, Francisco, and Rodrigo Carboni Méndez. "MAGNETOHYDRODYNAMIC EQUATIONS (MHD) GENERATION CODE." Revista de Matemática: Teoría y Aplicaciones 23, no. 1 (2017): 41–61. http://dx.doi.org/10.15517/rmta.v23i1.22343.

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A program to generate codes in Fortran and C of the full magnetohydrodynamic equations is shown. The program uses the free computer algebra system software REDUCE. This software has a package called EXCALC, which is an exterior calculus program. The advantage of this program is that it can be modified to include another complex metric or spacetime. The output of this program is modified by means of a LINUX script which creates a new REDUCE program to manipulate the magnetohydrodynamic equations to obtain a code that can be used as a seed for a magnetohydrodynamic code for numerical application

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Zeng, Lingbin, Xin Guo, Cheng Yang, Yao Lu, and Xiao Li. "TagNN: A Code Tag Generation Technology for Resource Retrieval from Open-Source Big Data." Wireless Communications and Mobile Computing 2021 (August 22, 2021): 1–11. http://dx.doi.org/10.1155/2021/9956207.

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With the vigorous development of open-source software, a huge number of open-source projects and open-source codes have been accumulated in open-source big data, which contains a wealth of code resources. However, effectively and efficiently retrieving the relevant code snippets in such a large amount of open-source big data is an extremely difficult problem. There are usually large gaps between the user’s natural language description and the open-source code snippets. In this paper, we propose a novel code tag generation and code retrieval approach named TagNN, which combines software enginee

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Khokhlov, Yury. "Advancing operational efficiency in software companies through generative AI." American Journal of Engineering and Technology 07, no. 01 (2025): 11–18. https://doi.org/10.37547/tajet/volume07issue01-03.

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Generative AI is rapidly reshaping the landscape of software (SW) companies’ operations, offering unprecedented capabilities for creating new code, documentation, designs, and more. By harnessing advanced machine learning architectures such as large language models (LLMs), agent-based frameworks, retrieval-augmented generation (RAG), and multimodal systems, organizations can reduce development cycles, improve service quality, and unlock innovative business opportunities. Recent articles highlight how these AI-driven approaches not only address routine tasks—such as boilerplate code generation

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Lee, Woojin, Ilwoo Choi, Cheeyang Song, and Juil Kim. "An Attribute-Based Development Framework of Node Software for Various Operating Systems in Sensor Network Environment." International Journal of Software Engineering and Knowledge Engineering 26, no. 07 (2016): 1055–75. http://dx.doi.org/10.1142/s0218194016500364.

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This paper proposes a framework for the development of sensor node software for various operating systems in a sensor network environment. The proposed development framework consists of attributes, code templates and development support tool. Sensor node software is developed, based on the framework through four steps —sensor network modeling, PIM design, PSM design and code generation. Accordingly, this paper presents the methods for attributes design, code templates design, PIM-to-PSM mapping, and source code generation. Through the proposed technique, reusability of sensor network software

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Chen, Whai-En, Yi-Bing Lin, Tai-Hsiang Yen, Syuan-Ru Peng, and Yun-Wei Lin. "DeviceTalk: A No-Code Low-Code IoT Device Code Generation." Sensors 22, no. 13 (2022): 4942. http://dx.doi.org/10.3390/s22134942.

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The deployment of a client–server-based distributed intelligent system involves application development in both the network domain and the device domain. In the network domain, an application server (typically in the cloud) is deployed to execute the network applications. In the device domain, several Internet of Things (IoT) devices may be configured as, for example, wireless sensor networks (WSNs), and interact with each other through the application server. Developing the network and the device applications are tedious tasks that are the major costs for building a distributed intelligent sy

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Samodra, Bayu, Vebby Amelya Nora, Fitra Arifiansyah, Gusti Ayu Putri Saptawati Soekidjo, and Muhamad Koyimatu. "DEVELOPMENT OF GRAPH GENERATION TOOLS FOR PYTHON FUNCTION CODE ANALYSIS." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 10, no. 3 (2025): 690–97. https://doi.org/10.33480/jitk.v10i3.6177.

