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Path-oriented test cases generation based adaptive genetic algorithm

Xiaoan Bao, Zijian Xiong, Na Zhang, Junyan Qian, Biao Wu, Wei Zhang, Francesco Pappalardo
2017 PLoS ONE  
The automatic generation of test cases oriented paths in an effective manner is a challenging problem for structural testing of software.  ...  The experimental results confirm that the proposed method is efficient in generating test cases for path coverage.  ...  The challenge of test cases generation using GAs can be converted to an optimization problem of generating test data covering the target path, and the design of fitness function is the key to solve it.  ... 
doi:10.1371/journal.pone.0187471 pmid:29136028 pmcid:PMC5685491 fatcat:x7fuwe4q7rbc5ni5xwmlv3kl2y

An Adaptive PSO-based Approach for Data Flow Coverage of a Program

Sapna Varshney, Dr. Monica Mehrotra, Charu Saini
2017 International Journal of Engineering Research and  
There has been an extensive application of meta-heuristic search algorithms to generate software test data for branch coverage and path coverage test adequacy criteria.  ...  Genetic algorithm and its variants have been the choice of researchers for automated test data generation.  ...  An elitist GA-based test data generator guided by the same fitness function and random test data generator are also implemented for comparison to evaluate the efficiency and effectiveness of the proposed  ... 
doi:10.17577/ijertv6is020143 fatcat:cd6y2pzqfnh7bnsx5x5fouy2yu

Automated Test Data Generation Based on a Genetic Algorithm with Maximum Code Coverage and Population Diversity

Tatiana Avdeenko, Konstantin Serdyukov
2021 Applied Sciences  
The first term of the fitness function is responsible for the complexity of the code statements executed on the path generated by the current individual test case (current set of statements).  ...  Optimal relation between the two terms of fitness function was obtained for two very different programs under testing.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app11104673 fatcat:6oorlrerwjavplirny3pe3o3qi

A Hybrid Particle Swarm Optimization and Differential Evolution based Test Data Generation Algorithm for Data-Flow Coverage using Neighbourhood Search Strategy

Sapna Varshney, Monica Mehrotra
2018 Informatica (Ljubljana, Tiskana izd.)  
Over several experiments on a set of benchmark programs, it is shown that the hybrid algorithm performed significantly better than DE, PSO, GA and random search in data-flow test data generation with respect  ...  a program with a neighbourhood search strategy to improve the search capability of the hybrid algorithm. The fitness function is based on the concepts of dominance relations and branch distance.  ...  Path-oriented test data generator [5] uses control flow information to identify a set of independent paths to generate test data.  ... 
doi:10.31449/inf.v42i3.1497 fatcat:m73sntbla5dfhpyqlp6nyy4zhy

Search-based testing of service level agreements

Massimiliano Di Penta, Gerardo Canfora, Gianpiero Esposito, Valentina Mazza, Marcello Bruno
2007 Proceedings of the 9th annual conference on Genetic and evolutionary computation - GECCO '07  
The diffusion of service oriented architectures introduces the need for novel testing approaches. On the one side, testing must be able to identify failures in the functionality provided by service.  ...  This paper proposes the use of Genetic Algorithms to generate inputs and configurations for service-oriented systems that cause SLA violations.  ...  To let the GA generate testing data that causes SLA violations, the fitness must be a function of how far an individual is from QoS constraint violation.  ... 
doi:10.1145/1276958.1277174 dblp:conf/gecco/PentaCEMB07 fatcat:4jdbtggudngnzoevbf3bl2oiwi

A GA based Software Test Data to Generator Suitable Test Cases

2015 International Journal of Electronics, Mechanical and Mechatronics Engineering  
In presented test input data generation application, the solution sought by genetic algorithm is a set of test data that causes execution of all possible paths of a given program under test.  ...  In presented test input data generation application, the solution sought by genetic algorithm is a set of test data that causes execution of all possible paths of a given program under test.  ...  There are two fundamental strategies for improving GA based test data generation to test software. The first one is direct improvement of genetic algorithm components.  ... 
doi:10.17932/iau.ijemme.m.21460604.2015.5/4.1035-1042 fatcat:6ztmcwc5x5erzla4dmb4kvnqom

An evolutionary generation method of test data for multiple paths based on coverage balance

Shuping Fan, Nianmin Yao, Li Wan, Baoying Ma, Yan Zhang
2021 IEEE Access  
Up to now, GA is not only used for target-oriented software test data generation [15] but also object-oriented test data generation [16] . It can also be applied to embedded systems [17] .  ...  However, most of the fitness functions in the methods above are designed for one single target path, which will cause test data that only traverse one path is generated by GA for running one time.  ...  That is, it is verified that the proposed method can improve the efficiency of test data generation for multi-target paths. E.  ... 
doi:10.1109/access.2021.3089196 fatcat:uia7uzgnhjgffhm6voagxro3mm

