scholarly journals Exploring Software Security Test Generation Techniques: Challenges and Opportunities

2021 ◽  
Vol 15 ◽  
pp. 107-121
Author(s):  
Mamdouh Alenezi ◽  
Mohammed Akour ◽  
Hamid Abdul Basit
Keyword(s):  
Test Generation ◽  
Software Security ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Current State ◽  
Development Life Cycle ◽  
Challenges And Opportunities ◽  
Software Development Life Cycle ◽  
Security Test

Ensuring the security of the software has raised concerns from the research community which triggered numerous approaches that tend to eliminate it. The process of ensuring the security of software includes the introduction of processes in the Software Development Life Cycle where one of them is testing after the software is developed. Manually testing software for security is a labor-intensive task. Therefore, it is required to automate the process of testing by generating test cases by automated techniques. In this paper, we review various software security test case generation approaches and techniques. We try to explore and classify the most eminent techniques for test case generation. The techniques are summarized and presented briefly to covers all researches work that has been done in the targeted classification. Moreover, this paper aims to depict the sound of security in the current state of the art of test case generation. The findings are summarized and discussed where the opportunities and challenges are revealed narratively. Although the paper intends to provide a comprehensive view of the research in test case generation, there was a noticeable lack in the test case generation from the security perspectives

Survey on Prioritizing Test Cases in Various Levels of the Software Development Life Cycle

2021 ◽  
Vol 12 (1) ◽  
pp. 1-28
Author(s):  
Gayatri Nayak ◽  
Mitrabinda Ray
Keyword(s):  
Life Cycle ◽  
Software Development ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Research Gaps ◽  
Prioritization Process ◽  
Development Life Cycle ◽  
Software Development Life Cycle ◽  
Conclusion Section

Test case prioritization is a technical method to reorder the execution of test cases to reduce regression testing costs. This paper has examined various existing techniques that are widely used and suggests improving test case prioritization process after finding many research gaps. These research gaps are collected after doing a thorough study on 206 papers after surfing 310 papers on test case generation and prioritization techniques. These papers are collected from different electronic databases such as IEEE Explore, Science Direct, ACM Library, Springer, Wiley, and Elsevier. The authors have targeted to make a statistical record to show research contribution on test case prioritization at three levels of software development life cycle. This survey shows that 20.87% of papers are contributing for TCP at the requirement phase, 38.83% of papers are contributing for TCP at the design phase, 40.29% of papers are contributing to TCP at the coding phase. The inference of this study cites many future recommendations for the current researchers in the conclusion section.

scholarly journals MODEL BASED TEST CASE PRIORITIZATION USING UML ACTIVITY DIAGRAM AND EVOLUTIONARY ALGORITHM

2014 ◽  
pp. 76-81
Author(s):  
PRATEEVA MAHALI ◽  
ARUP ABHINNA ACHARYA
Keyword(s):  
Critical Path ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Activity Diagram ◽  
Development Life Cycle ◽  
Software Development Life Cycle ◽  
Uml Activity Diagram ◽  
Independent Path ◽  
Time Required

With the exponential growth in size and complexity of softwares, the testing activity is no more limited to testing phase of SDLC (Software Development Life Cycle). Testing process has been made iterative and incremental in Object Oriented development scenario. This leads to increase in effort and time required for testing as well as explosion in test case. But when it comes to regression testing, it has the additional issue of test case retesting which further increasing the effort and time. So a suitable prioritization technique should be used to address these issues. In this paper we had given a proposal which is based on prioritization of test cases using GA (Genetic Algorithm). This process is found to be very effective during regression testing. In this paper we found an optimized independent path having maximum critical path value, which further leads to prioritization of test cases. The three component of regression testing i.e effort, time, cost will be gradually reduce by using this approach.

Survey on Testing of BPEL Processes

2014 ◽  
Vol 13 (7) ◽  
pp. 4633-4637
Author(s):  
Gurpreet Kaur ◽  
Mrs. Gaganpreet Kaur
Keyword(s):  
Life Cycle ◽  
Software Testing ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Development Life Cycle ◽  
Testing Techniques ◽  
Critical Task ◽  
The Cost ◽  
Bpel Processes

Software testing is very important phase in any development Life Cycle. The test Case generation is critical task in any type of testing. The automation of test case generation is necessary to reduce cost and effort incurred in the testing of large software. Testing of the BPEL processes is new area of research and the automation of the test cases is necessary in order to find bugs in the processes and reduce the cost of the  testing business  processes .This paper focuses on the survey of the testing techniques used to test the BPEL processes.

scholarly journals Swarm Intelligence Techniques and Genetic Algorithms for Test Case Prioritization

