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检索条件"主题词=Software algorithms"

共 12410 条记录，以下是281-290 订阅

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Development of an Automated Measurement System for Sapphire Glass Holes Based on Shape from Focus Method

Development of an Automated Measurement System for Sapphire ...

引用

IEEE International Conference on Consumer Electronics (ICCE)

作者： Yan-Rui Lin Chun-Yen Chu Ju-Yi Lee Department of Mechanical Engineering National Central University Taoyuan City Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This research developed an automated system for positioning and measuring circular holes using the Shape from Focus technique, aimed at addressing the challenges of precise alignment and fully automated measurement of circular holes in sapphire glass. The system comprises both hardware components (such as a CCD camera, various lenses, lighting devices, and an electronically controlled displacement platform) and software components (developed in Python and C++). The process is divided into two stages: coarse and fine automated positioning of circular holes, followed by three-dimensional shape reconstruction. Among several focus value algorithms tested, the Scharr algorithm proved to be the most effective, yielding the smallest error compared to commercial equipment. Performance validation of the system shows that with a 20x objective lens, the measurement range is 425 µm by 355 µm, with lateral and longitudinal resolutions of 0.871 µm and 0.8 µm, respectively.

关键词： Solid modeling Three-dimensional displays Shape Shape measurement software algorithms Glass Position measurement Hardware software Lenses

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Approach to Forming Vulnerability Datasets for Fine-Tuning AI Agents

Approach to Forming Vulnerability Datasets for Fine-Tuning A...

引用

International Russian Smart Industry Conference (SmartIndustryCon)

作者： Kirill Gladkikh Alexander A. Zakharov Information security department Tyumen State University Tyumen Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

This study addresses the problem of identifying vulnerabilities in open-source code by exploring existing methods of code analysis and evaluating the feasibility of automating security assessments using large language models (LLMs). We propose an approach for constructing high-quality datasets to fine-tune LLMs for software security analysis. Our methodology involves collecting and processing vulnerability data, filtering and curating security-related code changes, and structuring datasets to optimize model fine-tuning. We present an algorithm for aggregating vulnerability data sources and constructing a dataset specifically for training security-focused LLMs. To validate our approach, we fine-tune models from the Qwen family for software vulnerability detection in Python codebases during development and testing. Our findings demonstrate that the proposed method enables the development of intelligent, continuously adaptable AI agents capable of identifying and analyzing emerging zero-day vulnerabilities, not only in Python but also in other structurally similar programming languages.

关键词： Training Adaptation models Codes Filtering Large language models Soft sensors software algorithms Python Testing software development management

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Fast sequential and parallel algorithms for label selection to obtain space efficient implementations in a software configuration management system

Fast sequential and parallel algorithms for label selection ...

引用

International Conference on Parallel Computing in Electrical Engineering (PARLEC)

作者： R. Greenlaw C. Shipley J. Wogulis Armstrong Atlantic State University Savannah USA StarBase Corporation Santa Ana CA USA

software configuration management (SCM) systems require the ability to maintain collections of a large number of specific versions of files. One technique for managing these configurations is to maintain a single time-stamp identifying which versions of the files belong to the configuration along with an exception list to that time-stamp. Given the individual time-stamps of n files represented by half-open intervals, we develop an O(n log n) tone sequential algorithm that finds a time-stamp leading to the smallest possible exception list. The technique is used in a commercial SCM system and works well in practice. We also develop a parallel version of the algorithm that runs in O(log n) time using n processors on an EREW-PRAM. If the intervals are sorted, the sequential algorithm runs optimally in O(n) time and the parallel algorithm runs optimally in O(log n) time using n/log n processors on an EREW-PRAM.

关键词： Parallel algorithms software maintenance software systems Computer science Drives software algorithms Documentation Optimizing compilers Operating systems

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Estimation of distribution algorithms for testing object oriented software

Estimation of distribution algorithms for testing object ori...

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Congress on Evolutionary Computation

作者： Ramon Sagarna Andrea Arcuri Xin Yao Centre of Excellence for Research in Computational Intelligence and Applications School of Computer Science University of Binningham Birmingham UK

One of the main tasks software testing involves is the generation of the test cases to be used during the test. Due to its expensive cost, the automation of this task has become one of the key issues in the area. While most of the work on test data generation has concentrated on procedural software, little attention has been paid to object oriented programs, even so they are a usual practice nowadays. We present an approach based on estimation of distribution algorithms (EDAs) for dealing with the test data generation of a particular type of objects, that is, containers. This is the first time that an EDA has been applied to testing object oriented software. In addition to automated test data generation, the EDA approach also offers the potential of modelling the fitness landscape defined by the testing problem and thus could provide some insight into the problem. Firstly, we show results from empirical evaluations and comment on some appealing properties of EDAs in this context. Next, a framework is discussed in order to deal with the generation of efficient tests for the container classes. Preliminary results are provided as well.

