Personalized Learning Path Recommendations for Software Testing Courses Based on Knowledge Graphs

Software testing courses are characterized by strong practicality,comprehensiveness,and diversity.Due to the differences among students and the needs to design personalized solutions for their specific requirements,the design of the existing software testing courses fails to meet the demands for personalized learning.Knowledge graphs,with their rich semantics and good visualization effects,have a wide range of applications in the field of education.In response to the current problem of software testing courses which fails to meet the needs for personalized learning,this paper offers a learning path recommendation based on knowledge graphs to provide personalized learning paths for students.

Empirical Measurement of the Software Testing and Reliability

The meanings of parameters of software reliabi- lity models are investigated in terms of the process of the software testing and in terms of other measurements of software. Based on the investigation, the empirical estimation of the parameters is addressed. On one hand, these empirical estimates are also measurements of the software, which can be used to control and to optimize the process of the software development. On the other hand, by treating these empirical estimates as Bayes priors, software reliability models are extended such that the engineers’ experience can be integrated into and hence to improve the models.

Modified Harris Hawks Optimization Based Test Case Prioritization for Software Testing

Generally,software testing is considered as a proficient technique to achieve improvement in quality and reliability of the software.But,the quality of test cases has a considerable influence on fault revealing capability of software testing activity.Test Case Prioritization(TCP)remains a challenging issue since prioritizing test cases is unsatisfactory in terms of Average Percentage of Faults Detected(APFD)and time spent upon execution results.TCP ismainly intended to design a collection of test cases that can accomplish early optimization using preferred characteristics.The studies conducted earlier focused on prioritizing the available test cases in accelerating fault detection rate during software testing.In this aspect,the current study designs aModified Harris Hawks Optimization based TCP(MHHO-TCP)technique for software testing.The aim of the proposed MHHO-TCP technique is to maximize APFD and minimize the overall execution time.In addition,MHHO algorithm is designed to boost the exploration and exploitation abilities of conventional HHO algorithm.In order to validate the enhanced efficiency of MHHO-TCP technique,a wide range of simulations was conducted on different benchmark programs and the results were examined under several aspects.The experimental outcomes highlight the improved efficiency of MHHO-TCP technique over recent approaches under different measures.

Research and Practice of Hybrid Teaching for Software Testing based on Interactive Learning

To address the problems of insufficient number of personalized exercises and cases and teachers’lack of grasp of students’weak knowledge points in the current software testing online courses,we study the strategy of establishing and updating intelligent exercise sets and case libraries and analyze the answers and dig out the weak points of knowledge through group intelligence reasoning and interactive machine learning methods.This will help teachers to make uniform and targeted explanations,reduce manual judgment,and achieve intelligent teaching quality reform,and implement the educational concepts of“keeping up with the times”and“teaching according to students’abilities”.

Optimal Budget Spending for Software Testing under the Condition of Nonlinear Constraint

Software testing is a very important phase of the software development process. It is a very difficult job for a software manager to allocate optimally the financial budget to a software project during testing. In this paper the problem of optimal allocation of the software testing cost is studied. There exist several models focused on the development of software costs measuring the number of software errors remaining in the software during testing. The purpose of this paper is to use these models to formulate the optimization problems of resource allocation: Minimization of the total number of software errors remaining in the system. On the assumption that a software project consists of some independent modules, the presented approach extends previous work by defining new goal functions and extending the primary assumption and precondition.

Managing Software Testing Technical Debt Using Evolutionary Algorithms

Technical debt(TD)happens when project teams carry out technical decisions in favor of a short-term goal(s)in their projects,whether deliberately or unknowingly.TD must be properly managed to guarantee that its negative implications do not outweigh its advantages.A lot of research has been conducted to show that TD has evolved into a common problem with considerable financial burden.Test technical debt is the technical debt aspect of testing(or test debt).Test debt is a relatively new concept that has piqued the curiosity of the software industry in recent years.In this article,we assume that the organization selects the testing artifacts at the start of every sprint.Implementing the latest features in consideration of expected business value and repaying technical debt are among candidate tasks in terms of the testing process(test cases increments).To gain the maximum benefit for the organization in terms of software testing optimization,there is a need to select the artifacts(i.e.,test cases)with maximum feature coverage within the available resources.The management of testing optimization for large projects is complicated and can also be treated as a multi-objective problem that entails a trade-off between the agile software’s short-term and long-term value.In this article,we implement a multi-objective indicatorbased evolutionary algorithm(IBEA)for fixing such optimization issues.The capability of the algorithm is evidenced by adding it to a real case study of a university registration process.

Optimal Reordering Trace Files for Improving Software Testing Suitcase

An invariant can be described as an essential relationship between program variables.The invariants are very useful in software checking and verification.The tools that are used to detect invariants are invariant detectors.There are two types of invariant detectors:dynamic invariant detectors and static invariant detectors.Daikon software is an available computer program that implements a special case of a dynamic invariant detection algorithm.Daikon proposes a dynamic invariant detection algorithm based on several runs of the tested program;then,it gathers the values of its variables,and finally,it detects relationships between the variables based on a simple statistical analysis.This method has some drawbacks.One of its biggest drawbacks is its overwhelming time order.It is observed that the runtime for the Daikon invariant detection tool is dependent on the ordering of traces in the trace file.A mechanism is proposed in order to reduce differences in adjacent trace files.It is done by applying some special techniques of mutation/crossover in genetic algorithm(GA).An experiment is run to assess the benefits of this approach.Experimental findings reveal that the runtime of the proposed dynamic invariant detection algorithm is superior to the main approach with respect to these improvements.

