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iNnovative Informatics of Biskra (ISNIB), International Symposium on

作者： Tarek Ababsa Dept. Computer science University of Biskra Biskra Algeria

Genetic programming (GP) has been applied as an automatic programming tool to solve various kinds of problems by genetically breeding a population of computer programs using biologically inspired operations. However, it is well known as a computationally demanding approach with a significant potential of parallelization. In this paper, we emphasize parallelizing the evaluation of genetic programs on Graphics Processing Unit (GPU). We used a compact representation for genotypes. This representation is a memory-efficient method that allows efficient evaluation of programs. Our implementation clearly distinguishes between an individual's genotype and phenotype. Thus, the individuals are represented as linear entities (arrays of 32 bits integers) that are decoded and expressed just like nonlinear entities (trees).

关键词： Graphics automatic programming Sociology Genetic programming Graphics processing units Arrays Statistics

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A Path Sensitive and Scalable Approach for Mapping the Capabilities Required for Linux Kernel Defined System Calls Using Static Analysis Techniques

A Path Sensitive and Scalable Approach for Mapping the Capab...

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IEEE International Conference on Intelligent Computer Communication and Processing (ICCP)

作者： Răzvan-Mihai Bolundut Adrian-Viorel Coleşa Radu-Marian Portase Dept. of Computer Science Techical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9798331539979

ISBN: (纸本)9798331539986

This paper outlines an analysis method for automatically identifying the required capabilities for the system calls defined by the Linux Kernel. Integrated in the LLVM compiler infrastructure, the static and path sensitive approach used aims to generate a complete set of logical equations, expressed in terms of the syscalls' argument configurations, that describe the relation between system calls and capabilities. In order to achieve the desired syscall to capability mapping a four stage analysis pipeline is proposed, each stage complimenting one of the abstraction layers used by the LLVM compiler. First, all possible execution paths, in terms of functions, are identified by the first stage, which is followed by the identification of the control flow blocks leading to calls to ensuing functions in the chain. Once all such paths are known, the third stage is tasked with solving the path explosion problem and to eliminate all control flow blocks that do not influence overall analysis goals. The final stages are responsible with instruction level analysis which binds each path to a constant equation verifying the arguments passed to the system call. By means of a scalable synthetic test program generator four sets of test-benches are constructed. Each test-bench is responsible to validate its corresponding stage of the analysis pipeline. They consists of programs that are scaled on the dimensions identified through mathematical analysis of the tested algorithm and problem.

关键词： automatic programming Linux Scalability Pipelines Mathematical analysis Static analysis Explosions Complexity theory Kernel Testing

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Exploring LLM Support for Generating IEC 61131-3 Graphic Language Programs

arXiv

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arXiv 2024年

作者： Zhang, Yimin de Sousa, Mario CISTER Faculty of Engineering University of Porto Porto Portugal Faculty of Engineering University of Porto Porto Portugal

The capabilities demonstrated by Large Language Models (LLMs) inspire researchers to integrate them into industrial production and automation. In the field of Programmable Logic Controller (PLC) programming, previous researchers have focused on using LLMs to generate Structured Text (ST) language, and created automatic programming workflows based on it. The IEC 61131 graphic programming languages, which still has the most users [17], have however been overlooked. In this paper we explore using LLMs to generate graphic languages in ASCII art to provide assistance to engineers. Our series of experiments indicate that, contrary to what researchers usually think, it is possible to generate a correct Sequential Function Chart (SFC) for simple requirements when LLM is provided with several examples. On the other hand, generating a Ladder Diagram (LD) automatically remains a challenge even for very simple use cases. The automatic conversion between LD and SFC without extra information also fails when using prompt engineering alone. Copyright © 2024, The Authors. All rights reserved.

