A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple levels.Combining different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve performance.During the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these errors.Numerous studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming models.Despite the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been developed.Therefore,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot detect.For the first time,this paper presents a classification of operational errors that can result from the integration of the three models.This paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and OpenACC.This hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic technology.The hybrid technique can detect more errors because it combines two distinct technologies.The proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environment are left to the dynamic technology,which completes the validation.

Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering:An Innovative Multilingual Approach for Social Media Reviews

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

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.

Evaluating the Impact of Prediction Techniques: Software Reliability Perspective

Maintaining software reliability is the key idea for conducting quality research.This can be done by having less complex applications.While developers and other experts have made signicant efforts in this context,the level of reliability is not the same as it should be.Therefore,further research into the most detailed mechanisms for evaluating and increasing software reliability is essential.A signicant aspect of growing the degree of reliable applications is the quantitative assessment of reliability.There are multiple statistical as well as soft computing methods available in literature for predicting reliability of software.However,none of these mechanisms are useful for all kinds of failure datasets and applications.Hence nding the most optimal model for reliability prediction is an important concern.This paper suggests a novel method to substantially pick the best model of reliability prediction.This method is the combination of analytic hierarchy method(AHP),hesitant fuzzy(HF)sets and technique for order of preference by similarity to ideal solution(TOPSIS).In addition,using the different iterations of the process,procedural sensitivity was also performed to validate the ndings.The ndings of the software reliability prediction models prioritization will help the developers to estimate reliability prediction based on the software type.

A Survey on Software Cost Estimation Techniques

The ability to accurately estimate the cost needed to complete a specific project has been a challenge over the past decades. For a successful software project, accurate prediction of the cost, time and effort is a very much essential task. This paper presents a systematic review of different models used for software cost estimation which includes algorithmic methods, non-algorithmic methods and learning-oriented methods. The models considered in this review include both the traditional and the recent approaches for software cost estimation. The main objective of this paper is to provide an overview of software cost estimation models and summarize their strengths, weakness, accuracy, amount of data needed, and validation techniques used. Our findings show, in general, neural network based models outperforms other cost estimation techniques. However, no one technique fits every problem and we recommend practitioners to search for the model that best fit their needs.

Fault-tolerant Strategies and Their Design Methods for Application Software

The key to software reliability is fault-tolerant design ofapplication software.New fault-tolerant strategies andtheir design methods for application software under vari-ous computer system are introduced.It has such advan-tages as simple hardware platform,independent fromapplication,stable reliability.lastly,some technicalproblems are discussed in details.

A New Software for GIS Image Pixel Topographic Fac－tors in Remote Sensing Monitoring of Soil Losses

Based on the new algorithm for GIS image pixel topographic factors in remote sensing monitoring ofsoil losses, a software was developed for microcomputer to carry out computation at a medium river basin(county). This paper lays its emphasis on algorithmic skills and programming techniques as well as applicationof the software.

Numerical Techniques Used in Modeling Codes for Dual-Reflector Antenna Design and Analysis

An user-oriented computer software consisting of three modeling codes, named DRAD, DRAA and FDPAT, is introduced. It can be used to design three types of Cassegrain system: classical, with shaped subreflector and with dual shaped reflectors, and to analyse radiation patterns for the antennas. Several mathematical models and numerical techniques are presented.

Economical Requirements Elicitation Techniques During COVID-19: A Systematic Literature Review

Requirements elicitation is a fundamental phase of software development in which an analyst discovers the needs of different stakeholders and transforms them into requirements.This phase is cost-and time-intensive,and a project may fail if there are excessive costs and schedule overruns.COVID-19 has affected the software industry by reducing interactions between developers and customers.Such a lack of interaction is a key reason for the failure of software projects.Projects can also fail when customers do not know precisely what they want.Furthermore,selecting the unsuitable elicitation technique can also cause project failure.The present study,therefore,aimed to identify which requirements elicitation technique is the most cost-effective for large-scale projects when time to market is a critical issue or when the customer is not available.To that end,we conducted a systematic literature review on requirements elicitation techniques.Most primary studies identified introspection as the best technique,followed by survey and brainstorming.This finding suggests that introspection should be the first choice of elicitation technique,especially when the customer is not available or the project has strict time and cost constraints.Moreover,introspection should also be used as the starting point in the elicitation process of a large-scale project,and all known requirements should be elicited using this technique.

