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检索条件"任意字段=10th International Conference on Predictive Models in Software Engineering, PROMISE 2014"

共 161 条记录，以下是51-60 订阅

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A UML Profile for Prediction of Significant software Requirements

A UML Profile for Prediction of Significant Software Require...

引用

international conference and Workshop on Computing and Communication (IEMCON)

作者： Ayesha Tariq Farooque Azam Muhammad Waseem Anwar Bilal Maqbool Haider Ali Javaid Department of Computer & Software Engineering College of Electrical and Mechanical Engineering National University of Sciences & Technology (NUST) Islamabad Pakistan

ISBN: (纸本)9781728125312

the preliminary phase of the software development life cycle is Requirements engineering that is nearest to the user's world. this phase contains tasks that are knowledge concentrated. therefore, the practice of Bayesian Belief Network (BBN) for modelling this knowledge would be worthful assistance. Accordingly, predicting significant requirements is essential. When poorly identified, numerous problems happen, such as budget overrun, software failures and schedule overrun. However, this phase is usually not performed and skipped by assuming as an inconsequential phase. Significant requirements identification become more vital and challenging when the complexity of the software increases. therefore, managers and developers recurrently lose confidence in software artifacts. Wavering in the developer's confidence may in return distress the decisions of which requirement is to implement first. In this paper Bayesian belief network (BBN) approach for predicting significant requirements is proposed to solve the problem by UML Profile mechanism. this probabilistic model supports in accomplishing the uncertainties and inaccuracy generally exists in the requirements engineering process. It makes the prediction more precise, intuitionistic and reasonable. the Profile is imported into a UML tool, which helps in prompt validation of meta-model concepts in practice. the approach is practicable in a realistic context and addresses uncertainties.

关键词： Unified modeling language software Requirements engineering Bayes methods predictive models software engineering Mechanical engineering

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A LSTM Based Campus Network Traffic Prediction System

A LSTM Based Campus Network Traffic Prediction System

引用

IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Yue Geng Shuyu Li School of Computer Science Shaanxi Normal University Xi’an China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

Accurate and real-time campus network traffic prediction is very important in network management. Aiming at security analysis of network traffic and prediction problems caused by the nonlinearity and multi-dimensional dynamics of campus network traffic. A network traffic prediction system based on long-term/short-term memory (LSTM) model is presented for analysis of campus users' network behaviors in the paper. the prediction system uses immortal log analysis tool of Xijia Education to gather and pre-process multi-source heterogeneous log data from various network applications, and adopts an improved LSTM model to analyze and predict network traffic of campus users. Experimental results validate the effectiveness of the proposed system by using real campus network data.

关键词： Telecommunication traffic predictive models Computational modeling Analytical models Data models Training Computer science

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Storage Performance Prediction

Storage Performance Prediction

引用

IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Qiang Li Hui Li Kai Zhang State Key Laboratory of High-end Server & Storage Technology Beijing China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

Performance is one of the most important capabilities provided by storage systems. How to accurately understand the performance of storage systems and predict future performance requirements for storage is a major challenge. the traditional method is to avoid the problem through Over Provisioning. Buying more and more advanced storage devices. this solution greatly increases the purchase cost of data center. In this paper, dynamic time wrapping is introduced on the basis of fbprophet algorithm. the accuracy of storage performance prediction is further improved. Experiments show that our method can reduce the deviation value of MAPE to less than 10%. It can effectively provide the basis for storage managers to expand capacity and upgrade system.

关键词： Time series analysis Performance evaluation Business Prediction algorithms Bandwidth predictive models Heuristic algorithms

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A New Distributed Power System for Stability Prediction And Analysis

A New Distributed Power System for Stability Prediction And ...