2020 ◽  
Vol 9 (4) ◽  
pp. 465-469
Keyword(s):  
Genetic Algorithms ◽  
Swarm Intelligence ◽  
Regression Testing ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Random Testing ◽  
Development Life Cycle ◽  
Software Development Life Cycle ◽  
Time Required

Regression testing is a technique which is carried out to ascertain that the changes that were done in the source code have not negatively damped its performance. Hence, it is a crucial and an expensive step of the software development life cycle. It re-establishes confidence in correctness of the software after changes were made to it. A test suite is used to test the software, but often it becomes time consuming to re-execute each test case every time regression testing is done. Therefore, it becomes essential to decrease the number of the test cases by prioritizing them based on some criterion. This ensures maximum detection of faults in least amount of time. In this paper, author has compared swarm intelligence techniques with genetic algorithms for such a test suite prioritization. In particular, by taking a sample GCD program Ant Colony Optimization (ACO) has been compared with Genetic Algorithms (GA) for the purpose of test suite minimization. Unit of comparison has been execution time required for prioritization of test cases. Further, experimental results have been compared with time taken by both with random testing.

A Systematic Review and Catalog of Security Metric during the Secure Software Development Life Cycle

2020 ◽  
Vol 13 ◽  
Author(s):  
Sampada G.C ◽  
Tende Ivo Sake ◽  
Amrita
Keyword(s):  
Systematic Review ◽  
Life Cycle ◽  
Software Development ◽  
Software Security ◽  
Security Assessment ◽  
Security Metrics ◽  
Development Life Cycle ◽  
Development Processes ◽  
Secure Software Development ◽  
Software Development Life Cycle

Background: With the advancement in the field of software development, software poses threats and risks to customers’ data and privacy. Most of these threats are persistent because security is mostly considered as a feature or a non-functional requirement, not taken into account during the software development life cycle (SDLC). Introduction: In order to evaluate the security performance of a software system, it is necessary to integrate the security metrics during the SDLC. The appropriate security metrics adopted for each phase of SDLC aids in defining the security goals and objectives of the software as well as quantify the security in the software. Methods: This paper presents systematic review and catalog of security metrics that can be adopted during the distinguishable phases of SDLC, security metrics for vulnerability and risk assessment reported in the literature for secure development of software. The practices of these metrics enable software security experts to improve the security characteristics of the software being developed. The critical analysis of security metrics of each phase and their comparison are also discussed. Results: Security metrics obtained during the development processes help to improve the confidentiality, integrity, and availability of software. Hence, it is imperative to consider security during the development of the software, which can be done with the use of software security metrics. Conclusion: This paper reviews the various security metrics that are meditated in the copious phases during the progression of the SDLC in order to provide researchers and practitioners with substantial knowledge for adaptation and further security assessment.

scholarly journals Performance of Enhanced Multiple-Searching Genetic Algorithm for Test Case Generation in Software Testing

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1779
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang ◽  
Chi-En Tsai
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Random Search ◽  
Automatic Generation ◽  
Superior Performance ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Network Systems ◽  
Routing Problem

Test case generation is an important process in software testing. However, manual generation of test cases is a time-consuming process. Automation can considerably reduce the time required to create adequate test cases for software testing. Genetic algorithms (GAs) are considered to be effective in this regard. The multiple-searching genetic algorithm (MSGA) uses a modified version of the GA to solve the multicast routing problem in network systems. MSGA can be improved to make it suitable for generating test cases. In this paper, a new algorithm called the enhanced multiple-searching genetic algorithm (EMSGA), which involves a few additional processes for selecting the best chromosomes in the GA process, is proposed. The performance of EMSGA was evaluated through comparison with seven different search-based techniques, including random search. All algorithms were implemented in EvoSuite, which is a tool for automatic generation of test cases. The experimental results showed that EMSGA increased the efficiency of testing when compared with conventional algorithms and could detect more faults. Because of its superior performance compared with that of existing algorithms, EMSGA can enable seamless automation of software testing, thereby facilitating the development of different software packages.

Proposal of Iterative Genetic Algorithm for Test Suite Generation

2021 ◽  
Vol 12 (1) ◽  
pp. 111-130
Author(s):  
Ankita Bansal ◽  
Abha Jain ◽  
Abhijeet Anand ◽  
Swatantra Annk
Keyword(s):  
Genetic Algorithm ◽  
Execution Time ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Computation Complexity ◽  
Test Suite Generation ◽  
Adaptive Nature ◽  
Efficiency And Reliability

Huge and reputed software industries are expected to deliver quality products. However, industry suffers from a loss of approximately $500 billion due to shoddy software quality. The quality of the product in terms of its accuracy, efficiency, and reliability can be revamped through testing by focusing attention on testing the product through effective test case generation and prioritization. The authors have proposed a test-case generation technique based on iterative listener genetic algorithm that generates test cases automatically. The proposed technique uses its adaptive nature and solves the issues like redundant test cases, inefficient test coverage percentage, high execution time, and increased computation complexity by maintaining the diversity of the population which will decrease the redundancy in test cases. The performance of the technique is compared with four existing test-case generation algorithms in terms of computational complexity, execution time, coverage, and it is observed that the proposed technique outperformed.