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The increasing complexity of programs in software development requires understanding and analysis of code structure, especially in Python, which dominates machine learning and data science applications. Manual static analysis is often time-consuming and prone to errors. Meanwhile, static analysis tools for Python, like PyCG and Code2graph, are still limited to generating call graphs without including dependency and control flow analysis. This research addresses these shortcomings by proposing the development of a web-based tool that integrates the generation of function call graphs, function d

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Liu, Mingxing, Junfeng Wang, Tao Lin, Quan Ma, Zhiyang Fang, and Yanqun Wu. "An Empirical Study of the Code Generation of Safety-Critical Software Using LLMs." Applied Sciences 14, no. 3 (2024): 1046. http://dx.doi.org/10.3390/app14031046.

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In the digital era of increasing software complexity, improving the development efficiency of safety-critical software is a challenging task faced by academia and industry in domains such as nuclear energy, aviation, the automotive industry, and rail transportation. Recently, people have been excited about using pre-trained large language models (LLMs) such as ChatGPT and GPT-4 to generate code. Professionals in the safety-critical software field are intrigued by the code generation capabilities of LLMs. However, there is currently a lack of systematic case studies in this area. Aiming at the

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Eckert, Candice, Brian Cham, Pengyi Li, Jing Sun, and Gillian Dobbie. "Linking Design Model with Code." International Journal of Software Engineering and Knowledge Engineering 26, no. 09n10 (2016): 1473–91. http://dx.doi.org/10.1142/s0218194016400131.

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With the growing in size and complexity of modern computer systems, the need for improving the quality at all stages of software development has become a critical issue. The current software production has been largely dependent on manual code development. Despite the slow development process, the errors introduced by the programmers contribute to a substantial portion of defects in the final software product. Model-driven engineering (MDE), despite having many advantages, is often overlooked by programmers due to lack of proper understanding and training in the matter. This paper investigates

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Arutyunov, erman Arsenovich, and Sergey Mikchailovitch Avdoshin. "Big Transformers for Code Generation." Proceedings of the Institute for System Programming of the RAS 34, no. 4 (2022): 79–88. http://dx.doi.org/10.15514/ispras-2022-34(4)-6.

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IT industry has been thriving over the past decades. Numerous new programming languages have emerged, new architectural patterns and software development techniques. Tools involved in the process ought to evolve as well. One of the key principles of new generation of instruments for software development would be the ability of the tools to learn using neural networks. First of all, it is necessary for the tools to learn how to write code. In this work we study the ability of Transformers to generate competition level code. The main goal is to discover whether open-source Big Transformers are “

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Tang, Jin Chu. "Application of Code Generation Technology in Software Projects." Applied Mechanics and Materials 513-517 (February 2014): 2090–93. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.2090.

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Today, software systems become more and more larger and complex, higher level requirements for software availability, robustness, easy installation and integration have been put forward, through the application of code generation technology (CGT) in the actual projects, the code naming convention, document preparation, access mechanism, normative and readability are guaranteed effectively for software projects, the labor intensity is reduced effectively, the work efficiency is improved, and the probability of errors is reduced, project staffs can focus more on business logic, significant resul

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Wipliez, Matthieu, Ghislain Roquier, and Jean-François Nezan. "Software Code Generation for the RVC-CAL Language." Journal of Signal Processing Systems 63, no. 2 (2009): 203–13. http://dx.doi.org/10.1007/s11265-009-0390-z.

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Zalamea, Javier, Josep Llosa, Eduard Ayguadé, and Mateo Valero. "Improved spill code generation for software pipelined loops." ACM SIGPLAN Notices 35, no. 5 (2000): 134–44. http://dx.doi.org/10.1145/358438.349319.