Software Testing Method Based on Model Comparison

A model comparison based software testing method （MCST） is proposed. In this method, the requirements aria programs or software under test are transformed into the ones in the same form, and described by the same model describe language （MDL）. Then, the requirements are transformed into a specification model and the programs into an implementation model. Thus, the elements and structures of the two models are compared, and the differences between them are obtained. Based on the diffrences, a test suite is generated. Different MDLs can be chosen for the software under test. The usages of two classical MDLs in MCST, the equivalence classes model and the extended finite state machine （EFSM） model, are described with example applications. The results show that the test suites generated by MCST are more efficient and smaller than some other testing methods, such as the pathcoverage testing method, the object state diagram testing method, etc.

Software Testing Applications Based on a Virtual Reality System

Software testing is an important part of software engineering and has been more and more popular as the rapid growth of the software products market. Good skills of communication with clients and programmers play a significant role for a tester during the test process. This paper presents some important and basic software testing applications （such as static testing, dynamic testing, black-box testing, white-box testing and their combinations） based on a virtual reality system, named as rocket digital simulation system （RDSS）. Different testing methods are exercised during the software developing lifecycle and finally achieving significant quality improvement.

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.

Search-Based Software Test Data Generation for Path Coverage Based on a Feedback-Directed Mechanism

Automatically generating test cases by evolutionary algorithms to satisfy the path coverage criterion has attracted much research attention in software testing.In the context of generating test cases to cover many target paths,the efficiency of existing methods needs to be further improved when infeasible or difficult paths exist in the program under test.This is because a significant amount of the search budget(i.e.,time allocated for the search to run)is consumed when computing fitness evaluations of individuals on infeasible or difficult paths.In this work,we present a feedback-directed mechanism that temporarily removes groups of paths from the target paths when no improvement is observed for these paths in subsequent generations.To fulfill this task,our strategy first organizes paths into groups.Then,in each generation,the objective scores of each individual for all paths in each group are summed up.For each group,the lowest value of the summed up objective scores among all individuals is assigned as the best aggregated score for a group.A group is removed when no improvement is observed in its best aggregated score over the last two generations.The experimental results show that the proposed approach can significantly improve path coverage rates for programs under test with infeasible or difficult paths in case of a limited search budget.In particular,the feedback-directed mechanism reduces wasting the search budget on infeasible paths or on difficult target paths that require many fitness evaluations before getting an improvement.

Measuring Reliability of A Web Portal Based on Testing Profile

Conventionally,the reliability of a web portal is validated with generalized conventional methods,but they fail to provide the desired results.Therefore,we need to include other quality factors that affect reliability such as usability for improving the reliability in addition to the conventional reliability testing.Actually,the primary objectives of web portals are to provide interactive integration of multiple functions confirming diverse requirements in an efficient way.In this paper,we employ testing profiles tomeasure the reliability through software operational profile,input space profile and usability profile along with qualitative measures of reliability and usability.Moreover,the case study used for verification is based on aweb application that facilitates information and knowledge sharing among its online members.The proposed scheme is compared with the conventional reliability improvement method in terms of failure detection and reliability.The final results unveil that the computation of reliability by using the traditional method(utilizing failure points with the assistance of Mean Time Between Failures(MTBF)and Mean Time To Failure(MTTF)becomes ineffective under certain situations.Under such situations,the proposed scheme helps to compute the reliability in an effective way.Moreover,the outcomes of the study provide insight recommendations about the testing and measurement of reliability for Web based software or applications.

On modeling approach for embedded real-time software simulation testing

Modeling technology has been introduced into software testing field. However, how to carry through the testing modeling effectively is still a difficulty. Based on combination of simulation modeling technology and embedded real-time software testing method, the process of simulation testing modeling is studied first. And then, the supporting environment of simulation testing modeling is put forward. Furthermore, an approach of embedded real-time software simulation testing modeling including modeling of cross-linked equipments of system under testing （SUT）, test case, testing scheduling, and testing system service is brought forward. Finally, the formalized description and execution system of testing models are given, with which we can realize real-time, closed loop, mad automated system testing for embedded real-time software.

Optimal stopping of multi-project software testing in the context of software cybernetics

Software cybernetics explores the interplay between control theory/engineering and software theory/engineering. The controlled Markov chains (CMC) approach to software testing follows the idea of software cybernetics and treats software testing as a control problem. The software under test serves as a controlled object and the software testing strategy serves as the corresponding controller. The software under test and the software testing strategy make up a closed-loop feedback control system, and the theory of controlled Markov chains can be used to design and optimize software testing strategies in accordance with testing/reliability goals given a priori. In this paper we apply the CMC approach to the optimal stopping problem of multi-project software testing. The problem under consideration assumes that a single stopping action can stop testing of all the software systems under test simultaneously. The theoretical results presented in this paper describe how to test multiple software systems and when to stop testing in an optimal manner. An illustrative example is used to explain the theoretical results. The study of this paper further justifies the effectiveness of the CMC approach to software testing in particular and the idea of software cybernetics in general.

