Using Organizational Evolutionary Particle Swarm Techniques to Generate Test Cases for Combinatorial Testing

2011 ◽  
Author(s):  
Xiaoying Pan ◽  
Hao Chen
Keyword(s):  
Particle Swarm ◽  
Test Cases ◽  
Combinatorial Testing

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.   

scholarly journals Procurement Interaction Minimize Test Arrangement Formation of Software Testing Using Cuckoo Search Methods

2018 ◽  
Vol 7 (4.6) ◽  
pp. 302
Author(s):  
Dr. Anandam Velagandula ◽  
P. Buddha Reddy ◽  
N. Hanuman Reddy ◽  
G. Srikanth Reddy ◽  
Ch Anil
Keyword(s):  
Particle Swarm Optimization ◽  
Service Oriented Architecture ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Harmony Search ◽  
Interaction Strength ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Test Cases ◽  
Swarm Optimization

As of late number of meta based heuristic algorithms are suggested to fill in as the premise of test era technique (where shows the interaction strength) embracing  with Simulated Annealing (SA), Ant Colony Optimization (ACO), Cuckoo Search (CS), Genetic Algorithms (GA), Harmony Search (HS) and Particle Swarm Optimization (PSO). Albeit helpful methodologies are requiring particular area learning so as to permit successful tuning before great quality arrangements can be gotten. The multi-target molecule swarm optimization, and multithreading is utilized to overwhelm the compound judgement criteria for an ideal arrangement. The procedure and its related algorithms are assessed broadly utilizing diverse benchmarks and examinations. In our proposed technique test cases are advanced by utilizing Particle Swarm Optimization algorithm (PSO). At that point the streamlined test cases are organized by utilizing to enhanced Cuckoo Search algorithm (ECSA). As the quantity of inserted systems increments quickly, there has been developing interest for the utilization of Service Oriented Architecture (SOA) for some requests. At last, the enhanced outcome will be assessed by programming quality measures.

scholarly journals CTJ: Input-Output Based Relation Combinatorial Testing Strategy Using Jaya Algorithm

2020 ◽  
Vol 17 (3(Suppl.)) ◽  
pp. 1002
Author(s):  
Mohammed Issam Younis ◽  
Abdul Rahman A. Alsewari ◽  
Ng Yeong Khang ◽  
Kamal Z. Zamli
Keyword(s):  
Test List ◽  
Interaction Strength ◽  
Time Range ◽  
Test Case ◽  
Optimum Number ◽  
Test Cases ◽  
Jaya Algorithm ◽  
Combinatorial Testing ◽  
Input Output ◽  
Development Life Cycle

Software testing is a vital part of the software development life cycle. In many cases, the system under test has more than one input making the testing efforts for every exhaustive combination impossible (i.e. the time of execution of the test case can be outrageously long). Combinatorial testing offers an alternative to exhaustive testing via considering the interaction of input values for every t-way combination between parameters. Combinatorial testing can be divided into three types which are uniform strength interaction, variable strength interaction and input-output based relation (IOR). IOR combinatorial testing only tests for the important combinations selected by the tester. Most of the researches in combinatorial testing applied the uniform and the variable interaction strength, however, there is still a lack of work addressing IOR. In this paper, a Jaya algorithm is proposed as an optimization algorithm engine to construct a test list based on IOR in the proposed combinatorial test list generator strategy into a tool called CTJ. The result of applying the Jaya algorithm in input-output based combinatorial testing is acceptable since it produces a nearly optimum number of test cases in a satisfactory time range.

Software test data generation technology based on polymorphic particle swarm evolutionary algorithm

2021 ◽  
pp. 1-13
Author(s):  
Wenning Zhang ◽  
Qinglei Zhou
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Test Case Generation ◽  
Particle Swarm Algorithm ◽  
Test Case ◽  
Test Cases ◽  
Data Generation ◽  
Learning Factor ◽  
Swarm Algorithm

Combinatorial testing is a statute-based software testing method that aims to select a small number of valid test cases from a large combinatorial space of software under test to generate a set of test cases with high coverage and strong error debunking ability. However, combinatorial test case generation is an NP-hard problem that requires solving the combinatorial problem in polynomial time, so a meta-heuristic search algorithm is needed to solve the problem. Compared with other meta-heuristic search algorithms, the particle swarm algorithm is more competitive in terms of coverage table generation scale and execution time. In this paper, we systematically review and summarize the existing research results on generating combinatorial test case sets using particle swarm algorithm, and propose a combinatorial test case generation method that can handle arbitrary coverage strengths by combining the improved one-test-at-a-time strategy and the adaptive particle swarm algorithm for the variable strength combinatorial test problem and the parameter selection problem of the particle swarm algorithm. To address the parameter configuration problem of the particle swarm algorithm, the four parameters of inertia weight, learning factor, population size and iteration number are reasonably set, which makes the particle swarm algorithm more suitable for the generation of coverage tables. For the inertia weights.

