Building Custom Spreadsheet Functions with Python: End-User Software Engineering Approach

End-user computing empowers non-developers to manage data and applications, enhancing collaboration and efficiency. Spreadsheets, a prime example of end-user programming environments widely used in business for data analysis. However, Excel functionalities have limits compared to dedicated programming languages. This paper addresses this gap by proposing a prototype for integrating Python’s capabilities into Excel through on-premises desktop to build custom spreadsheet functions with Python. This approach overcomes potential latency issues associated with cloud-based solutions. This prototype utilizes Excel-DNA and IronPython. Excel-DNA allows creating custom Python functions that seamlessly integrate with Excel’s calculation engine. IronPython enables the execution of these Python (CSFs) directly within Excel. C# and VSTO add-ins form the core components, facilitating communication between Python and Excel. This approach empowers users with a potentially open-ended set of Python (CSFs) for tasks like mathematical calculations, statistical analysis, and even predictive modeling, all within the familiar Excel interface. This prototype demonstrates smooth integration, allowing users to call Python (CSFs) just like standard Excel functions. This research contributes to enhancing spreadsheet capabilities for end-user programmers by leveraging Python’s power within Excel. Future research could explore expanding data analysis capabilities by expanding the (CSFs) functions for complex calculations, statistical analysis, data manipulation, and even external library integration. The possibility of integrating machine learning models through the (CSFs) functions within the familiar Excel environment.

Static Extracting Method of Software Intended Behavior Based on API Functions Invoking

The method of extracting and describing the intended behavior of software precisely has become one of the key points in the fields of software behavior＇s dynamic and trusted authentication. In this paper, the author proposes a specified measure of extracting SIBDS （software intended behaviors describing sets） statically from the binary executable using the software＇s API functions invoking, and also introduces the definition of the structure used to store the SIBDS in detail. Experimental results demonstrate that the extracting method and the storage structure definition offers three strong properties： （i） it can describe the software＇s intended behavior accurately; （ii） it demands a small storage expense; （iii） it provides strong capability to defend against mimicry attack.

Service Function Chain in Small Satellite-Based Software Defined Satellite Networks

Software Defined Satellite Networks(SDSN) are proposed to solve the problems in traditional satellite networks, such as time-consuming configuration and inflexible traffic scheduling. The emerging application of small satellite and research of SDSN make it possible for satellite networks to provide flexible network services. Service Function Chain(SFC) can satisfy this need. In this paper, we are motivated to investigate applying SFC in the small satellite-based SDSN for service delivery. We introduce the structure of the multi-layer constellation-based SDSN. Then, we describe two deployment patterns of SFC in SDSN, the Multi-Domain(MD) pattern and the Satellite Formation(SF) pattern. We propose two algorithms, SFP-MD, and SFP-SF, to calculate the Service Function Path(SFP). We implement the algorithms and conduct contrast experiments in our prototype. Finally, we summarize the applicable conditions of two deployment patterns according to the experimental results in terms of hops, delay, and packet loss rate.

The Exploration of Talent Cultivation in Software Engineering“ Internet Plus Finance”

With the rapid development of the Internet finance industry,society has put forward urgent needs for“Internet Plus finance”composite talents,and also put forward challenges to talent cultivation in colleges and universities.To cultivate the talents in the“Internet Plus finance”,the School of Information and Software Engineering of the University of Electronic Science and Technology of China(hereinafter“the School”)begins the 10 major measures,such as the construction of training programs,the construction of the curriculum system,the construction of the faculty team,and the construction of the practice base.Meanwhile,the School works together with banks,which have cooperated for a long time,to establish the talent cultivation system in the direction of“Internet Plus finance”in the software engineering major,solving the problem of interdisciplinary talent training in the field of software engineering and finance.

Orchestrating Network Functions in Software-Defined Networks

Software.defined networking(SDN) enables third.part companies to participate in the network function innovations. A number of instances for one network function will inevitably co.exist in the network. Although some orchestration architecture has been proposed to chain network functions, rare works are focused on how to optimize this process. In this paper, we propose an optimized model for network function orchestration, function combination model(FCM). Our main contributions are as following. First, network functions are featured with a new abstraction, and are open to external providers. And FCM identifies network functions using unique type, and organizes their instances distributed over the network with the appropriate way. Second, with the specialized demands, we can combine function instances under the global network views, and formulate it into the problem of Boolean linear program(BLP). A simulated annealing algorithm is designed to approach optimal solution for this BLP. Finally, the numerical experiment demonstrates that our model can create outstanding composite schemas efficiently.