Knowledge Management of Software Productivity and Development Time

In this paper, we identify a set of factors that may be used to forecast software productivity and software development time. Software productivity was measured in function points per person hours, and software development time was measured in number of elapsed days. Using field data on over 130 field software projects from various industries, we empirically test the impact of team size, integrated computer aided software engineering (ICASE) tools, software development type, software development platform, and programming language type on the software development productivity and development time. Our results indicate that team size, software development type, software development platform, and programming language type significantly impact software development productivity. However, only team size significantly impacts software development time. Our results indicate that effective management of software development teams, and using different management strategies for different software development type environments may improve software development productivity.

A Hybrid Associative Classification Model for Software Development Effort Estimation

A mathematical model that makes use of data mining and soft computing techniques is proposed to estimate the software development effort. The proposed model works as follows: The parameters that have impact on the development effort are divided into groups based on the distribution of their values in the available dataset. The linguistic terms are identified for the divided groups using fuzzy functions, and the parameters are fuzzified. The fuzzified parameters then adopt associative classification for generating association rules. The association rules depict the parameters influencing the software development effort. As the number of parameters that influence the effort is more, a large number of rules get generated and can reduce the complexity, the generated rules are filtered with respect to the metrics, support and confidence, which measures the strength of the rule. Genetic algorithm is then employed for selecting set of rules with high quality to improve the accuracy of the model. The datasets such as Nasa93, Cocomo81, Desharnais, Maxwell, and Finnish-v2 are used for evaluating the proposed model, and various evaluation metrics such as Mean Magnitude of Relative Error, Mean Absolute Residuals, Shepperd and MacDonell’s Standardized Accuracy, Enhanced Standardized Accuracy and Effect Size are adopted to substantiate the effectiveness of the proposed methods. The results infer that the accuracy of the model is influenced by the metrics support, confidence, and the number of association rules considered for effort prediction.

基于多目标优化算法NSGA-Ⅱ的软件多样化组合方法

软件多样化因能有效提升系统弹性、增加恶意二进制分析的成本而被广泛应用于软件开发等场景中。而如何对现有软件多样化技术进行组合部署,以在获得更高安全增益的同时保持较低的性能开销,是当前软件多样化研究的核心问题之一。针对现有软件多样化组合方法中搜索算法效率低、搜索空间小、安全性评估指标不全面、难以综合考量软件多样化对各类攻击的影响等问题,提出了一种基于多目标优化算法的软件多样化组合方法,将软件多样化组合问题构建为综合考量TLSH相似度、gadget质量分数和CPU时钟周期数指标的多目标优化模型,并设计了包括染色体编码、自适应交叉和变异算子,以及针对组合方案的有效性验证算法等在内的NSGA-Ⅱ求解算法。最后,在GNU核心工具组数据集上进行实验,结果表明,该组合方法可有效生成高安全增益、低性能开销的软件多样化组合方案。

Oilfield analogy and productivity prediction based on machine learning: Field cases in PL oilfield, China

In the early time of oilfield development, insufficient production data and unclear understanding of oil production presented a challenge to reservoir engineers in devising effective development plans. To address this challenge, this study proposes a method using data mining technology to search for similar oil fields and predict well productivity. A query system of 135 analogy parameters is established based on geological and reservoir engineering research, and the weight values of these parameters are calculated using a data algorithm to establish an analogy system. The fuzzy matter-element algorithm is then used to calculate the similarity between oil fields, with fields having similarity greater than 70% identified as similar oil fields. Using similar oil fields as sample data, 8 important factors affecting well productivity are identified using the Pearson coefficient and mean decrease impurity(MDI) method. To establish productivity prediction models, linear regression(LR), random forest regression(RF), support vector regression(SVR), backpropagation(BP), extreme gradient boosting(XGBoost), and light gradient boosting machine(Light GBM) algorithms are used. Their performance is evaluated using the coefficient of determination(R^(2)), explained variance score(EV), mean squared error(MSE), and mean absolute error(MAE) metrics. The Light GBM model is selected to predict the productivity of 30 wells in the PL field with an average error of only 6.31%, which significantly improves the accuracy of the productivity prediction and meets the application requirements in the field. Finally, a software platform integrating data query,oil field analogy, productivity prediction, and knowledge base is established to identify patterns in massive reservoir development data and provide valuable technical references for new reservoir development.