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Нагорных, М. Э., А. Н. Быков, and С. А. Чернышёв. "Software for domain ontology formation and code generation." Vestnik of Russian New University. Series «Complex systems: models, analysis, management», no. 4 (January 23, 2023): 151–59. http://dx.doi.org/10.18137/rnu.v9187.22.04.p.149.

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Разработано программное обеспечение для построения онтологий и их кодогенерации. Представлены аналоги приложения, выявлены их сильные и слабые стороны. Подробно рассмотрены процессы описания онтологий и кодогенерации, а также стек технологий разработки, архитектура приложения. Продемонстрированы результаты работы программы на абстрактных примерах. Software for building ontologies and their code generation has been developed. During development, analogues of the application were considered, their strengths and weaknesses were identified. Also, the processes of ontology description and code gene

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Hani Al-Bloush. "Analyzing ChatGPT's Problem-solving Capabilities in Arabic-Based Software tasks." Journal of Information Systems Engineering and Management 10, no. 23s (2025): 688–702. https://doi.org/10.52783/jisem.v10i23s.3766.

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This study investigates the problem of ChatGPT’s performance in generating and optimizing Python code in both English and Arabic, addressing the challenges of low-resource languages. The objective is to compare how ChatGPT versions 3.5 and 4.0 handle standard algorithms-Linear Search, Binary Search, and Quick Sort in terms of code generation, optimization, and readability. Using a comparative experimental design, key performance metrics such as time complexity, execution speed, and error rates were analyzed. The results reveal substantial disparities between the two languages: English exhibite

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Zhu, Haoming. "A Study of Coding Framework Generation by ChatGPT." Applied and Computational Engineering 100, no. 1 (2024): 177–85. https://doi.org/10.54254/2755-2721/2025.18277.

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In recent years, large language models (LLMs) have demonstrated remarkable capabilities in the field of code generation. However, existing research has primarily focused on algorithmic problem-solving code generation, with limited attention to the ability to generate framework code used in actual software development. Programming frameworks are vital tools in software development, effectively reducing development time and enhancing code compatibility. This paper takes the Qt framework in C++ as an example to systematically evaluate ChatGPTs performance in code generation at different levels of

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Witte, Philipp A., Mathias Louboutin, Navjot Kukreja, et al. "A large-scale framework for symbolic implementations of seismic inversion algorithms in Julia." GEOPHYSICS 84, no. 3 (2019): F57—F71. http://dx.doi.org/10.1190/geo2018-0174.1.

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Writing software packages for seismic inversion is a very challenging task because problems such as full-waveform inversion or least-squares imaging are algorithmically and computationally demanding due to the large number of unknown parameters and the fact that waves are propagated over many wavelengths. Therefore, software frameworks need to combine versatility and performance to provide geophysicists with the means and flexibility to implement complex algorithms that scale to exceedingly large 3D problems. Following these principles, we have developed the Julia Devito Inversion framework, a

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Yang, Chen, and Yan Liu. "Enhancements to Neural Language Model for Generating System Configuration Code: A Study with Maven Dependency." Applied Sciences 12, no. 16 (2022): 8347. http://dx.doi.org/10.3390/app12168347.

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Thanks to the widespread application of software frameworks, OTS components, DSLs, and new-generation software building and construction systems, leveraging system configuration code to assist in development is increasingly common, especially in agile software development. In software system implementation, the system configuration code is used to separate configuration items from the underlying logic, of which Maven dependency code is a typical example. To improve software productivity, developers often reuse existing dependency libraries. However, the large quantity, rapid iteration, and var

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Shah, Jyoti Kunal. "Ethical Considerations of LLM-Driven Quantum Code Generation for Optimization Tasks." American Journal of Engineering and Technology 05, no. 12 (2023): 52–59. https://doi.org/10.37547/tajet/volume05issue12-13.

