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The Influence of Deep Learning algorithms Factors in software Fault Prediction

IEEE ACCESS 2020年 8卷 63945-63960页

作者： Al Qasem, Osama Akour, Mohammed Alenezi, Mamdouh Yarmouk Univ Informat Syst Dept Irbid 21163 Jordan Al Yamamah Univ Comp Engn Dept Riyadh 13541 Saudi Arabia Prince Sultan Univ Comp Sci Dept Riyadh 11586 Saudi Arabia

The discovery of software faults at early stages plays an important role in improving software quality;reduce the costs, time, and effort that should be spent on software development. Machine learning (ML) have been widely used in the software faults prediction (SFP), ML algorithms provide varying results in terms of predicting software fault. Deep learning achieves remarkable performance in various areas such as computer vision, natural language processing, speech recognition, and other fields. In this study, two deep learning algorithms are studied, Multi-layer perceptron & x2019;s (MLPs) and Convolutional Neural Network (CNN) to address the factors that might have an influence on the performance of both algorithms. The experiment results show how modifying parameters is directly affecting the resulting improvement, these parameters are manipulated until the optimal number for each of them is reached. Moreover, the experiments show that the effect of modifying parameters had an important role in prediction performance, which reached a high rate in comparison with the traditional ML algorithm. To validate our assumptions, the experiments are conducted on four common NASA datasets. The result shows how the addressed factors might increase or decrease the fault detection rate measurement. The improvement rate was as follows up to 43.5 & x0025;for PC1, 8 & x0025;for KC1, 18 & x0025;for KC2 and 76.5 & x0025;for CM1.

关键词： software Deep learning Prediction algorithms software algorithms Biological neural networks Predictive models Computer architecture Deep learning algorithms software fault prediction classification hyper parameters

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Evaluating Multi-Channel Multi-Device Speech Separation algorithms in the Wild: A Hardware-software Solution

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2020年 28卷 1428-1439页

作者： Ceolini, Enea Kiselev, Ilya Liu, Shih-Chii Univ Zurich Inst Neuroinformat CH-8057 Zurich Switzerland Swiss Fed Inst Technol CH-8057 Zurich Switzerland

Evaluation methods for multi-channel speech separation algorithms in the real world are becoming increasingly important as the number of applications involving audio assistants and hearing aid devices continues to grow. To make such evaluations easier, this paper presents a multi-microphone hardware platform, WHISPER, built specifically for this purpose and its subsequent use for evaluating speech processing algorithms. The platform can also be constructed as an ad-hoc wireless acoustic sensor network (WASN) with high synchronization precision. Using WHISPER, we describe real-world experiments where an example speech separation algorithm is applied to mixtures of varying number of talkers and signal-to-noise ratios. The results when compared with those from a simulated environment, show the usefulness of WASNs and that simulations tend to underestimate the difficulty of speech separation in real-world scenarios. This work represents an important step towards developing a hardware-software framework for evaluating speech processing algorithms in the wild.

关键词： Clocks Synchronization Microphones Signal processing algorithms Speech processing Hardware software algorithms array signal processing wireless acoustic sensor networks speech separation submicrosecond synchronization

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algorithms AND software TOOLS FOR IC YIELD OPTIMIZATION BASED ON FUNDAMENTAL FABRICATION PARAMETERS

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 1986年第1期5卷 79-89页

作者： STYBLINSKI, MA OPALSKI, LJ Technical University of Warsaw Warsaw Poland Department of Electrical Engineering Texas A and M University College Station TX USA

algorithms, software tools and the relevant methodology for production yield optimization with respect to fundamental technological parameters of the IC manufacturing process (such as diffusion times and temperatures) and element layout mask dimensions are discussed. The tools developed include: STOCH-PAC--a package of new yield optimization and yield gradient estimation algorithms based on Stochastic Approximation approach and the Method of Random Perturbations; YIELD-PAC - a package of yield evaluation subroutines providing an interface to SPICE-PAC circuit simulation package; FABPAC - an interface to the FABRICS statistical process simulator; and IRIS (Interactive Restructurable Interface System)--a flexible user interface for efficient data manipulation, creation of different design tasks and macrotasks, restructuring the set of active subroutines, etc. These tools have been integrated into the TOY (Technological Optimization of Yield) program. Examples of yield optimization with respect to process parameters and layout dimensions using TOY are given.

