The static page processing software is easily disturbed by code defects, which causes the static page processing software to be paralyzed, thus making the accuracy of the static page processing poor. In order to impro...
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The static page processing software is easily disturbed by code defects, which causes the static page processing software to be paralyzed, thus making the accuracy of the static page processing poor. In order to improve the automatic prediction capability of the static page processing software, a code defect prediction technology for the static page processing software based on big data fusion and defect feature location technology algorithm is proposed, and the syntax running state characteristics of the operation and maintenance control management layer and software source code of the static page processing software are analyzed and tested. Using polymorphic software to drive the control program to carry out fault feature monitoring and information fusion of the page static processing software, carrying out polymorphic factor fusion and state feature analysis on the large data of defect fault feature distribution in the pseudo code of the software control program, combining Boehm model and ISO/IEC 9126 model to realize fault feature point location and defect active prediction of the page static processing software, According to the logicality of functions, codes and state variables of the page static processing software, the method of software running program continuity and similarity feature detection is adopted to realize the self-adaptive defect prediction and positioning of the page static processing software, and the global convergence control in defect prediction of the page static processing software is carried out by combining the big data fusion and defect feature positioning algorithm. The simulation results show that the prediction accuracy of page static processing software defects using this method is higher, the localization of defect codes is better, and the reliable operation capability of page static processing software is improved.
Determining the effort required to make a transition is one of the key factors that help in the decision-making of an enterprise software upgrade. In the last decade, extensive research has been carried out on Softwar...
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ISBN:
(数字)9781728162218
ISBN:
(纸本)9781728162225
Determining the effort required to make a transition is one of the key factors that help in the decision-making of an enterprise software upgrade. In the last decade, extensive research has been carried out on software Development Effort Estimation. The results show various models and approaches taken towards arriving at the effort needed during software development. However, to the best of our knowledge, no effective model has been studied that can estimate change efforts of upgrading a large productive enterprise system. In this paper, we propose a new model that uses both an algorithmic and non-algorithmic approach and comes up with efforts needed during the upgrade of an enterprise software. The proposed model has been evaluated through an extensive experimental validation and was applied to the upgrade of three enterprise systems of different domains (namely, Enterprise Resource Planning, Customer Relationship Management and Human Resource). The results obtained showed that the model was 91% accurate in providing effort estimates for the above mentioned three systems.
[Purpose / significance] in order to maximize the sales volume of application software, strengthen the advantages of application software store, and assist the business department of application software store to make...
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ISBN:
(纸本)9781728192833
[Purpose / significance] in order to maximize the sales volume of application software, strengthen the advantages of application software store, and assist the business department of application software store to make business development strategic decisions. [method / process] considering various factors affecting software sales, the software sales data and user data are mined. With the help of the research results of artificial intelligence technology, the business decision-making model of software store is constructed by using random forest method. Taking the sales data of a software application store as a sample, this paper makes an experimental comparative analysis, adjusts and improves the model repeatedly to form a new decision model. [result / Conclusion] the experimental results show that the model is feasible and accurate, and can effectively help app stores to formulate appropriate business development strategies and improve software sales.
The following topics are dealt with: data visualisation; software engineering; software maintenance; program visualisation; software architecture; public domain software; Java; object-oriented programming; augmented r...
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ISBN:
(数字)9781728199146
ISBN:
(纸本)9781728199153
The following topics are dealt with: data visualisation; software engineering; software maintenance; program visualisation; software architecture; public domain software; Java; object-oriented programming; augmented reality; and graph theory.
Newly emerging nonvolatile alternatives to DRAM raise the possibility that applications might compute directly on long-lived data, rather than serializing them to and from a file system or database. To ensure crash co...
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Newly emerging nonvolatile alternatives to DRAM raise the possibility that applications might compute directly on long-lived data, rather than serializing them to and from a file system or database. To ensure crash consistency, such data must, like a file system or database, provide failure-atomic transactional semantics. Several persistent software transactional memory (STM) systems have been devised to provide these semantics, but only one-the OneFile system of Ramalhete et al.-is nonblocking. Nonblocking progress is desirable to avoid both performance anomalies due to process preemption or failures and deadlock due to priority inversion. Unfortunately, OneFile achieves nonblocking progress at the cost of 2 × space overhead, sacrificing much of the cost and density benefit of nonvolatile memory relative to DRAM. OneFile also requires extensive and intrusive changes to data declarations, and works only on a machine with double-width compare-and-swap (CAS) or load-linked/store-conditional (LL/SC) instructions. To address these limitations, we introduce QSTM, a nonblocking persistent STM that requires neither the modification of target data structures nor the availability of a wide CAS instruction. We describe our system, give arguments for safety and liveness, and compare performance to that of the Mnemosyne and OneFile persistent STM systems. We argue that modest performance costs (within a factor of 2 of OneFile in almost all cases) are easily justified by dramatically lower space overhead and higher programmer convenience.
software enhancement must be carefully planned and quantified to satisfy customer change requests, such as adding a new functionality, or deleting or changing an existing one. This paper investigates the use of M5P Ma...