引用

IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Durong Yin Yi Yang Mao Yang Zhigang Yang Caihong Li Lian Li School of Information Science and Engineering Lanzhou University Lanzhou China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

Grid system is prone to all kinds of disturbances. these disturbances only appear in a moment, but often cause cascading failures. Aiming at this problem, a prediction and analysis system for distributed power grid stability is developed. Firstly, combined with advanced data processing technology, the factors affecting the stability of power grid are analyzed and described. Secondly, a combined model KRR-XGBoost is proposed to predict the grid stability index, and the grid stability can be judged according to the predicted results. In order to verify the effectiveness of the model, we take the UCI distributed grid stability data set as an example to carry out the test. the experimental results show that compared with the other four models, this model can provide more accurate prediction results and better predict the stability of distributed power system, which further provides effective design guidance and cost optimization for distributed power supply system.

关键词： Power system stability predictive models Power grids Analytical models Stability criteria Prediction algorithms

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Research on Real-time Learning Prediction Method Based on Spark

Research on Real-time Learning Prediction Method Based on Sp...

引用

IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Shaolin Gong Xuan Qin Nanjing Les Cybersecurity and Informatization Technology Research Institute Co. Ltd Nanjing Jiangsu Province China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

Based on the research of real-time prediction and big data processing platform, an effective solution is proposed to solve the shortcomings of current real-time learning prediction in engineering application. By analyzing learners' learning behaviors related to a certain course, learners' learning behaviors can be divided into three categories in terms of time and space: online learning behaviors, offline learning behaviors and performance of relevant basic courses. Based on the parallel computation and binary logistic regression algorithm in Spark framework, the off-line learning prediction model is created. In the real-time environment, large scale real-time learning prediction can be realized based on Spark Streaming and kafka. With the increase of learning behaviors data, the scalability problem of prediction scheme can be solved by expanding Spark cluster nodes. the advantages of the proposed scheme have been verified in the practical application of smart campus.

关键词： Sparks predictive models Real-time systems Logistics Data models Machine learning Mathematical model

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Application of GA-Optimized ANN on Modeling the Performance of Coiled Adiabatic Capillary Tubes

Application of GA-Optimized ANN on Modeling the Performance ...

引用

IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Yuchen Zhou Guobing Zhou School of Software Engineering South China University of Technology Guangzhou Guangdong Province China School of Energy Power and Mechanical Engineering North China Electric Power University Beijing China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

An Artificial Neural Network (ANN) model optimized with Genetic Algorithms (GA) is applied to predict the mass flow rate in coiled adiabatic capillary tubes. Capillary tubes are the key flow control devices in small refrigeration and air conditioning units, which are usually coiled to save space. the flashing flow through coiled capillary tubes is much complex and the physical process is typically non-linear, which needs complicated mathematical model (conservative equations) for precise prediction. A GA-optimized ANN model is thus employed to address this challenging problem, which is valuable for the design of coiled capillary tubes in real applications. the training samples are from the experimental data on a one-pass-through test facility, which provides accurate source datasets. the results show that the predicted mass flow rates with GA-optimized ANN model agree well with the test data with an average error of 2.43%.

关键词： Electron tubes Artificial neural networks Mathematical model Genetic algorithms predictive models Temperature measurement Data models

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Trend Prediction of Github using Time Series Analysis

Trend Prediction of GitHub using Time Series Analysis

引用

international conference on Computing and Networking Technology (ICCNT)

作者： T V Varuna Anuraj Mohan NSS College of Engineering Palakkad India

Github is a web-based collaborative platform for software developers with around 28 million users and 57 million public repositories which promote social coding. Github is an evolving network with the developments in the projects and repositories. the underlying working principle of Github is based on the events occurring in a time-based manner. the number of events of a repository is proportional to its popularity. In this proposed work, we perform trend prediction on Github. the trend prediction is categorised into three tasks, repository trend prediction, language trend prediction and domain trend prediction respectively. Using most relevant and recurring events, such as Create, Fork, Pullrequest, Push and issue, a multivariate time series is constructed. Trends are predicted with time series forecasting using Long Short Term Memory(LSTM) model. the trend prediction results compare the repositories, languages and domains and help in identifying the trending ones. Github insights are taken to facilitate social coding and in selecting the relevant languages and domains for the upcoming projects.