Estimation Models for Software Functional Test Effort

The International Software Benchmarking and Standards Group (ISBSG) data-base was used to build estimation models for estimating software functional test effort. The analysis of the data revealed three test productivity patterns representing economies or diseconomies of scale and these patterns served as a basis for investigating the characteristics of the corresponding projects. Three groups of projects related to the three different productivity patterns, characterized by domain, team size, elapsed time and rigor of verification and validation carried out during development, were found to be statistically significant. Within each project group, the variations in test effort can be explained, in addition to functional size, by 1) the processes executed during development, and 2) the processes adopted for testing. Portfolios of estimation models were built using combinations of the three independent variables. Performance of the estimation models built using the function point method innovated by the Common Software Measurement International Consortium (COSMIC) known as COSMIC Function Points, and the one advocated by the International Function Point Users Group (IFPUG) known as IFPUG Function Points, were compared to evaluate the impact of these respective sizing methods on test effort estimation.

Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction

The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods.

Local Radial Basis Function Methods: Comparison, Improvements, and Implementation

Radial Basis Function methods for scattered data interpolation and for the numerical solution of PDEs were originally implemented in a global manner. Subsequently, it was realized that the methods could be implemented more efficiently in a local manner and that the local approaches could match or even surpass the accuracy of the global implementations. In this work, three localization approaches are compared: a local RBF method, a partition of unity method, and a recently introduced modified partition of unity method. A simple shape parameter selection method is introduced and the application of artificial viscosity to stabilize each of the local methods when approximating time-dependent PDEs is reviewed. Additionally, a new type of quasi-random center is introduced which may be better choices than other quasi-random points that are commonly used with RBF methods. All the results within the manuscript are reproducible as they are included as examples in the freely available Python Radial Basis Function Toolbox.

Optimize the Deployment and Integration for Multicast-Oriented Virtual Network Function Tree

Due to the development of network technology,the number of users is increasing rapidly,and the demand for emerging multicast services is becoming more and more abundant,traffic data is increasing day by day,network nodes are becoming denser,network topology is becoming more complex,and operators’equipment operation and maintenance costs are increasing.Network functions virtualization multicast issues include building a traffic forwarding topology,deploying the required functions,and directing traffic.Combining the two is still a problem to be studied in depth at present,and this paper proposes a two-stage solution where the decisions of these two stages are interdependent.Specifically,this paper decouples multicast traffic forwarding and function delivery.The minimum spanning tree of traffic forwarding is constructed by Steiner tree,and the traffic forwarding is realized by Viterbi-algorithm.Use a general topology network to examine network cost and service performance.Simulation results show that this method can reduce overhead and delay and optimize user experience.

Maturity assessment model for aircraft collaborative design software solution

In order to assure quality and control process in the development of the aircraft collaborative design software, a maturity assessment model is proposed. The requirements designing—house of quality is designed to evaluate the maturity degree of the solution, and the evaluation results can help to manage and control the development process. Furthermore, a fuzzy evaluation method based on the minimum deviation is proposed to deal with the fuzzy information. The quantitative evaluation result of the maturity degree can be calculated by optimizing the semantic discount factor aim for the minimum deviation. Finally, this model is illustrated and analyzed by an example study of the aircraft collaborative design software.

Discrete Software Reliability Growth Modeling for Errors of Different Severity Incorporating Change-point Concept

Several software reliability growth models （SRGM） have been developed to monitor the reliability growth during the testing phase of software development. In most of the existing research available in the literatures, it is considered that a similar testing effort is required on each debugging effort. However, in practice, different types of faults may require different amounts of testing efforts for their detection and removal. Consequently, faults are classified into three categories on the basis of severity： simple, hard and complex. This categorization may be extended to r type of faults on the basis of severity. Although some existing research in the literatures has incorporated this concept that fault removal rate （FRR） is different for different types of faults, they assume that the FRR remains constant during the overall testing period. On the contrary, it has been observed that as testing progresses, FRR changes due to changing testing strategy, skill, environment and personnel resources. In this paper, a general discrete SRGM is proposed for errors of different severity in software systems using the change-point concept. Then, the models are formulated for two particular environments. The models were validated on two real-life data sets. The results show better fit and wider applicability of the proposed models as to different types of failure datasets.