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ISBN:
(数字)9781728185774
ISBN:
(纸本)9781728185781
software enhancement must be carefully planned and quantified to satisfy customer change requests, such as adding a new functionality, or deleting or changing an existing one. This paper investigates the use of M5P Machine Learning (ML) algorithm on predicting software enhancement effort. This M5P ML algorithm is trained and tested with 302 software enhancement projects obtained from the ISBSG dataset. The correlation-based feature selection (CFS) algorithm is used to achieve efficient data reduction. Thus, the selected ML techniques are trained on a dataset with relevant features that lead to improve the accuracy of their estimates. The Performance of the M5P using CFS is compared with the three Machine Learning Regression Methods (MLRM): Gradient Boosting Regression (GBRegr), Linear Support Vector Regression (LinearSVR), and Random Forest Regression (RFR). Results show that the prediction of software Enhancement Effort using Correlation-based Feature Selection and M5P is improved in terms of MAE (Mean Absolute Error) = 0.0612 and Root Mean Square Error (RMSE) = 0.2514.
The COVID-19 pandemic has resulted in widespread changes to how the higher education sector operates. In this paper, the experience of delivering an eight-week undergraduate software Engineering programme during the p...
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ISBN:
(纸本)9781728168074
The COVID-19 pandemic has resulted in widespread changes to how the higher education sector operates. In this paper, the experience of delivering an eight-week undergraduate software Engineering programme during the pandemic is discussed. The programme in question exhibits a number of unique features, including the intensive nature of the teaching, and the timing of its delivery, which coincided almost exactly with the introduction of lockdown measures. Reflections are offered on the rapid transition to online delivery of three different modules, including consideration of students' wellbeing. The implications for software Engineering education, and online education more broadly, are considered.
Conformal antenna arrays are of significant interest for automotive and aerospace applications. Such arrays can be conformed to the surface of vehicles and aircraft while providing minimum impact on aerodynamics. Howe...
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ISBN:
(数字)9781728166704
ISBN:
(纸本)9781728166711
Conformal antenna arrays are of significant interest for automotive and aerospace applications. Such arrays can be conformed to the surface of vehicles and aircraft while providing minimum impact on aerodynamics. However, the design and analysis of such antenna systems differs from the planar case. Also, beamforming and nulling for curved antenna arrays with directional elements requires modification of the beamforming and nulling algorithms. Here, a linear array of four elements is considered on a curved surface. Instead of using traditional beamforming with phase shifters, software defined radio (SDR) is used. A method is proposed to achieve adaptive beamforming and nulling of the flexible array, by using a SDR platform to feed the antenna array, in various conformed configurations. Minimum Variance Distortionless Response (MVDR) beamforming is used and adapted to the conformal antenna. Experimental results show good agreement with simulations.
The article substantiates the fundamental possibilities of implementing control algorithms for asynchronous electric drives, the structure of which differs from the standard algorithms for scalar and vector control. I...
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ISBN:
(数字)9781728180755
ISBN:
(纸本)9781728180762
The article substantiates the fundamental possibilities of implementing control algorithms for asynchronous electric drives, the structure of which differs from the standard algorithms for scalar and vector control. It is shown how specific programmable logic controllers from Schneider Electric can be used for these purposes, which are most often used to work with frequency converters to solve problems of controlling technological modes of operation of technological equipment. Examples of algorithms that implement dynamic positive feedback on the stator current of asynchronous electric motors without changing the software and hardware of the frequency converters are given. This control is very promising, since it opens up the possibility of introducing new control algorithms into frequency converters widely used in industry and power engineering.
The modern capabilities of intelligent systems are increasingly spreading in areas that were previously considered the exclusive work of people-experts with relevant experience in one area or another. The possibilitie...
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ISBN:
(数字)9781728191164
ISBN:
(纸本)9781728191171
The modern capabilities of intelligent systems are increasingly spreading in areas that were previously considered the exclusive work of people-experts with relevant experience in one area or another. The possibilities of machine learning in the electric power industry, obtaining forecasts based on data from intelligent sensors for various purposes, were no exception. This article proposes the concept of an intelligent system for controlling the condition monitoring process based on data from intelligent sensors. The novelty of the concept lies in considering a solution to the problem of integrating information systems associated with semi-structured subject-oriented information flows at an electric power enterprise using the methods of set theory and category theory.
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