关键词： automatic programming

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A Deep Learning Framework for Verilog Autocompletion Towards Design and Verification Automation

arXiv

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arXiv 2023年

作者： Dehaerne, Enrique Dey, Bappaditya Halder, Sandip De Gendt, Stefan Faculty of Science KU Leuven Belgium Belgium Dept. Chemistry KU Leuven Belgium

Innovative Electronic Design Automation (EDA) solutions are important to meet the design requirements for increasingly complex electronic devices. Verilog, a hardware description language, is widely used for the design and verification of digital circuits and is synthesized using specific EDA tools. However, writing code is a repetitive and time-intensive task. This paper proposes, primarily, a novel deep learning framework for training a Verilog autocompletion model and, secondarily, a Verilog dataset of files and snippets obtained from open-source repositories. The framework involves integrating models pretrained on general programming language data and finetuning them on a dataset curated to be similar to a target downstream task. This is validated by comparing different pretrained models trained on different subsets of the proposed Verilog dataset using multiple evaluation metrics. These experiments demonstrate that the proposed framework achieves better BLEU, ROUGE-L, and chrF scores by 9.5%, 6.7%, and 6.9%, respectively, compared to a model trained from scratch. Code and data are made available at: https://***/99EnriqueD/verilog_autocompletion. © 2023, CC BY-NC-ND.

关键词： automatic programming

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Introducing automatic Verification Strategy in Online Study of Computational Science

Introducing Automatic Verification Strategy in Online Study ...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Jun Zhang Ruzong Fan Fangyang Shen Department of Computer Science and Engineering Technology University of Maryland Eastern Shore Princess Anne MD USA Department of Biostatistics Bioinformatics and Biomathematics Georgetown University Washington DC USA Dept. of Computer Systems Technology New York City College of Technology City University of New York Brooklyn NY USA

ISBN: (数字)9798350361513

ISBN: (纸本)9798350372304

Online study is a very powerful method for global education, as it is available anywhere for anyone with internet access. It is getting more and more important, especially during the pandemic. Computational science is relatively new but very important. It is considered as the third pillar for scientific research, which advances all sciences and technologies in a new dimension. More and more institutes offer online courses or programs in computational science. Unfortunately, almost no rigorous educational study was publicly available towards online computational science education. In this paper, the authors shall introduce a new automatic Verification Strategy (AVS) in online study of computational science by using Sumudu transform as an example to undergraduate or graduate students in computational science related courses. AVS is a set of programs to provide interaction and verification during the learning process. AVS will reduce cognitive load and support both synchronous and asynchronous learning. Once the AVS is implemented, it is sustainable and can be used any number of times anywhere anytime, like students have private teachers helping them anytime anywhere. Type: Full / Regular Research Paper for CSCI-RTED

关键词： automatic programming Scientific computing Pandemics Distance learning Education Transforms Cognitive load

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Research and Application of Machine Learning in automatic Program Generation

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Chinese Journal of Electronics 2020年第6期29卷 1001-1015页

作者： ZHANG Xiaojiang JIANG Ying Yunnan Key Laboratory of Computer Technology Application Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming University of Science and Technology

With the development of artificial intelligence,machine learning has been applied in more and more *** order to improve the quality and efficiency of software,automatic program generation is becoming a research *** recent years,machine learning has also been gradually applied in automatic program *** trees,language models,and cyclic neural networks have been applied in code generation,code completion and code knowledge *** efficiency of software development has been improved to a certain extent using machine *** at the automatic program generation,this paper analyzes and summarizes the models of machine learning,the modifications involved in the models and the application *** research direction is discussed from the aspects of programmer behavior and automatic program generation of machine learning.

关键词： automatic programming data mining decision trees learning (artificial intelligence) neural nets software engineering programmer behavior cyclic neural networks language models decision trees code knowledge mining code completion code generation automatic program generation machine learning Machine learning Code generation Code completion Code knowledge mining Application effect

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GRU: optimization of NPI performance

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JOURNAL OF SUPERCOMPUTING 2020年第5期76卷 3542-3554页

作者： Liu, Wei Wang, Quan Zhu, Yunlong Chen, Hanning Jilin Normal Univ Sch Informat & Technol Siping 136000 Peoples R China Tianjin Polytech Univ Sch Comp Sci & Software Tianjin 300387 Peoples R China Dongguan Univ Technol Sch Elect Engn & Intellgentizat Dongguan 523000 Peoples R China

