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About 5,305 papers for “code generators”

Towards Code-centric Code Generators

Busch, Daniel · Technische Universität Berlin – Universitätsbibliothek · 2023

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Robux code generators

Robux generator · Zenodo (CERN European Organization for Nuclear Research) · 2020

Robux code generators\n\nCLICK HERE\n\nRobux Generator Free Robux in Roblox 2020\n\n\nStep by step instructions to Earn Free Robux in 2020 in Roblox\n\nRoblox is an effective multiplayer online computer game that has caught the consideration of in excess of 100 customary clients, getting to the stage at any rate once every month. Moreover, the Roblox is getting progressively valued by an expansive open, which brings about a great increment in the quantity of first-time clients that join the network every day. The in-game money is Robux. Here, we'll give you a few different ways to acquire free Robux.\n\nThe most effective method to Earn Free Robux in 2020 in Roblox\n\nThe game highlights the alternative for gamers to buy their ideal things paying in the game's virtual money. In any case, there are minutes when individuals can't bear to purchase their own Robux. Here you can discover how to acquire the game's cash with the expectation of complimentary utilizing specific assets.\n\nThe best way is join studies. Numerous destinations are requesting that gamers fill in certain structures in return for focuses that can be utilized to buy your own Robux.\n\nTags|:\n\nroblox robux cheats\nroblox generator\nroblox free robux\nroblox robux generator\nroblox robux\nfree robux cheats\nroblox free\nfree robux generator\nroblox online\nget free robux\nfree robux codes\nroblox cheats 2020\nfree robux and tix\nrobux generator no verification\nroblox cheats no human verification\nfree robux no verification\ncheats robux 2020\nrobux cheats no survey\nroblox online generator\nfree robux 2020\nroblox robux cheats generator\nroblox cheats no survey\nfree robux no human verification 2020\nroblox money cheats\nrobux generator no human verification\nfree robux win\nroblox online cheats\nroblox cheats 2019\nfree robux generator no survey\nrobux generator 2019\nrobux cheats no human verification\ncheats roblox 2019\nroblox cheats free robux\nfree roblox money\nfree robux website\nroblox cheats tool\nrobloxcheats site\nrobux generator no human verification 2019\nroblox codes for robux\nroblox cheats 2019\nfree robucks\nrobux generator online\nroblox cheats robux 2019\nrobux cheats 2019\nrobux cheat\nfree robux generator for roblox\nroblox free robux no survey\nroblox tix 2019\nroblox cheats no verification\nfree roblox accounts\nfree robux 2019\nroblox cheats generator\ngenerator robux\nroblox generator 2019\nrobux cheats generator\nroblox robux generator online\nfree robux cheats no survey\nroblox money\nrobux no survey\nrobux code generator\nhow to get free robux on roblox\nfree robux no cheats\neasy robux 2019\nget free robux for roblox\nrobux and tix generator\nrobux cheats no verification\nfree roblox cheatss\nfree robux generator no verification\nfree robux generator no human verification 2019\nfree robux no human verification or survey 2019\nfree robux generator no survey no download no human verification\nrobux generator no human verification or survey 2019\nfree robux generator no human verification 2018\nrobux generator no human verification or survey 2018\nfree robux no human verification no download no survey no offers\nfree robux no verification no download

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Hierarchical Code Generators

Mariusz Nowostawski Information · 2007

Hierarchically organised recursive virtual machines can be used as a novel dynamic hierarchies modelling technique.

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Automated Code Generators for Compilers.

Eric Chu, E. Halb, H. McCoy et al. · 1978

Abstract : This project was undertaken with the intent of devising a method or tool for automating the production of the code generator portion of compilers. The main objective was to develop a Quick Code Generator (QCG) that would be capable of producing JOVIAL compilers in less than 3 months. This generator would develop loose code for an interim compiler. This effort resulted in developing the Machine Independent Macro Instruction Code (MIMIC) language. This language consisted of 113 macros and a QCG was successfully produced for the HIS 6000 system. However, the effort fell short by not producing macro definition files for other target machines. It should be noted that RADC's JOVIAL Compiler Validation System was executed with the original JOCIT compiler and the QCG JOCIT compiler and the differences in compilation time, execution time and the total code produced were insignificant. (Author)

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clij/clij2-code-generators: 2.5.0.1

Haase, Robert · Zenodo (CERN European Organization for Nuclear Research) · 2021

See https://github.com/clij/clij2/releases/tag/2.5.0.1

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Code generators for mathematical functions

Nicolas Brunie, Florent de Dinechin, Olga Kupriianova et al. · HAL (Le Centre pour la Communication Scientifique Directe) · 2015

Best paper award

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Methods of evaluating code generators