Surface crack evolution patterns in freeze-thaw damage of fissured rock bodies

To explore the effects of freeze‒thaw cycles on the mechanical properties and crack evolution of fissured sandstone,biaxial compression experiments were carried out on sandstone subjected to freeze‒thaw cycles to characterize the changes in the physical and mechanical properties of fissured sandstone caused by freeze‒thaw cycles.The crack evolution and crack change process on the surface of the fissured sandstone were recorded and analysed in detail via digital image technology(DIC).Numerical simulation was used to reveal the expansion process and damage mode of fine-scale cracks under the action of freeze‒thaw cycles,and the simulation results were compared and analysed with the experimental data to verify the reliability of the numerical model.The results show that the mass loss,porosity,peak stress and elastic modulus all increase with increasing number of freeze‒thaw cycles.With an increase in the number of freeze‒thaw cycles,a substantial change in displacement occurs around the prefabricated cracks,and a stress concentration appears at the crack tip.As new cracks continue to sprout at the tips of the prefabricated cracks until the microcracks gradually penetrate into the main cracks,the displacement cloud becomes obviously discontinuous,and the contours of the displacement field in the crack fracture damage area simply intersect with the prefabricated cracks to form an obvious fracture.The damage patterns of the fractured sandstone after freeze‒thaw cycles clearly differ,forming a symmetrical"L"-shaped damage pattern at zero freeze‒thaw cycles,a symmetrical"V"-shaped damage pattern at 10 freeze‒thaw cycles,and a"V"-shaped damage pattern at 20 freeze‒thaw cycles.After 20 freeze‒thaw cycles,a"V"-shaped destruction pattern and"L"-shaped destruction pattern are formed;after 30 freeze‒thaw cycles,an"N"-shaped destruction pattern is formed.This shows that the failure mode of fractured sandstone gradually becomes more complicated with an increasing number of freeze‒thaw cycles.The effects of freeze‒thaw cycles on the direction and rate of crack propagation are revealed through a temperature‒load coupled model,which provides an important reference for an in-depth understanding of the freeze‒thaw failure mechanisms of fractured rock masses.

计算机软件安全漏洞检测技术与应用路径

软件本质是一种按照特定顺序组织的计算机数据和指令的集合,通常而言软件可以分为系统软件、应用软件和支撑软件等多种类型。其中系统软件是计算机在使用过程中最基本的软件类型,而应用软件是能够实现特定功能的软件系统,支撑软件则是帮助用户进行软件开发的工具性软件。相关软件通常不可避免地会存在漏洞,而这些漏洞会对软件的使用安全性和可靠性造成威胁,本文针对当前计算机软件安全漏洞的检测与应用进行研究,期望对后续的工作有所帮助。

高可信度音爆预测平台设计研究

分析了高可信度音爆数值预测平台在超音速民机低音爆设计中的应用现状及需求,重点阐述了某高可信度音爆预测平台的集成框架、应用流程、数据存储和交换、以及数据可视化等方面的设计策略,并提出了构建方案,为工程一线设计人员研制了友好易用的音爆预测软件。软件以音爆预测问题的可视化交互定义为基础,采用混合程序策略,可实现音爆信号的近场预测、远场预测及主观响应评价。同时,软件具有良好的开放性及兼容性,可灵活集成先进的音爆预测程序,快速完成音爆数值预测的最新研究成果的应用转化,为超音速民机低音爆设计研制提供技术和工具支撑。

地面探测业务系列软件安装技巧浅谈

根据近年来多次新装机经验的总结,以Windows10操作系统为例,探讨了地面探测业务在用机选购、安装系列软件技巧以及应注意细节等方面的问题。结果表明:Windows10操作系统的电脑适用于地面探测业务的开展,能兼容安装CTS2传输软件、Meinberg时钟对时软件、SMOPORT串口驱动程序、ISOS地面测报主软件、北斗传输软件,并建立VPN传输通道,从而高效快速地完成庞大的地面数据的采集、质控、监控、上传,有效地实现了地面探测业务的自动化、信息化、现代化。

分层技术在计算机软件开发中的应用

软件开发一直是信息技术领域的核心内容。随着软件需求的日益增长和复杂化,传统的软件开发方法已面临瓶颈。为提高软件开发效率和质量,分层技术应运而生。分层技术通过对系统进行分解,可提升软件的可维护性、可扩展性和灵活性。基于分层技术在计算机软件开发中的重要意义,文中以分层技术为切入点,详细分析了分层技术的概念、优势、发展历程和结构形式,重点阐述了分层技术在模块化设计、数据抽象、边界分离、接口设计等方面的具体应用,旨在总结分层技术在软件开发中的应用策略,为推动软件工程发展提供借鉴和参考。