关键词： Time series analysis Market research predictive models Logic gates Data mining Recurrent neural networks Collaboration

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Learning Behavior Analysis and Dropout Rate Prediction Based on MOOCs Data

Learning Behavior Analysis and Dropout Rate Prediction Based...

引用

IEEE international Symposium on Information (IT) in Medicine and Education, ITME

作者： Lutong Wang Hong Wang School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory of Distributed Computing Software Jinan China

With the continuous development of the MOOC, a large number of learners have joined the online classroom. Distance education has the advantage of being free from time and geographical restrictions. However, it still faces the dilemma of high dropout rate and the continuous loss of learners. By studying the MOOC log data, we model the various behaviors of students and hope to make more accurate predictions of dropout rates. the student's learning sequence information is essentially time-series data, and the time interval between events is often different, which leads to difficulties in prediction. therefore, we propose a time-controlled Long Short-Term Memory neural network (E-LSTM) prediction model that incorporates time-control units, the unit has the ability to model early learning behaviors with different time intervals. Based on the original LSTM model, we design time-controlled gates to better capture long-and short-term information and simulate learning process information to improve forecast performance. the experimental results on the real MOOC dataset show that the accuracy of the proposed model is higher than that of multiple comparison models, which proves the effectiveness of the proposed method.

关键词： Education Data models Logic gates predictive models Hidden Markov models Data mining Time series analysis

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An Approximate CTL Model Checking Approach

An Approximate CTL Model Checking Approach

引用

IEEE international conference on software engineering and Service Sciences (ICSESS)

作者： Weijun Zhu Pan Feng Miaolei Deng School of Information Engineering Zhengzhou University Zhengzhou Henan China School of Information Science & Engineering Henan University of Technology Zhengzhou Henan China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

the state space explosion is the main bottleneck of Computational Tree Logic (CTL) model checking. In the framework of traditional model checking technique, this problem is difficult to be solved completely. To this end, a method is proposed for predicting the results for CTL model checking using several Machine Learning (ML) algorithms. First, the data set with a number of Kripke structures, CTL formulas and their model checking results are obtained, using the existing CTL model checking tool. Second, Random Forest (RF), Boosted Trees (BT), Decision Trees (DT) and Logistic Regression (LR) algorithms are employed to train the data set. On the basis of it, the four ML models are obtained to predict the results of CTL model checking. the experiments show that the best accuracy of the new method is same as the existing CTL model checking methods, and its average efficiency is 378 times higher than that of the existing method, if the length of each of CTL formulas equals to 500.

关键词： Model checking Prediction algorithms Vegetation predictive models Approximation algorithms Radio frequency Machine learning

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software Defect Prediction: A Comparison Between Artificial Neural Network and Support Vector Machine 10th

Software Defect Prediction: A Comparison Between Artificial ...

引用

10th international conference on Advanced Computing and Communication Technologies (ICACCT)

作者： Arora, Ishani Saha, Anju Northern India Engn Coll FC 26Shastri Pk Delhi 110053 India Guru Gobind Singh Indraprastha Univ Univ Sch Informat & Commun Technol Sect 16C Delhi 110078 India

ISBN: (纸本)9789811046032;9789811046025

software industry has stipulated the need for good quality software projects to be delivered on time and within budget. software defect prediction (SDP) has led to the application of machine learning algorithms for building defect classification models using software metrics and defect proneness as the independent and dependent variables, respectively. this work performs an empirical comparison of the two classification methods: support vector machine (SVM) and artificial neural network (ANN), both having the predictive capability to handle the complex nonlinear relationships between the software attributes and the software defect. Seven data sets from the promise repository are used and the prediction models' are assessed on the parameters of accuracy, recall, and specificity. the results show that SVM is better than ANN in terms of recall, while the later one performed well along the dimensions of accuracy and specificity. therefore, it is concluded that it is necessary to determine the evaluation parameters according to the criticality of the project, and then decide upon the classification model to be applied.

关键词： Back propagation Supervised learning Artificial neural networks software quality Support vector machine

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