EVOTLBO: A TLBO based Method for Automatic Test Data Generation in EvoSuite

Mohammad Mehdi, S. Parsa, S. Ehsan, Reza Akbari, S. Mohammad
2017 International Journal of Advanced Computer Science and Applications  
In this paper TLBO, a swarm intelligence technique, is proposed for automatic test data generation as well as for evaluation of test results.  ...  Search-Based Software Testing (SBST), specifically genetic algorithm, is the most popular technique in automated testing for achieving appropriate degree of software quality.  ...  Our approach for automatically generating test input data is a search based evolutionary algorithm, guided by a fitness function. B.  ... 
doi:10.14569/ijacsa.2017.080627 fatcat:fq6pkm643jcgrfbshr4lzt4toi

ADF-GA: Data Flow Criterion Based Test Case Generation for Ethereum Smart Contracts [article]

Pengcheng Zhang, Jianan Yu, Shunhui Ji
2020 arXiv   pre-print
A data flow oriented test case generation approach for dynamic testing of smart contract programs is still missing.  ...  To address this problem, this paper proposes a novel test case generation approach, called ADF-GA (All-uses Data Flow criterion based test case generation using Genetic Algorithm), for Solidity based Ethereum  ...  [21] applied the GA to the data flow test of object-oriented programs and proved the validity of test case generation methods based on data flow criterion in practice.  ... 
arXiv:2003.00257v1 fatcat:23frvs2oi5a2pd63bcrzmbsf64

Research on use of Nature Inspired Algorithms in Software Testing

The number of efforts required in software testing may be reduced if test data is generated automatically, without trade off the quality of the developed software.  ...  In literature, various nature inspired algorithms are used for the optimization of the software testing process.  ...  [3] used a search-based approach GA with the help of EVOSUITE tool for finding out test cases for data flow testing.  ... 
doi:10.35940/ijitee.k2573.0981119 fatcat:s5fikyux4fglvihtr44l67lbty

Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach

Madhumita Panda, Sujata Dash, Anand Nayyar, Muhammad Bilal, Raja Majid Mehmood
2020 IEEE Access  
Here the total path weight is the fitness function for each feasible path [24] .  ...  In many cases the FA and DE generated zero data for path 2 and 3 whereas the proposed hybrid FA-DE algorithm provides uniform number of test data for every path.  ... 
doi:10.1109/access.2020.3026911 fatcat:vr44rftfzbc4rc75zqo4sjyf5y

Prioritizing JUnit Test Cases without Coverage Information: An Optimization Heuristics Based Approach

R. Mukherjee, K.S.Patnaik
2019 IEEE Access  
TCP techniques for object oriented programs need attention and in our study, we explored prioritization of JUnit test cases.  ...  We examined the usage of Multi-objective GA by building another new fitness metric which aims to maximize the number of inheritance edges covered by a test case.  ...  TCP techniques for GUI/Web based Applications has the edge of using user session data as test data [23] .  ... 
doi:10.1109/access.2019.2922387 fatcat:6eaih5km3jfiljembne6qggsem

Basis Path Testing Using SGA & HGA with ExLB Fitness Function

Deepak Garg, Pallvi Garg
2015 Procedia Computer Science  
all these approaches in terms of test data generation under basis path coverage criteria.  ...  In this paper a new fitness function has been proposed named as Extended Level Branch (ExLB) Fitness function for basis path testing using simple genetic algorithm (SGA) and hybrid genetic algorithm (hill  ...  into a path oriented test data generation problem6.  ... 
doi:10.1016/j.procs.2015.10.044 fatcat:2vjgzflfqrd3ng7ier3lhj63ma

AVISAR – An Automated Framework for Test Case Selection & Prioritization using GA for OOS

In this paper we have presented an automated unified approach called AVISAR for the testing of the Object-oriented Systems (OOS) by Test Case Prioritization (TCP) & their selection using Genetic Algorithm  ...  Thus it can be used for reducing the efforts of the users for testing by efficient selection of effective test cases  ...  The use of the GA-based rolling test approach tackles the control flow accessibility problem in Object Oriented Software's flow path tests and helps the effective generation of Object Oriented test data  ... 
doi:10.35940/ijitee.f4570.049620 fatcat:sehmhlox3ngj7iy3i22xmi3cji

Search-Based Software Test Data Generation Using Evolutionary Computation

P Maragathavalli
2011 International Journal of Computer Science & Information Technology (IJCSIT)  
The fitness function is tailored to find test data for the type of test that is being undertaken.  ...  One area where Search-Based Software Engineering has seen much application is test data generation. Evolutionary testing designates the use of metaheuristic search methods for test case generation.  ...  Comparison of genetic algorithm with random testing is given below: The results show that for the same test data random testing requires 9 times more timing than GA.  ... 
doi:10.5121/ijcsit.2011.3115 fatcat:cwoqrugbxfgk3bduhsvinsy23u
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