Currently, artificial intelligence is being used in automatic programming by producing snippets of code. NPI (neural programmer-interpreter) is the most used technology that uses machine learning to implement automatic programming. This paper is aimed to improve the performance of traditional NPI and improve the speed of NPI training without loss of precision. To achieve this goal, we changed the core structure of NPI by adopting the GRU (gated recurrent unit) to replace LSTM (long short-term memory) in NPI. GRU has a control unit that regulates the flow of information within the hidden unit while without single memory unit. Numerical results have been presented to demonstrate the performance of the proposed methodology. That is, GRU-based NPI improved the performance of the original LSTM-based NPI by nearly 33% under the premise of ensuring equal accuracy.

关键词： automatic programming Neural programmer-interpreter GRU

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automatic Deictic Gestures for Animated Pedagogical Agents

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IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES 2020年第1期13卷 1-13页

作者： Kappagantula, Sri Rama Kartheek Adamo-Villani, Nicoletta Wu, Meng-Lin Popescu, Voicu Purdue Univ Dept Comp Graph Technol W Lafayette IN 47907 USA Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

We present a system that automatically generates deictic gestures for animated pedagogical agents (APAs). The system takes audio and text as input, which define what the APA has to say, and generates animated gestures based on a set of rules. The automatically generated gestures point to the exact locations of elements on a whiteboard nearby the APA, which are calculated by searching for keywords mentioned in the speech. We conducted a study with 100 subjects, in which we compared lecture videos containing gestures automatically-scripted by the system to videos of the same lecture containing manually-scripted gestures. The study results show that the manually-scripted and automatically-scripted lectures had comparable number of gestures, and that the gestures were timed equally well.

关键词： Animation Electronic learning Manuals Mathematics Videos Scalability Animation intelligent agents automatic programming computer-assisted instruction educational simulations instructor interfaces learning management systems speech analysis gesture

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Firefly programming For Symbolic Regression Problems 28

Firefly Programming For Symbolic Regression Problems

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28th Signal Processing and Communications Applications Conference (SIU)

作者： Aliwi, Mohamed Aslan, Selcuk Demirci, Sercan Ondokuz Mayis Univ Dept Comp Engn Samsun Turkey

ISBN: (纸本)9781728172064

Symbolic regression is the process of finding a mathematical formula that fits a specific set of data by searching in different mathematical expressions. This process requires great accuracy in order to reach the correct formula. In this paper, we will present a new method for solving symbolic regression problems based on the firefly algorithm. This method is called Firefly programming (FP). The results of applying firefly programming algorithm to some symbolic regression benchmark problems will be compared to the results of Genetic programming (GP) and Artificial Bee Colony programming (ABCP) methods.

关键词： firefly algorithm symbolic regression automatic programming

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Code Building Genetic programming

Code Building Genetic Programming

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Pantridge, Edward Spector, Lee Swoop Cambridge MA 02140 USA Hampshire Coll Amherst Coll Amherst MA 01002 USA UMass Amherst Amherst MA USA

ISBN: (纸本)9781450371285

In recent years the field of genetic programming has made significant advances towards automatic programming. Research and development of contemporary program synthesis methods, such as PushGP and Grammar Guided Genetic programming, can produce programs that solve problems typically assigned in introductory academic settings. These problems focus on a narrow, predetermined set of simple data structures, basic control flow patterns, and primitive, non-overlapping data types (without, for example, inheritance or composite types). Few, if any, genetic programming methods for program synthesis have convincingly demonstrated the capability of synthesizing programs that use arbitrary data types, data structures, and specifications that are drawn from existing codebases. In this paper, we introduce Code Building Genetic programming (CBGP) as a framework within which this can be done, by leveraging programming language features such as reflection and first-class specifications. CBGP produces a computational graph that can be executed or translated into source code of a host language. To demonstrate the novel capabilities of CBGP, we present results on new benchmarks that use non-primitive, polymorphic data types as well as some standard program synthesis benchmarks.

关键词： automatic programming genetic programming inductive program synthesis

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