Construction and Implementation of OBE-based Practical Course for Software System Development with Solid Foundation and Continuous Improvement

Software engineering is a highly practical major,and students need a lot of hands-on practice to transform the theoretical contents learned in class into the practical ability to solve practical problems,so practical courses are an essential and important part in the process of training talents in software engineering.From the point of view of cultivating talents in software engineering,this paper expounds the important position of practical courses in software engineering in the process of cultivating talents,analyzes the problems in the existing practical courses,and puts forward the construction ideas and characteristics of practical courses in software engineering which strengthen the foundation,advance steadily,and face the output.Taking the practical course for software system development as an example,this paper introduces in detail the concrete implementation process,achievements,existing problems and countermeasures of the course.

A MySQL-Based Software System of Urban Land Planning Database of Shanghai in China

With the development of the digital city,data and data analysis have become more and more important.The database is the foundation of data analysis.In this paper,the software system of the urban land planning database of Shanghai in China is developed based on MySQL.The conceptual model of the urban land planning database is proposed,and the entities,attributes and connections of this model are discussed.Then the E-R conceptual model is transformed into a logical structure,which is supported by the relational databasemanagement system(DBMS).Based on the conceptual and logical structures,by using Spring Boot as the back-end framework and using MySQL and Java API as the development tools,a platformwith datamanagement,information sharing,map assistance and other functions is established.The functionalmodules in this platformare designed.The results of J Meter test show that the DBMS can add,store and retrieve information data stably,and it has the advantages of fast response and low error rate.The software system of the urban land planning database developed in this paper can improve the efficiency of storing and managing land data,eliminating redundant data and sharing data.

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.

The complexity nature of large-scale software systems

In software engineering, class diagrams are often used to describe the system＇s class structures in Unified Modelling Language （UML）. A class diagram, as a graph, is a collection of static declarative model elements, such as classes, interfaces, and the relationships of their connections with each other. In this paper, class graphs axe examined within several Java software systems provided by Sun and IBM, and some new features are found. For a large-scale Java software system, its in-degree distribution tends to an exponential distribution, while its out-degree and degree distributions reveal the power-law behaviour. And then a directed preferential-random model is established to describe the corresponding degree distribution features and evolve large-scale Java software systems.

A Study of Performance Testing in Configurable Software Systems

Customizing applications through program configuration options has been proved by many open-source and commercial projects as one of the best practices in software engineering. However, traditional performance testing is not in synch with this industrial practice. Traditional performance testing techniques consider program inputs as the only external factor. It ignores the performance influence of configuration options. This study aims to stimulate research interest in performance testing in the context of configurable software systems by answering three research questions. That is, why it is necessary to conduct research in performance testing, what are the state-of-the-art techniques, and how do we conduct performance testing research in configurable software systems. In this study, we examine the unique characteristics and challenges of performance testing research in configurable software systems. We review and discuss research topics on the performance bug study, performance anti-patterns, program analysis, and performance testing. We share the research findings from the empirical study and outline the opening opportunities for new and advanced researchers to contribute to the research community.

Models for Improving Software System Size Estimates during Development

This paper addresses the challenge of estimating eventual software system size during a development project. The ap-proach is to build a family of estimation models that use information about architectural design characteristics of the evolving software product as leading indicators of system size. Four models were developed to provide an increasingly accurate size estimate throughout the design process. Multivariate regression analyses were conducted using 21 Ada subsystems, totaling 183,000 lines of code. The models explain from 47% of the variation in delivered software size early in the design phase, to 89% late in the design phase.

REAL-TIME SOFTWARE SYSTEM FOR PRODUCTION PROCESS SUPERVISION

The real-time software system for production process supervision is a inte-gral system,including on-line function subsystem for supervision and off-line auxiliarysubsystem for development and diagnosis.This paper introduces a real-time software sys-tem which has been used in a power station for monitoring a large capacity thermal gener-ating unit.The subsystems,environment,performance and development of the system areexplained,and the common problems about real-time software system are described.

Research on Software-Cell-Based Software System

The aim of research on software architecture is to improve the quality attributes of software systems, such as security, reliability, maintainability, testability, reassembility, evolvability. However, a single running system is hard to achieve all these goals. In this paper, software-cell is introduced as the basic unit throughout developing process. Then it is further advanced that a robust, safe and high-quality software system is composed of a running system and four supportive systems. This paper especially discusses the structure of software-cell, the construction of the five systems, and the relations between them.

A Fuzzy Approach for Component Selection amongst Different Versions of Alternatives for a Fault Tolerant Modular Software System under Recovery Block Scheme Incorporating Build-or-Buy Strategy

Software projects generally have to deal with producing and managing large and complex software products. As the functionality of computer operations become more essential and yet more critical, there is a great need for the development of modular software system. Component-Based Software Engineering concerned with composing, selecting and designing components to satisfy a set of requirements while minimizing cost and maximizing reliability of the software system. This paper discusses the fuzzy approach for component selection using “Build-or-Buy” strategy in designing a software structure. We introduce a framework that helps developers to decide whether to buy or build components. In case a commercial off-the-shelf (COTS) component is selected then different versions are available for each alternative of a module and only one version will be selected. If a component is an in-house built component, then the alternative of a module is selected. Numerical illustrations are provided to demonstrate the model developed.