关键词： software algorithms software tools Fabrication Packaging Optimized production technology Circuit simulation Integrated circuit layout Manufacturing processes Temperature Design optimization

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AUTOMATING THE SIMULATION OF COMPLEX DISCRETE-TIME CONTROL-SYSTEMS - A MATHEMATICAL FRAMEWORK, algorithms, AND A software PACKAGE

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1994年第9期39卷 1795-1801页

作者： ELLI, SRD RAVIKANTH, R KUMAR, PR UNIV ILLINOIS DEPT ELECT & COMP ENGNURBANAIL 61801 UNIV ILLINOIS COORDINATED SCI LABURBANAIL 61801

With the rapid proliferation of complex control systems, such as adaptive control systems, a need has arisen to automate as much as possible of the time-consuming preparatory work needed to simulate such systems. This burden currently falls on the user. The smaller the user time required and the more effortless the process, the greater, we believe, will be the eventual widespread acceptability of these control systems. This paper is aimed at bridging this gap between the theoretical specification of a complex discrete-time control system as described in a typical research paper and the obtaining of a first simulation. It provides a graph theoretical formulation of the issues involved, discrete algorithms to execute these tasks, and finally describes ISIM, an interpretive simulator which incorporates these features. These algorithms automate the simulation process, as seen by the user. The net result is a package which essentially requires only that the user type in a control system more or less verbatim from a technical paper to obtain a simulation output.

关键词： Control system synthesis Automatic control Packaging software algorithms software packages Adaptive control Computational modeling Equations Control systems Discrete event simulation

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Performance Comparison of software Reliability Estimation algorithms

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COMPUTER 2024年第4期57卷 26-36页

作者： Yano, Hiromu Dohi, Tadashi Okamura, Hiroyuki Hiroshima Univ Adv Sci & Engn Hiroshima 7390046 Japan

Specific optimization algorithms have been developed for the purpose of automated software reliability assessment tools. In this article, we propose the Monte Carlo expectation-maximization algorithm as another optimization algorithm, and carry out the performance comparison of the software reliability estimation algorithms through comprehensive numerical experiments.

关键词： Monte Carlo methods software algorithms Estimation software reliability Expectation-maximization algorithms Optimization

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GENERALIZED EIGENPROBLEM algorithms AND software FOR ALGEBRAIC RICCATI-EQUATIONS

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PROCEEDINGS OF THE IEEE 1984年第12期72卷 1746-1754页

作者： ARNOLD, WF LAUB, AJ UNIV CALIF SANTA BARBARA DEPT ELECT & COMP ENGN SANTA BARBARA CA 93106 USA

Numerical issues related to the computational solution of the algebraic matrix Riccati equation are discussed. The approach presented uses the generalized eigenproblem formulation for the solution of general forms of algebraic Riccati equations arising in both continuous- and discrete-time applications. These general forms result from control and filtering problems for systems in generalized (or implicit or descriptor) state space form. A Newtontype iterative refinement procedure for the generalized Riccati solution is given. The issue of numerical condition of the Riccati problem is addressed. Balancing to improve numerical condition is discussed. An overview of a software package, RICPACK, coded in portable, reliable Fortran is given. Results of numerical experiments are reported.

关键词： Riccati equations software algorithms software packages Application software Control systems Filtering Symmetric matrices Transmission line matrix methods Nonlinear equations Difference equations

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Enhanced Performance of software Defined Network (SDN) Through Meta Heuristic Particle Swarm Optimization Random Forest Algorithm

Enhanced Performance of Software Defined Network (SDN) Throu...

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International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Deepak Bishla Brijesh Kumar School of Engineering and Technology Manav Rachna International Institute of Research Studies (MRIIRS) Faridabad

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Integration of software-defined Networking (SDN) with traditional networking technologies is crucial due to the increasing demand for network performance. SDN offers a centralized and programmable solution for managing the network, while legacy networking systems provide stability and compatibility with existing infrastructures. However, integrating these two systems often results in suboptimal network performance. The Particle Swarm Optimization (PSO) and Random Forest (RF) algorithm is suggested in this research as a potential solution to this problem of network performance improvement in SDN-legacy network integration. PSO and RF are metaheuristic algorithms that have been successfully applied to many optimization problems, including network optimization. The proposed solution used PSO to optimize the routing of packets in the SDN-legacy network, resulting in improved network performance and reduced latency. Simulation outcomes demonstrate the success of the proposed solution and show that it outperforms existing solutions in terms of network performance. When the outcomes are contrasted with other field-wide algorithms already in use, it is found that PSO and RF perform better than the competitors. The results show that PSO-RF decreases end-to-end delay and enhance the performance from 99% to 100% compare to other meta-heuristic algorithms.

关键词： Radio frequency Simulation Metaheuristics software algorithms Throughput Routing Stability analysis software defined networking Particle swarm optimization Genetic algorithms

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Service Anycast Forwarding for software Defined Computing Power Network

Service Anycast Forwarding for Software Defined Computing Po...