关键词： software algorithms software testing Electronic design automation and methodology Containers Automatic testing System testing Costs Object oriented modeling Probability distribution software quality

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Bio-inspired algorithms in software Fault Prediction: A Systematic Literature Review

Bio-inspired Algorithms in Software Fault Prediction: A Syst...

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Asad Ali Carmine Gravino University of Salerno Fisciano SA Italy

ISBN: (数字)9781728190501

ISBN: (纸本)9781728190518

Bio-inspired (and meta-heuristic) algorithms are successfully employed in different domains and the research is going on to accommodate them in all the contexts where optimization is required. In software engineering, and especially software Fault Prediction (SFP), they are investigated in various forms, e.g., to extract the most relevant features in a dataset or to select the most appropriate set of parameter values in the application of estimation techniques. In SFP, feature selection and optimization/tuning of estimation technique's parameters are an active research area, where recently various bio-inspired algorithms have been employed for both strategies. In this work, we present a Systematic Literature Review (SLR) about the use of bio-inspired algorithms for feature selection and parameter optimization aiming at increasing fault prediction accuracy of the models built with various estimation techniques. To the best of our knowledge, there is no SLR in SFP which covers the use of bioinspired algorithms, both for feature selection and parameter optimization. Since, the use of bio-inspired algorithms in the area of SFP started to be investigated in the late 2000, we have considered studies published between 2007 and 2019. As result, we have selected about 19 studies related to parameter optimization and 15 dealing with feature selection (in total 34 studies), extracted from five well-known digital libraries (ACM digital library, IEEE explore, Springer, ScienceDirect, and Scopus). Genetic algorithms (GA) and Particle Swarm Optimization (PSO) are the widely used bio-inspired algorithms, both for parameter optimization and feature selection. Among them, GA is the better performed algorithm when evaluating its performance against the baseline (i.e., estimation techniques without any algorithm for feature selection or parameter optimization and trained with their default values). The SLR results also suggests that bio-inspired algorithms seem to provide more accurate

关键词： Bibliographies software algorithms Estimation Prediction algorithms Feature extraction Optimization Genetic algorithms

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Scheduling algorithms for Simultaneous software Updates of Electronic Devices in Vehicles

Scheduling Algorithms for Simultaneous Software Updates of E...

引用

IEEE International Conference on Communications Workshops, ICC

作者： J. Sommer V. Feil E. A. Sanz Institute of Communication Networks and Computer Engineering (IKR) University of Stuttgart Stuttgart Germany Infotainment and Telematics Daimler Benz Aerospace Sindelfingen Germany

Today's upper-class passenger cars have various interconnected electronic devices. Each device performs complex functions, enabled by software that can be stored in a flash memory. Of these, the devices in the multimedia and infotainment domain contain by far the most software with a size in the order of one Gbyte. In this domain, the devices are the performance bottlenecks, not the communication systems. Throughout the vehicle life cycle, parts of the software have to be frequently updated during maintenance. Today, the software of the devices is updated in a consecutive manner. Due to performance bottlenecks caused by the affected devices, the update can take a long time that leads to high costs. Therefore, the objective is to reduce the total update time by a higher utilization of the common bus resource. In this paper, we introduce and investigate algorithms that update the software of multiple devices simultaneously and evaluate the efficiency of these algorithms. We focus on scheduling algorithms on the Application layer and the logical link control (LLC) layer and model the update process by means of Petri nets. Our studies show that it is most promising to combine a simple scheduling algorithm on the Application layer with Round Robin on the LLC layer.

关键词： Scheduling algorithm Vehicles software algorithms Round robin software performance Flash memory software maintenance Costs Application software Petri nets

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Search-Based software Testing Driven by Bioinspired algorithms: A Systematic Literature Mapping

Search-Based Software Testing Driven by Bioinspired Algorith...