Imperfection of Domain Knowledge and Its Formalization in Context of Design of Robust Software Systems

In this paper, it is emphasized that taking into consideration of imperfection of knowledge, of the team of the designers/developers, about the problem domains and environments is essential in order to develop robust software metrics and systems. In this respect, first various possible types of imperfections in knowledge are discussed and then various available formal/mathematical models for representing and handling these imperfections are discussed. The discussion of knowledge classification & representation is from computational perspective and that also within the context of software development enterprise, and not necessarily from organizational management, from library & information science, or from psychological perspectives.

Rational Cost Estimation of Dedicated Software Systems

Dedicated software systems hold a character of individual solutions that entail particular problems with regard to their cost estimation. Thus for many years now objective and reliable approaches to the cost estimation of such systems have been sought out so that they could provide the possibility to make rational investment decisions concerning those systems. The purpose of this paper is to bring in the approach to cost estimation of dedicated software systems that has been recently growing in global popularity, using it as a background for presenting conclusions resulting from practical verification of author’s own model of dedicated software system cost estimation based on that approach.

Software System Rejuvenation Modeling Based on Sequential Inspection Periods and State Multi-control Limits

This paper addresses the issue of software rejuvenation modeling.Rejuvenation strategies with sequential inspection periods and state multi-control limits are proposed here because the inspection-based approach involves the sampling of longer fixed periods of the state of system, which increases the probability of soft failure. The degradation process of the software system interferes with inspection and rejuvenation is modeled as a Markov chain. The steady-state probability density function of the system is thus derived, and a numerical solution of the function is provided. Expressions for mean unavailability time are derived during the inspection period when soft failure occurs. Finally, the steady-state availability of the system is modeled, and the solution to it is obtained using a genetic algorithm. The effectiveness of the model was verified by numerical experiments. Compared with rejuvenation strategies with fixed inspection periods, those with sequential inspection periods yielded greater steady-state availability of the software system.

CRAB—CombinatoRial Auction Body Software System

Auctions are important market mechanisms for the allocation of goods and services. Combinatorial auctions are those auctions in which buyers can place bids on combinations of items. Combinatorial auctions have many applications. The paper presents the CRAB software system. CRAB is a non-commercial software system for generating, solving, and testing of combinatorial auction problems. The system solves problems by Balas’ method or by the primal-dual algo-rithm. CRAB is implemented in Ruby and it is distributed as the file crab.rb. The system is freely available on web pag-es for all interested

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

Auxiliary Software for Defining the Parameters of the Structural Organization of a Complex System

The developed auxiliary software serves to simplify, standardize and facilitate the software loading of the structural organization of a complex technological system, as well as its further manipulation within the process of solving the considered technological system. Its help can be especially useful in the case of a complex structural organization of a technological system with a large number of different functional elements grouped into several technological subsystems. This paper presents the results of its application for a special complex technological system related to the reference steam block for the combined production of heat and electricity.

Forecasting technical debt evolution in software systems:an empirical study

Technical debt is considered detrimental to the long-term success of software development,but despite the numerous studies in the literature,there are still many aspects that need to be investigated for a better understanding of it.In particular,the main problems that hinder its complete understanding are the absence of a clear definition and a model for its identification,management,and forecasting.Focusing on forecasting technical debt,there is a growing notion that preventing technical debt build-up allows you to identify and address the riskiest debt items for the project before they can permanently compromise it.However,despite this high relevance,the forecast of technical debt is still little explored.To this end,this study aims to evaluate whether the quality metrics of a software system can be useful for the correct prediction of the technical debt.Therefore,the data related to the quality metrics of 8 different open-source software systems were analyzed and supplied as input to multiple machine learning algorithms to perform the prediction of the technical debt.In addition,several partitions of the initial dataset were evaluated to assess whether prediction performance could be improved by performing a data selection.The results obtained show good forecasting performance and the proposed document provides a useful approach to understanding the overall phenomenon of technical debt for practical purposes.

Artificial Intelligence Model for Software Reusability Prediction System

The most significant invention made in recent years to serve various applications is software.Developing a faultless software system requires the soft-ware system design to be resilient.To make the software design more efficient,it is essential to assess the reusability of the components used.This paper proposes a software reusability prediction model named Flexible Random Fit(FRF)based on aging resilience for a Service Net(SN)software system.The reusability predic-tion model is developed based on a multilevel optimization technique based on software characteristics such as cohesion,coupling,and complexity.Metrics are obtained from the SN software system,which is then subjected to min-max nor-malization to avoid any saturation during the learning process.The feature extrac-tion process is made more feasible by enriching the data quality via outlier detection.The reusability of the classes is estimated based on a tool called Soft Audit.Software reusability can be predicted more effectively based on the pro-posed FRF-ANN(Flexible Random Fit-Artificial Neural Network)algorithm.Performance evaluation shows that the proposed algorithm outperforms all the other techniques,thus ensuring the optimization of software reusability based on aging resilient.The model is then tested using constraint-based testing techni-ques to make sure that it is perfect at optimizing and making predictions.

Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System

The software engineering field has long focused on creating high-quality software despite limited resources.Detecting defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the modules.This approach can enhance the quality of the final product while lowering development costs.Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team.This process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and maintenance.This study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high accuracy.In the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty modules.The second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and Stacking.Finally,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning techniques.The experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse datasets.Five NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and CM1.According to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

A Hybrid Model for Improving Software Cost Estimation in Global Software Development

Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.