Decomposition-Based Visual Function Specification and Auto-Generation of Function

On the software module, this paper proposes a visual specification language(VSL). Based on decomposition, the language imitates men's thinking procedure that decomposes aproblem into smaller ones, then independently solves the results of every small problem to get theresult of original problem (decomposition and synthesis). Besides, the language mixes visual withspecification. With computer supporting, we can implement the software module automatically. It willgreatly improve the quality of software and raise the efficiency of software development. Thesimple definition of VSL, the principle of auto-generation, an example and the future research areintroduced.

（Dis）Economies of Scale in Business Software Systems Development and Enhancement Projects

In the software engineering literature, it is commonly believed that economies of scale do not occur in case of software Development and Enhancement Projects （D＆EP）. Their per-unit cost does not decrease but increase with the growth of such projects product size. Thus this is diseconomies of scale that occur in them. The significance of this phenomenon results from the fact that it is commonly considered to be one of the fundamental objective causes of their low effectiveness. This is of particular significance with regard to Business Software Systems （BSS） D＆EP characterized by exceptionally low effectiveness comparing to other software D＆EP. Thus the paper aims at answering the following two questions： （1） Do economies of scale really not occur in BSS D＆EP？ （2） If economies of scale may occur in BSS D＆EP, what factors are then promoting them？ These issues classify into economics problems of software engineering research and practice.

Virtualizing Network and Service Functions: Impact on ICT Transformation and Standardization

Virtualization of network/service functions means time sharing network/service（and affiliated）resources in a hyper speed manner.The concept of time sharing was popularized in the 1970s with mainframe computing.The same concept has recently resurfaced under the guise of cloud computing and virtualized computing.Although cloud computing was originally used in IT for server virtualization,the ICT industry is taking a new look at virtualization.This paradigm shift is shaking up the computing,storage,networking,and ser vice industries.The hope is that virtualizing and automating configuration and service management/orchestration will save both capes and opex for network transformation.A complimentary trend is the separation（over an open interface）of control and transmission.This is commonly referred to as software defined networking（SDN）.This paper reviews trends in network/service functions,efforts to standardize these functions,and required management and orchestration.

Building Wave Functions of the Outer Electron for Alkaline Atoms Theory and Wave Functions Representation

The effects of the polarization potential serve to model spectra of alkaline atoms. These effects have been known for a long time and notably explained by the physicist Max Born (1926). The experimental knowledge of these alkaline spectra enables us to specify the values of these quantum defects. A simple code is used to calculate two quantum defects for which δl can be distinguished as: δs l = 0 and δp l = 1. On the theoretical part, it is possible to have an analytical expression for these quantum defects δl. A second code gives the correct wave functions modified by the quantum defects δl with the condition for the principal number: n* = n – δl ≥ 1. It is well known that δl → 0 when the kinetic momentum l ≥ 4, and for such momenta the spectra turns out to be hydrogenic. Modern software such as Mathematica, allows us to efficiently generate the polynomes defining wave functions with fractional quantum numbers. This leads to a good theoretical representation of these wave functions. To get numerically the quantum defects, a simple code is given to obtain these quantities when the levels assigned to a transition are known. Then, the quantum defects are inserted into the arguments of the correct modified wave functions for the outer electron of an atom or ion undergoing the short range polarization potential.

Software Defect Prediction Based on Non-Linear Manifold Learning and Hybrid Deep Learning Techniques

Software defect prediction plays a very important role in software quality assurance,which aims to inspect as many potentially defect-prone software modules as possible.However,the performance of the prediction model is susceptible to high dimensionality of the dataset that contains irrelevant and redundant features.In addition,software metrics for software defect prediction are almost entirely traditional features compared to the deep semantic feature representation from deep learning techniques.To address these two issues,we propose the following two solutions in this paper:(1)We leverage a novel non-linear manifold learning method-SOINN Landmark Isomap(SL-Isomap)to extract the representative features by selecting automatically the reasonable number and position of landmarks,which can reveal the complex intrinsic structure hidden behind the defect data.(2)We propose a novel defect prediction model named DLDD based on hybrid deep learning techniques,which leverages denoising autoencoder to learn true input features that are not contaminated by noise,and utilizes deep neural network to learn the abstract deep semantic features.We combine the squared error loss function of denoising autoencoder with the cross entropy loss function of deep neural network to achieve the best prediction performance by adjusting a hyperparameter.We compare the SL-Isomap with seven state-of-the-art feature extraction methods and compare the DLDD model with six baseline models across 20 open source software projects.The experimental results verify that the superiority of SL-Isomap and DLDD on four evaluation indicators.