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The convergence of large language models (LLMs) and quantum computing has the potential to revolutionize software development for quantum optimization tasks. AI-assisted code generation, powered by models like OpenAI Codex, can accelerate the design of quantum algorithms by automating routine coding tasks and democratizing access to quantum programming. However, this innovation introduces a web of ethical, legal, and technical challenges. This paper investigates the implications of using LLMs to generate quantum code, focusing on intellectual property (IP) concerns, the risk of unintended outc

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Gacemi, Abdelaziz, Abdehak Seriai, and Mourad Chabane Oussalah. "Code Generation from Architectural Multiviews Description." Journal of Object Technology 4, no. 3 (2005): 41. http://dx.doi.org/10.5381/jot.2005.4.3.a6.

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Lengauer, Christian. "Code generation for a systolic computer." Software: Practice and Experience 20, no. 3 (1990): 261–82. http://dx.doi.org/10.1002/spe.4380200304.

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Cordy, James R., and Richard C. Holt. "Code generation using an orthogonal model." Software: Practice and Experience 20, no. 3 (1990): 301–20. http://dx.doi.org/10.1002/spe.4380200306.

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Mahoney, William, and Jay Pedersen. "Teaching compiler code generation." ACM SIGCSE Bulletin 41, no. 4 (2010): 30–34. http://dx.doi.org/10.1145/1709424.1709440.

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Naveen Reddy, Koppula, Boda Abhinav Reddy, Dr Mukesh Kumar, Lambu Nithin, and Daxa Vekariya. "Enhancing Secure Card Payments Using Dynamic Multi-Factor Authentication: A Code_safe_pay Approach with Time-Based Code Generation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42461.

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Project "Code Safe" is designed to increase the dependability and security of software through strong security measures, as well as sticking to coding guidelines. In today's digital world that is characterized by data breaches and cyber threats, the safety of sensitive data and protection against unauthorized entry have become very necessary. The project aims to provide a secure environment for software applications by including encryption, access control, and secure authentication mechanisms. The project ensures that any confidential information is kept safe from unauthorized tampering or acc

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Babich, Fulvio, and Francesca Vatta. "On the Error Statistics of Turbo Decoding for Hybrid Concatenated Codes Design." Journal of Communications Software and Systems 15, no. 2 (2019): 202. http://dx.doi.org/10.24138/jcomss.v15i2.669.

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In this paper we propose a model for the generation of error patterns at the output of a turbo decoder. One of the advantages of this model is that it can be used to generate the error sequence with little effort. Thus, it provides a basis for designing hybrid concatenated codes (HCCs) employing the turbo code as inner code. These coding schemes combine the features of parallel and serially concatenated codes and thus offer more freedom in code design. It has been demonstrated, in fact, that HCCs can perform closer to capacity than serially concatenated codes while still maintaining a minimum

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Wang, Shangwen, Bo Lin, Zhensu Sun, et al. "Two Birds with One Stone: Boosting Code Generation and Code Search via a Generative Adversarial Network." Proceedings of the ACM on Programming Languages 7, OOPSLA2 (2023): 486–515. http://dx.doi.org/10.1145/3622815.

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Automatically transforming developers' natural language descriptions into source code has been a longstanding goal in software engineering research. Two types of approaches have been proposed in the literature to achieve this: code generation, which involves generating a new code snippet, and code search, which involves reusing existing code. However, despite existing efforts, the effectiveness of the state-of-the-art techniques remains limited. To seek for further advancement, our insight is that code generation and code search can help overcome the limitation of each other: the code generato

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Tsoeunyane, Lekhobola, Simon Winberg, and Michael Inggs. "Automatic Configurable Hardware Code Generation for Software-Defined Radios." Computers 7, no. 4 (2018): 53. http://dx.doi.org/10.3390/computers7040053.

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The development of software-defined radio (SDR) systems using field-programmable gate arrays (FPGAs) compels designers to reuse pre-existing Intellectual Property (IP) cores in order to meet time-to-market and design efficiency requirements. However, the low-level development difficulties associated with FPGAs hinder productivity, even when the designer is experienced with hardware design. These low-level difficulties include non-standard interfacing methods, component communication and synchronization challenges, complicated timing constraints and processing blocks that need to be customized

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Bhartacharyya, S. S., R. Leupers, and P. Marwedel. "Software synthesis and code generation for signal processing systems." IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing 47, no. 9 (2000): 849–75. http://dx.doi.org/10.1109/82.868454.