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IEEE Conference on Wireless Communications and Networking

作者： Wenzheng Wang Renchao Xie Qinqin Tang Tao Huang Tianjiao Chen Zehui Xiong State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China China Mobile Research Institute Pillar of Information Systems Technology and Design Singapore University of Technology and Design

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

With the rise of the computing power network (CPN), which integrate edge computing, cloud computing, and network infrastructure, replicated computing services are increasingly distributed to meet user demands for location-independent, reliable, and low latency services. Service anycast forwarding coordinates distributed service instances by binding them to a unified identifier and dynamically routing requests to the optimal instance. However, challenges such as varying user demand distribution, network complexity, and service instance heterogeneity complicate balanced service forwarding. To address these, we propose an SDN-based service anycast forwarding mechanism for CPN (SA-CPN). In the data plane, a cyclic forwarding queue efficiently maps weighted strategies and selects instances for each service request, improving policy performance. In the control plane, an optimal transport model balances network and computation latency based on service instance capabilities. We further design an optimal transport-based service anycast forwarding algorithm (OTSAF) using Sinkhorn iterations. Our implementation of SA-CPN in a real system shows that OTSAF consistently outperforms four baseline methods across various performance metrics.

关键词： Computational modeling software algorithms Distribution networks Routing software Complexity theory Reliability software defined networking Low latency communication Edge computing

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Explainable-ML-based Framework to Predict software Defects in Real-Time Control Systems

Explainable-ML-based Framework to Predict Software Defects i...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Dhruv Sarju Thakkar Vanshil Vaghasiya Vivek Kumar Prasad Rajesh Gupta Sudeep Tanwar Department of Comp. Sci. & Engg. Institute of Technology Nirma University Ahmedabad India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Machine Learning (ML) algorithms have significantly improved software engineering activities by examining software metrics and repositories, leading to more accurate predictions and improved software quality. Here, we have used five Machine learning models, which are Random Forest, Bootstrap aggregating, Extreme Gradient Boosting algorithm and K-Nearest Neighbours algorithm, to compare which performs the best in software fault prediction, specifically targeting real-time control systems where reliability and responsiveness are critical. Comparing the results of the model we infer that the boosting algorithm has the highest accuracy, while still maintaining other evaluation scores. After thorough evaluation we got the maximum accuracy of 0.78 with the bagging algorithm. We have also added a layer of transparency by using X-AI. For this, we have used Shapley Additive Explanation (SHAP) and Local Interpretable Model (LIME). The results from both techniques support each other, and the feature which has the maximum impact is the number of lines of code. X-AI enhances predictability by unveiling the black box and revealing what features have the most impact on classification, which is especially important in ensuring the stability and safety of real-time control systems.

关键词： Machine learning algorithms Accuracy software algorithms software quality Predictive models Prediction algorithms Real-time systems Stability analysis software reliability Bagging

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Improving software Quality Assurance Using a Cross-Context-Attentive Cosine Convolutional Model

Improving Software Quality Assurance Using a Cross-Context-A...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Deena Sunil Devika Dabke Surendra Kumar Saini Ravendra Ratan Singh Jandail MWP Maduranga Sharada Nandan Raw Department of Mathematics Indira Gandhi National Tribal University Amarkantak Madhya Pradesh Department of Mathematics Central University of Karnataka Gulbarga Karnataka Department of Mathematics Indira Gandhi National Tribal University Amarkantak MadhyaPradesh Department of Computer Science South Asian University New Delhi New Delhi Department of Electrical and Electronic Engineering University of Sri JayewardenepuraNugegoda Department of Mathematics National Institute of Technology Raipur Raipur Chhattisgarh

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The software quality determines system performance and reliability. As software systems are becoming increasingly complex, this is an important determinant. Detection of defects at the early stages of the development lifecycle improves software reliability and reduces costs. Traditional software Defect Prediction (SDP) models suffer from class imbalance, complexity of datasets, and inefficient use of contextual information, which affect the accuracy of their predictions. Therefore, the development of reliable software systems still requires a more robust and efficient defect prediction model. In this manuscript, Improving software Quality Assurance Using a Cross-Context-Attentive Cosine Convolutional Model (Sox-CNN-XA) is proposed. This study uses the NASA-MDP dataset containing historical records of software defects and various software metrics. Data pre-processing included data cleaning, normalization, and splitting, which was done in order to enhance the quality of the data and make it ready for training and validation purposes. A new framework for software Defect Prediction (SDP) has been proposed by using the integration of Cosine Convolutional Neural Networks (Cos-CNN) and Cross-Contextual Attention (XCA), which captures spatial and contextual features of data effectively. The hyperparameters in this model have been fine-tuned with Sardine Optimization Algorithm (SOA) in order to get improved performances over diverse datasets. Experimental results show that the proposed framework achieves an accuracy of 99.9%, a true positive rate of 99.9%, and a precision of 99.8% while maintaining a false positive rate of 7.2%. The results clearly prove that the model is performing better than traditional methods. The conclusion shows that the framework can increase the reliability of defect prediction, thus enabling software practitioners to deliver high-quality and maintainable software within tight deadlines and budgets

关键词： Training Accuracy software algorithms software quality Predictive models software systems Prediction algorithms software reliability Convolutional neural networks Optimization

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