A Software-Defined Approach to IoT Networking

It is foreseen that the Internet of Things (IoT) will comprise billions of connected devices, and this will make the provi?sioning and operation of some IoT connectivity services more challenging. Indeed, IoT services are very different from lega?cy Internet services because of their dimensioning figures and also because IoT services differ dramatically in terms of na?ture and constraints. For example, IoT services often rely on energy and CPU?constrained sensor technologies, regardless of whether the service is for home automation, smart building, e?health, or power or water metering on a regional or national scale. Also, some IoT services, such as dynamic monitoring of biometric data, manipulation of sensitive information, and pri?vacy needs to be safeguarded whenever this information is for?warded over the underlying IoT network infrastructure. This paper discusses how software?defined networking (SDN) can facilitate the deployment and operation of some advanced IoT services regardless of their nature or scope. SDN introduces a high degree of automation in service delivery and operation-from dynamic IoT service parameter exposure and negotiation to resource allocation, service fulfillment, and assurance. This paper does not argue that all IoT services must adopt SDN. Rather, it is left to the discretion of operators to decide which IoT services can best leverage SDN capabilities. This paper only discusses managed IoT services, i.e., services that are op?erated by a service provider.

On the Use of E-Learning Software Data--with Speexx Foreign Lan guage Learning System Being the Case

E-learning produces the data on the learners’utilization of the software,which helps the teacher to perceive the learners’mental status and learning efficiency,so it is of great value to make full use of the data.With Speexx foreign language learning system being the case,this thesis introduces the function of such data and the modes of how to use them to facilitate the blendedteaching and learning.

A comparison of piecewise cubic Hermite interpolating polynomials,cubic splines and piecewise linear functions for the approximation of projectile aerodynamics

Modelling and simulation of projectile flight is at the core of ballistic computer software and is essential to the study of performance of rifles and projectiles in various engagement conditions.An effective and representative numerical model of projectile flight requires a relatively good approximation of the aerodynamics.The aerodynamic coefficients of the projectile model should be described as a series of piecewise polynomial functions of the Mach number that ideally meet the following conditions:they are continuous,differentiable at least once,and have a relatively low degree.The paper provides the steps needed to generate such piecewise polynomial functions using readily available tools,and then compares Piecewise Cubic Hermite Interpolating Polynomial(PCHIP),cubic splines,and piecewise linear functions,and their variant,as potential curve fitting methods to approximate the aerodynamics of a generic small arms projectile.A key contribution of the paper is the application of PCHIP to the approximation of projectile aerodynamics,and its evaluation against a set of criteria.Finally,the paper provides a baseline assessment of the impact of the polynomial functions on flight trajectory predictions obtained with 6-degree-of-freedom simulations of a generic projectile.

Using the Cooperative Game for Service Placement of Virtual Network Functions

To address the issues that middleboxes as a fundamental part of today's networks are facing, Network Function Virtualization(NFV)has been recently proposed, which in essence asserts to migrate hardware-based middleboxes into software-based virtualized function entities.Due to the demands of virtual services placement in NFV network environment, this paper models the service amount placement problem involving with the resources allocation as a cooperative game and proposes the placement policy by Nash Bargaining Solution(NBS). Specifically,we first introduce the system overview and apply the rigorous cooperative game-theoretic guide to build the mathematical model, which can give consideration to both the responding efficiency of service requirements and the allocation fairness.Then a distributed algorithm corresponding to NBS is designed to achieve predictable network performance for virtual instances placement.Finally, with simulations under various scenarios,the results show that our placement approach can achieve high utilization of network through the analysis of evaluation metrics namely the satisfaction degree and fairness index. With the suitable demand amount of services, the average values of two metrics can reach above 90%. And by tuning the base placement, our solution can enable operators to flexibly balance the tradeoff between satisfaction and fairness of resourcessharing in service platforms.