Optimized Test Case Generation for Object Oriented Systems Using Weka Open Source Software

2021 ◽  
pp. 596-618
Author(s):  
Rajvir Singh ◽  
Anita Singhrova ◽  
Rajesh Bhatia
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Effective Means ◽  
Object Oriented ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Time Saving ◽  
Class Level ◽  
Fault Proneness

Detection of fault proneness classes helps software testers to generate effective class level test cases. In this article, a novel technique is presented for an optimized test case generation for ant-1.7 open source software. Class level object oriented (OO) metrics are considered as effective means to find fault proneness classes. The open source software ant-1.7 is considered for the evaluation of proposed techniques as a case study. The proposed mathematical model is the first of its kind generated using Weka open source software to select effective OO metrics. Effective and ineffective OO metrics are identified using feature selection techniques for generating test cases to cover fault proneness classes. In this methodology, only effective metrics are considered for assigning weights to test paths. The results indicate that the proposed methodology is effective and efficient as the average fault exposition potential of generated test cases is 90.16% and test cases execution time saving is 45.11%.

scholarly journals Test case generation for object-oriented imperative languages in CLP

2010 ◽  
Vol 10 (4-6) ◽  
pp. 659-674 ◽  
Author(s):  
MIGUEL GÓMEZ-ZAMALLOA ◽  
ELVIRA ALBERT ◽  
GERMÁN PUEBLA
Keyword(s):  
Symbolic Execution ◽  
Object Oriented ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Software Development Process ◽  
Java Programs ◽  
Promising Application ◽  
Account Due ◽  
Imperative Language

AbstractTesting is a vital part of the software development process. Test Case Generation (TCG) is the process of automatically generating a collection of test-cases which are applied to a system under test. White-box TCG is usually performed by means of symbolic execution, i.e., instead of executing the program on normal values (e.g., numbers), the program is executed on symbolic values representing arbitrary values. When dealing with an object-oriented (OO) imperative language, symbolic execution becomes challenging as, among other things, it must be able to backtrack, complex heap-allocated data structures should be created during the TCG process and features like inheritance, virtual invocations and exceptions have to be taken into account. Due to its inherent symbolic execution mechanism, we pursue in this paper that Constraint Logic Programming (CLP) has a promising application field in tcg. We will support our claim by developing a fully CLP-based framework to TCG of an OO imperative language, and by assessing it on a corresponding implementation on a set of challenging Java programs.

A Critical Review on Automated Test Case Generation for Conducting Combinatorial Testing Using Particle Swarm Optimization

2018 ◽  
Vol 7 (3.8) ◽  
pp. 22 ◽  
Author(s):  
Dr V. Chandra Prakash ◽  
Subhash Tatale ◽  
Vrushali Kondhalkar ◽  
Laxmi Bewoor
Keyword(s):  
Particle Swarm Optimization ◽  
Critical Review ◽  
Optimization Problem ◽  
Test Suite ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Combinatorial Testing ◽  
Swarm Optimization ◽  
Automated Test Case Generation

In software development life cycle, testing plays the significant role to verify requirement specification, analysis, design, coding and to estimate the reliability of software system. A test manager can write a set of test cases manually for the smaller software systems. However, for the extensive software system, normally the size of test suite is large, and the test suite is prone to an error committed like omissions of important test cases, duplication of some test cases and contradicting test cases etc. When test cases are generated automatically by a tool in an intelligent way, test errors can be eliminated. In addition, it is even possible to reduce the size of test suite and thereby to decrease the cost & time of software testing.It is a challenging job to reduce test suite size. When there are interacting inputs of Software under Test (SUT), combinatorial testing is highly essential to ensure higher reliability from 72 % to 91 % or even more than that. A meta-heuristic algorithm like Particle Swarm Optimization (PSO) solves optimization problem of automated combinatorial test case generation. Many authors have contributed in the field of combinatorial test case generation using PSO algorithms.We have reviewed some important research papers on automated test case generation for combinatorial testing using PSO. This paper provides a critical review of use of PSO and its variants for solving the classical optimization problem of automatic test case generation for conducting combinatorial testing.   

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