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International Conference in software Engineering Research and Innovation (CONISOFT)

作者： Joshua E. Hernández-Suárez Mario A. Jiménez-Jiménez Saúl Domínguez-Isidro Jorge Octavio Ocharán-Hernández Facultad de Estadistíca e Informática Universidad Veracruzana Xalapa México

ISBN: (数字)9798331532116

ISBN: (纸本)9798331532123

Bioinspired algorithms have gained relevance in Search-Based software Testing (SBST) mainly because they can find multiple solutions to difficult-to-solve problems. Exploring the usage trends and elements of its implementation is necessary for software engineering since it allows for understanding the area's current state and proposing guidelines for innovation. We proposed a Systematic Literature Mapping to analyze the canonical bioinspired algorithms used in SBST. During the mapping conduction, 60 studies were analyzed, in which bio-inspired algorithms were used in different software testing activities, such as test data generation, test case generation, test case prioritization, and others. The practical implications of our research are significant. The most commonly applied bioinspired algorithms in these activities are the Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization. Concerning problem types, most of these algorithms are focused on single-objective, with or without constraints, and, to a lesser extent, multi-objective and phased problems. According to the results, SBST driven by bio-inspired algorithms favors variables such as execution time and computational cost. Code coverage is usually used as a performance metric to evaluate its effectiveness. The application of canonical bioinspired algorithms mainly seeks to solve single-objective problems.

关键词： software testing Measurement Technological innovation Systematics Codes software algorithms Data collection Computational efficiency Genetic algorithms software engineering

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Approximation algorithms for software Component Selection Problem

Approximation Algorithms for Software Component Selection Pr...

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Asia-Pacific Conference on software Engineering

作者： Nima Haghpanah Shahrouz Moaven Jafar Habibi Mehdi Kargar Soheil Hassas Yeganeh Department of Computer Engineering Sharif University of Technology Tehran Iran

Today's software systems are more frequently composed from preexisting commercial or non-commercial components and connectors. These components provide complex and independent functionality and are engaged in complex interactions. Component-Based software Engineering (CBSE) is concerned with composing, selecting and designing such components. As the popularity of this approach and hence number of commercially available software components grows, selecting a set of components to satisfy a set of requirements while minimizing cost is becoming more difficult. This problem necessitates the design of efficient algorithms to automate component selection for software developing organizations. We address this challenge through analysis of Component Selection, the NP-complete process of selecting a minimal cost set of components to satisfy a set of objectives. Due to the high order of computational complexity of this problem, we examine approximating solutions that make the component selection process practicable. We adapt a greedy approach and a genetic algorithm to approximate this problem. We examined the performance of studied algorithms on a set of selected ActiveX components. Comparing the results of these two algorithms with the choices made by a group of human experts shows that we obtain better results using these approximation algorithms.

关键词： Approximation algorithms software algorithms Costs software systems Connectors software engineering Algorithm design and analysis Computational complexity Genetic algorithms Humans

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Knowledge-based software testing agent using evolutionary learning with cultural algorithms

Knowledge-based software testing agent using evolutionary le...

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Congress on Evolutionary Computation

作者： D.A. Ostrowski R.G. Reynolds Scientific Research Laboratory Ford Motor Company Limited Dearborn MI USA Department of Computer Science Wayne State University Detroit MI USA

software testing is extremely difficult in the context of large scale engineering applications. We suggest that the application of the white and black box testing methods within a cultural algorithm environment will present a successful approach to fault detection. In order to utilize both a functional approach and a structural approach, two cultural algorithms will be applied within this tool. The first cultural algorithm will utilize the black box testing by learning equivalence classes of faulty input/output pairs. These equivalence classes are then passed over to the second cultural algorithm that will apply program slicing techniques to determine program slices from the data. The goal will be to pinpoint specific faults within the program design. Through the searching of the program code, this approach can be considered as behavioral mining of a program.

关键词： software testing Cultural differences software algorithms System testing Large-scale systems Application software Laboratories Programming software maintenance software systems

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Using DPSO and B&B algorithms for Hardware/software Partitioning in Co-design

Using DPSO and B&B Algorithms for Hardware/Software Partitio...

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International Conference on Computer Research and Development (ICCRD)

作者： Taees Eimuri Sara Salehi Islamic Azad University Parand Parand Iran Southampton UK

ISBN: (纸本)9781424469925;9780769540436

This paper presents a hybrid method based on Discrete Particle Swarm Optimization (DPSO) and Branch-and-Bound (B&B) algorithms to solve Hardware/software partitioning problems. In this approach, some pre-defined relations are used to present a formal solution for the partitioning problem. Then, DPSO is used to increase the speed of B&B. Results of experiments show that in case the problem has any optimal solution, proposed approach can find it in a better time, in addition to in the more formal manner.

关键词： Partitioning algorithms software algorithms Hardware Costs Signal processing algorithms Embedded software Embedded system software performance Clustering algorithms Particle swarm optimization

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