V. O. Mykolayenko · Connectivity · 2024

In the ever-evolving landscape of software development, code generators have emerged as vital instruments, fostering innovation and efficiency. These automated maestros translate abstract requirements into functional code, significantly reducing development time and bridging gaps across varied programming environments. This article categorizes code generators based on their operational domain, input specificity, and the nature of the output they produce, unraveling a diverse spectrum from foundational scaffolding creators to sophisticated, language-specific assistants. Selecting an apt code generator is pivotal to the success of a project. To assist developers in this critical choice, the article presents an evaluation method that weighs essential factors such as adaptability, efficiency, and ease of integration with existing systems. This systematic approach ensures the selection of a tool that not only meets immediate project needs but also supports future scalability and complexity. The discussion extends beyond functionality to explore the transformative role of code generators. By automating routine coding tasks, they allow developers to focus on more creative aspects of software design, enhancing code quality and fostering a culture of innovation. The generators thus become indispensable allies in the creative process of software development. In essence, the article advocates for an informed selection of code generators, viewing them as cornerstones for modern coding practices. The proposed evaluation method serves as a strategic framework, empowering developers to navigate the myriad of options and align their choice with both current requirements and future aspirations. It is an invitation to embrace the synergy between human creativity and machine precision, crafting code that is both efficient and elegant.

Cited by 0Open access

Code Generators for Mathematical Functions

Nicolas Brunie, Florent de Dinechin, Olga Kupriianova et al. · 2015

A typical floating-point environment includes support for a small set of about 30 mathematical functions such as exponential, logarithm, trigonometric and hyperbolic functions. These functions are provided by mathematical software libraries (libm), typically in IEEE754 single, double and quad precision. This article suggests to replace this libm paradigm by a more general approach: the on-demand generation of numerical function code, on arbitrary domains and with arbitrary accuracies. First, such code generation opens up the libm function space available to programmers. It may capture a much wider set of functions, and may capture even standard functions on non-standard domains and accuracy/performance points. Second, writing libm code requires fine-tuned instruction selection and scheduling for performance, and sophisticated floating-point techniques for accuracy. Automating this task through code generation improves confidence in the code while enabling better design space exploration, and therefore better time to market, even for the libm functions. This article discusses the new challenges of this paradigm shift, and presents the current state of open-source function code generators available on http://www.metalibm.org/.

Cited by 29

Surveyor's Forum: Retargetable Code Generators

Christopher W. Fraser · ACM Computing Surveys · 1983

article Free Access Share on Surveyor's Forum: Retargetable Code Generators Author: Christopher W. Fraser Department of Computer Science, University of Arizona, Tucson, AZ Department of Computer Science, University of Arizona, Tucson, AZView Profile Authors Info & Claims ACM Computing SurveysVolume 15Issue 3Sept. 1983 pp 281–283https://doi.org/10.1145/356914.356922Published:01 September 1983Publication History 0citation309DownloadsMetricsTotal Citations0Total Downloads309Last 12 Months5Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

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Data access layer code generators

Küçükeren, Gökçe · Akademik Açık Erişim (Işık Üniversitesi) · 2006

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Survey of Special Purpose Code Generators

Marco Craveiro · Zenodo (CERN European Organization for Nuclear Research) · 2021

MDE (Model Driven Engineering) is an approach to software engineering centred around modeling. Code generators are an important component of the MDE toolset because they bridge the gap between models and their implementation. Studies have demonstrated that deficiencies in MDE tooling are a common cause of failure in MDE adoption, highlighting the need for research to identify and address inadequacies. This paper has two contributions. First, it performs a survey of Open Source tools dedicated to special purpose code generation. Second, it identifies a core set of properties that characterise their approach, which we argue have application to code generation in MDE. These properties can be leveraged to guide new tool development as well as for evaluating existing tooling.

Cited by 0Open access

Bit-alignment for retargetable code generators

Koen Schoofs, Gert Goossens, H. De Man · 1994

Cited by 2

Bit-Alignment for Retargetable Code Generators

Koen Schoofs Gert, Gert Goossens, Hugo De Man · 1994

When building a bit-true retargetable compiler, every signal type must be implemented exactly as specified, even when the word-length of the signal does not match the length of the available hardware. Extra operations must be introduced in the algorithmic description in order to ensure that the remaining bits do not influence the databits and to assure that signal types are correctly converted from one type to another. An algorithm will be presented which generates code to assure bit-trueness, optimised for the available hardware. 1 Introduction In DSP-algorithms every signal has a certain signal type, indicating the number of bits in the signal, the number of bits behind the binary point, and the way of encoding (signed, unsigned, two&apos;s complement, etc ..). In most algorithms, signals with a large variety of types are present. The word length of these signals (the number of bits they contain) can be different from the size of the functional unit they are mapped upon. If the word len...

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Testing Code Generators against Definitional Interpreters

Giannis Papadopoulos · Research Repository (Delft University of Technology) · 2018

Large companies suffer from the increasing complexity that exist in their software systems. Evolving their software becomes even harder if we consider that a change in one system can affect several other parts of their software architecture. Especially banks that need to be always complied with regulations, have to constantly make changes in their software to reflect these changes. ING is a primary example that currently tries to find a solution to these problems through the use of model driven development and more specifically code generation. In particular, they have created a Domain Specific Language called Maverick to specify the requirements/business logic and through the usage of code generators to automatically generate their entire codebase from these Maverick specifications. Code generators as any other software artifact is not bug free, meaning that testing code generators is of paramount importance. However, testing code generators is not straightforward as their output is another program that besides syntactic structure also has behavior. Many formal approaches have been developed that try to formally prove the correctness of code generators. Nevertheless, the complexity and scalability issues that these approaches face make them infeasible in practice. This thesis presents a testing approach that leverages a definitional interpreter to test code generators. We evaluate and show the practicability of our approach using Maverick specifications developed by ING and we conclude that our proposed method can address many of the issues that formal approaches face.