Analysis of Software Test Item Generation -- Comparison Between High Skilled and Low Skilled Engineers

Recent software system contain many functions to provide various services.According to this tendency, it is difficult to ensure software quality and to eliminate crucialfaults by conventional software testing methods. So taking the effect of test engineer''s skill ontest item generation into consideration, we propose a new test item generation method, whichsupports the generation of test items for illegal behavior of the system. The proposed method cangenerate test items based on use-case analysis, deviation analysis for legal behavior, and faultstree analysis for system fault situations. Prom the results of the experimental applications of ourmethod, we confirmed that test items for illegal behavior of a system were effectively generated,and also the proposed method could effectively assist test item generation by an engineer withlow-level skill.

Reduction of Faults in Software Testing by Fault Domination

Although mutation testing is one of the practical ways of enhancing test effectiveness in software testing, it could be sometimes infeasible in practical work for a large scale software so that the mutation testing becomes time-consuming and even in prohibited time. Therefore, the number of faults assumed to exist in the software under test should be reduced so as to be able to confine the time complexity of test within a reasonable period of time. This paper utilizes the concept of fault dominance and equivalence, which has long been employed in hardware testing, for revealing a novel way of reducing the number of faults assumed to hide in software systems. Once the number of faults assumed in software is decreased sharply, the effectiveness of mutation testing will be greatly enhanced and become a feasible way of software testing. Examples and experimental results are presented to illustrate the effectiveness and the helpfulness of the technology proposed in the paper.

Method of militarysoftware securityand vulnerability testing based on process mutation

To solve the problems caused by military software security issues,this paper firstly introduces a new software fault injection technique,namely main static fault injection method:program mutation.And then source code for testing this algorithm is put forward.On this basis buffer overflow testing based on program mutation is conducted.Finally several military software source codes for buffer overflow testing are tested using deficiency tracking system(DTS)tool,Experimental results show the effectiveness of the proposed algorithm.

Implementation of Hybrid Particle Swarm Optimization for Optimized Regression Testing

Software test case optimization improves the efficiency of the software by proper structure and reduces the fault in the software.The existing research applies various optimization methods such as Genetic Algorithm,Crow Search Algorithm,Ant Colony Optimization,etc.,for test case optimization.The existing methods have limitations of lower efficiency in fault diagnosis,higher computa-tional time,and high memory requirement.The existing methods have lower effi-ciency in software test case optimization when the number of test cases is high.This research proposes the Tournament Winner Genetic Algorithm(TW-GA)method to improve the efficiency of software test case optimization.Hospital Information System(HIS)software was used to evaluate TW-GA model perfor-mance in test case optimization.The tournament Winner in the proposed method selects the instances with the best fitness values and increases the exploitation of the search to find the optimal solution.The TW-GA method has higher exploita-tion that helps to find the mutant and equivalent mutation that significantly increases fault diagnosis in the software.The TW-GA method discards the infor-mation with a lower fitness value that reduces the computational time and mem-ory requirement.The TW-GA method requires 5.47 s and the MOCSFO method requires 30 s for software test case optimization.

NHPP-based software reliability model considering testing effort and multivariate fault detection rate

In recent decades,many software reliability growth models（SRGMs） have been proposed for the engineers and testers in measuring the software reliability precisely.Most of them is established based on the non-homogeneous Poisson process（NHPP）,and it is proved that the prediction accuracy of such models could be improved by adding the describing of characterization of testing effort.However,some research work indicates that the fault detection rate（FDR） is another key factor affects final software quality.Most early NHPPbased models deal with the FDR as constant or piecewise function,which does not fit the different testing stages well.Thus,this paper first incorporates a multivariate function of FDR,which is bathtub-shaped,into the NHPP-based SRGMs considering testing effort in order to further improve performance.A new model framework is proposed,and a stepwise method is used to apply the framework with real data sets to find the optimal model.Experimental studies show that the obtained new model can provide better performance of fitting and prediction compared with other traditional SRGMs.

Fuzzing:Progress,Challenges,and Perspectives

As one of the most effective techniques for finding software vulnerabilities,fuzzing has become a hot topic in software security.It feeds potentially syntactically or semantically malformed test data to a target program to mine vulnerabilities and crash the system.In recent years,considerable efforts have been dedicated by researchers and practitioners towards improving fuzzing,so there aremore and more methods and forms,whichmake it difficult to have a comprehensive understanding of the technique.This paper conducts a thorough survey of fuzzing,focusing on its general process,classification,common application scenarios,and some state-of-the-art techniques that have been introduced to improve its performance.Finally,this paper puts forward key research challenges and proposes possible future research directions that may provide new insights for researchers.