Procurement Interaction Minimize Test Arrangement Formation of Software Testing Using Cuckoo Search Methods

2018 ◽  
Vol 7 (4.6) ◽  
pp. 302
Author(s):  
Dr. Anandam Velagandula ◽  
P. Buddha Reddy ◽  
N. Hanuman Reddy ◽  
G. Srikanth Reddy ◽  
Ch Anil
Keyword(s):  
Particle Swarm Optimization ◽  
Service Oriented Architecture ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Harmony Search ◽  
Interaction Strength ◽  
Particle Swarm ◽  
Cuckoo Search ◽  
Test Cases ◽  
Swarm Optimization

As of late number of meta based heuristic algorithms are suggested to fill in as the premise of test era technique (where shows the interaction strength) embracing  with Simulated Annealing (SA), Ant Colony Optimization (ACO), Cuckoo Search (CS), Genetic Algorithms (GA), Harmony Search (HS) and Particle Swarm Optimization (PSO). Albeit helpful methodologies are requiring particular area learning so as to permit successful tuning before great quality arrangements can be gotten. The multi-target molecule swarm optimization, and multithreading is utilized to overwhelm the compound judgement criteria for an ideal arrangement. The procedure and its related algorithms are assessed broadly utilizing diverse benchmarks and examinations. In our proposed technique test cases are advanced by utilizing Particle Swarm Optimization algorithm (PSO). At that point the streamlined test cases are organized by utilizing to enhanced Cuckoo Search algorithm (ECSA). As the quantity of inserted systems increments quickly, there has been developing interest for the utilization of Service Oriented Architecture (SOA) for some requests. At last, the enhanced outcome will be assessed by programming quality measures.  

COST-COGNIZANT COMBINATORIAL TEST CASE PRIORITIZATION

2011 ◽  
Vol 21 (06) ◽  
pp. 829-854 ◽  
Author(s):  
ZIYUAN WANG ◽  
LIN CHEN ◽  
BAOWEN XU ◽  
YAN HUANG
Keyword(s):  
Physical Meaning ◽  
Resource Constraints ◽  
Regression Testing ◽  
Experimental Results ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Combinatorial Testing

Combinatorial testing has been widely used in practice. People usually assume all test cases in combinatorial test suite will run completely. However, in many scenarios where combinatorial testing is needed, for example the regression testing, the entire combinatorial test suite is not run completely as a result of test resource constraints. To improve the efficiency of testing, combinatorial test case prioritization technique is required. For the scenario of regression testing, this paper proposes a new cost-cognizant combinatorial test case prioritization technique, which takes both combination weights and test costs into account. Here we propose a series of metrics with physical meaning, which assess the combinatorial coverage efficiency of test suite, to guide the prioritization of combinatorial test cases. And two heuristic test case prioritization algorithms, which are based on total and additional techniques respectively, are utilized in our technique. Simulation experimental results illustrate some properties and advantages of proposed technique.

A Method of Testing Generation Based on Prioritized Pair

2012 ◽  
Vol 241-244 ◽  
pp. 2696-2700
Author(s):  
Yu Wang ◽  
Hao Wu ◽  
Zhen Yu Sheng
Keyword(s):  
Genetic Algorithm ◽  
Detection Rate ◽  
Test Methods ◽  
Limited Resources ◽  
Test Case ◽  
Test Cases ◽  
Test Coverage ◽  
Combinatorial Testing ◽  
Critical Test ◽  
Generate Pair

Combinatorial testing has lots of test cases, but software testers hope to get the best test coverage with the smallest test case suite. For the scale of produced test cases is so large that researchers have considered the implementation of the critical test cases. This article researches the classic combinatorial test methods and proposes methods to generate pair-wise testing cases with a priority. Firstly, we design formulas to compute the weights of priorities. Secondly, we adopt a greed algorithm to solve the combinatorial testing problems. Furthermore, we integrate the greed strategy into a genetic algorithm to improve the efficiency. It improves the testing efficiency while securing the detection rate of defects under limited resources.

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