Cited by 0Open access

Thermohydraulic code coupling for steam generators

Pereyron, Edgar · UPCommons institutional repository (Universitat Politècnica de Catalunya) · 2025

This report presents the work carried out during a six-month internship at Framatome’s DTIMGFH section, focused on the thermohydraulic coupling of two simulation codes for modeling steam generator behavior. The objective was to enhance and automate the coupling between STAR-CCM+, a CFD-based software, and GENEPI+, a 3D two-phase thermal-hydraulic code developed by CEA. Building on an initial numerical framework, the internship concentrated on improving the physical fidelity of the coupling process to better simulate secondary-side flows under steady-state conditions. The work involved refining boundary conditions, integrating pressure loss coefficients and recirculation characteristics, and addressing challenges such as negative velocity fields and discrepancies between the codes’ results. In addition to the technical developments, an economic and environmental assessment of the simulations was performed. The outcomes contribute to the broader goal of developing a reliable digital twin of the steam generator, enhancing Framatome’s capabilities in predictive modeling for design and operational support.

Cited by 0

Bit-alignment for retargetable code generators

K. Schoofs, G. Goossens, H. De Man · 2002

Cited by 2

One-relation languages and code generators

Vinh Duc Tran, Igor Litovsky · RAIRO - Theoretical Informatics and Applications · 2021

We investigate the open problem to characterize whether the infinite power of a given language is generated by an ω -code. In case the given language is a code ( i.e. zero-relation language), the problem was solved. In this work, we solve the problem for the class of one-relation languages.

Cited by 1Open access

CDMA signal chip code generators estimation

Chuchu Chen, Shiju Li, Tengbo Ding et al. · IET International Conference on Wireless Mobile and Multimedia Networks Proceedings (ICWMMN 2006) · 2006

Conventional linear and nonlinear intercept receivers are ineffective in detecting direct-sequence spread spectrum (DS/SS) signals if the transmissions are designed to be covert and the chip code sequences are unavailable. The detection problems are exacerbated if the users operate within a code-division multiple access (CDMA) environment. Former researches show that it is possible to use triple correlation techniques to estimate the DS/SS chip code generator. However these researches concerned only with the single DS/SS signal case. The problem of estimation of generators of CDMA signal which is the sum of multiple DS/SS signals is still unsolved. In this paper, triple correlation function (TCF) of CDMA signal is investigated and the way to use TCF to estimate CDMA chip code generators is presented. (4 pages)

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The "code'' of Ethics:A Holistic Audit of AI Code Generators

Wanlun Ma, Yiliao Song, Minhui Xue et al. · arXiv (Cornell University) · 2023

AI-powered programming language generation (PLG) models have gained increasing attention due to their ability to generate source code of programs in a few seconds with a plain program description. Despite their remarkable performance, many concerns are raised over the potential risks of their development and deployment, such as legal issues of copyright infringement induced by training usage of licensed code, and malicious consequences due to the unregulated use of these models. In this paper, we present the first-of-its-kind study to systematically investigate the accountability of PLG models from the perspectives of both model development and deployment. In particular, we develop a holistic framework not only to audit the training data usage of PLG models, but also to identify neural code generated by PLG models as well as determine its attribution to a source model. To this end, we propose using membership inference to audit whether a code snippet used is in the PLG model's training data. In addition, we propose a learning-based method to distinguish between human-written code and neural code. In neural code attribution, through both empirical and theoretical analysis, we show that it is impossible to reliably attribute the generation of one code snippet to one model. We then propose two feasible alternative methods: one is to attribute one neural code snippet to one of the candidate PLG models, and the other is to verify whether a set of neural code snippets can be attributed to a given PLG model. The proposed framework thoroughly examines the accountability of PLG models which are verified by extensive experiments. The implementations of our proposed framework are also encapsulated into a new artifact, named CodeForensic, to foster further research.

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Code obfuscation using pseudo-random number generators

John Aycock, Juan M. Gutiérrez, Daniel M. Nunes · Repositorio de Revistas de Investigación USIL (la Universidad San Ignacio de Loyola) · 2014

We describe a novel method for malicious code obfuscation that uses code already present in systems: a pseudo-random number generator. This can also be seen as an antidisassembly and anti-debugging technique, depending on deploy- ment, because the actual code does not exist until run – it is generated dynamically by the pseudo-random number generator. A year’s worth of experiments are used to demonstrate that this technique is a viable code obfuscation option for a malicious adversary with access to large amounts of computing power.

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