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Shulga, T. E., E. A. Ivanov, M. D. Slastihina, and N. S. Vagarina. "Developing a software system for automata-based code generation." Programming and Computer Software 42, no. 3 (2016): 167–73. http://dx.doi.org/10.1134/s0361768816030075.

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Bachir, Mounira, Sid-Ahmed-Ali Touati, Frederic Brault, David Gregg, and Albert Cohen. "Minimal Unroll Factor for Code Generation of Software Pipelining." International Journal of Parallel Programming 41, no. 1 (2012): 1–58. http://dx.doi.org/10.1007/s10766-012-0203-z.

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Bacher, R. "Computer aided power flow software engineering and code generation." IEEE Transactions on Power Systems 11, no. 1 (1996): 490–96. http://dx.doi.org/10.1109/59.486138.

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Mentis, Anakreon, and Panagiotis Katsaros. "Model checking and code generation for transaction processing software." Concurrency and Computation: Practice and Experience 24, no. 7 (2012): 711–22. http://dx.doi.org/10.1002/cpe.1876.

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Li, Pengyi, Jing Sun, and Hai Wang. "Formal Approach to Assertion-Based Code Generation." International Journal of Software Engineering and Knowledge Engineering 27, no. 09n10 (2017): 1637–62. http://dx.doi.org/10.1142/s0218194017400162.

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With the growing in size and complexity of modern computer systems, the need for improving the quality at all stages of software development has become a critical issue. The current software production has been largely dependent on manual code development. Despite the slow development process, the errors introduced by the programmers contribute to a substantial portion of defects in the final software product. This paper investigates the synergy of generating code and assertion constraints from formal design models and use them to verify the implementation. We translate Z formal models into th

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Fraser, Christopher W., and Todd A. Proebsting. "Finite-state code generation." ACM SIGPLAN Notices 34, no. 5 (1999): 270–80. http://dx.doi.org/10.1145/301631.301680.

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Eriksson, Mattias, and Christoph Kessler. "Integrated Code Generation for Loops." ACM Transactions on Embedded Computing Systems 11S, no. 1 (2012): 1–24. http://dx.doi.org/10.1145/2180887.2180896.

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Oedingen, Marc, Raphael C. Engelhardt, Robin Denz, Maximilian Hammer, and Wolfgang Konen. "ChatGPT Code Detection: Techniques for Uncovering the Source of Code." AI 5, no. 3 (2024): 1066–94. http://dx.doi.org/10.3390/ai5030053.

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In recent times, large language models (LLMs) have made significant strides in generating computer code, blurring the lines between code created by humans and code produced by artificial intelligence (AI). As these technologies evolve rapidly, it is crucial to explore how they influence code generation, especially given the risk of misuse in areas such as higher education. The present paper explores this issue by using advanced classification techniques to differentiate between code written by humans and code generated by ChatGPT, a type of LLM. We employ a new approach that combines powerful

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Sri Durga, Tenneti Lekhya, Mallak Keshava Gayatri, Mukku Deepthi Prabha, and Dr D. Shravani. "Code Generation Using LLMs." International Journal of Future Engineering Innovations 2, no. 2 (2025): 16–22. https://doi.org/10.54660/ijfei.2025.2.2.16-22.

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Code generation using Large Language Models has brought a significant change in the software industry helping people in various things like understanding the code, writing a code, completion of the code. The main challenge that the Code Generation Models are facing is Hallucinations wherein the given code may not satisfy the users requirements completely or might deviate from the user’s requirements. There are many kinds of hallucinations and are divided into different types like the intent conflicting which consists of semantic conflicts, context deviation which includes inconsistency, repeti

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