scholarly journals Using Artificial Bee Colony Algorithm for Test Data Generation and Path Testing Coverage

2018 ◽  
Vol 12 (7) ◽  
pp. 99
Author(s):  
Faten Hamad
Keyword(s):  
Software Testing ◽  
Test Data ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Structural Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Bee Colony ◽  
Development Lifecycle ◽  
Path Testing

Software testing is a significant stage in software development lifecycle. There are different sorts of' structural software testing methodologies that may be generally utilized and moved forward through enhancing the traverse of all of the conceivable code software paths. The interest for automating data testing is growing; however, manual testing strategies utilization would be expensive and costly. Heuristic measure is being applied to; detect how better the result might be (solution fitness); result development mechanism; and suitableness criteria with stop search mechanism depending on wither a result is found or not. Testing experience could be exploited for finding a solution to the optimization problem by utilizing Meta heuristic procedures. The presented approach might have been tested for five programs to demonstrate the distinctive tests issues. This paper proposes an automatic test data generation approach that use artificial bee colony algorithm for software structural testing, particularly, path testing. This is brought on moving the centralization of data generation testing, as opposed to the automation of the whole testing operation. It executes artificial bee colony algorithm by creating testing data for the criteria of path coverage testing, and then applying the strategy to a group of test programs. 

TEST DATA GENERATION FOR SOFTWARE TESTING BASED ON QUANTUM-INSPIRED GENETIC ALGORITHM

2013 ◽  
Vol 12 (01) ◽  
pp. 1350004 ◽  
Author(s):  
CHENGYING MAO ◽  
XINXIN YU
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Test Data ◽  
Optimal Solution ◽  
Search Space ◽  
Structural Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Quantum Bit

The quality of test data has an important impact on the effect of software testing, so test data generation has always been a key task for finding the potential faults in program code. In structural testing, the primary goal is to cover some kinds of structure elements with some specific inputs. Search-based test data generation provides a rational way to handle this difficult problem. In the past, some well-known meta-heuristic search algorithms have been successfully utilized to solve this issue. In this paper, we introduce a variant of genetic algorithm (GA), called quantum-inspired genetic algorithm (QIGA), to generate the test data with stronger coverage ability. In this new algorithm, the traditional binary bit is replaced by a quantum bit (Q-bit) to enlarge the search space so as to avoid falling into local optimal solution. On the other hand, some other strategies such as quantum rotation gate and catastrophe operation are also used to improve algorithm efficiency and quality of test data. In addition, experimental analysis on eight real-world programs is performed to validate the effectiveness of our method. The results show that QIGA-based method can generate test data with higher coverage in much smaller convergence generations than GA-based method. More importantly, our proposed method is more robust for algorithm parameter change.

Automatic Test Data Generation Using Bio-Inspired Algorithms

2018 ◽  
pp. 140-159 ◽  
Author(s):  
Madhumita Panda ◽  
Sujata Dash
Keyword(s):  
Test Data ◽  
Artificial Bee Colony Algorithm ◽  
Cuckoo Search ◽  
Metaheuristic Algorithms ◽  
Test Data Generation ◽  
Data Generation ◽  
Bee Colony ◽  
Path Coverage ◽  
Automatic Test Data Generation ◽  
Generate Test Data

This chapter presents an overview of some widely accepted bio-inspired metaheuristic algorithms which would be helpful in solving the problems of software testing. Testing is an integral part of the software development process. A sizable number of Nature based algorithms coming under the per- view of metaheuristics have been used by researchers to solve practical problems of different disciplines of engineering and computer science, and software engineering. Here an exhaustive review of metaheuristic algorithms which have been employed to optimize the solution of test data generation for past 20 -30 years is presented. In addition to this, authors have reviewed their own work has been developed particularly to generate test data for path coverage based testing using Cuckoo Search and Gravitational Search algorithms. Also, an extensive comparison with the results obtained using Genetic Algorithms, Particle swarm optimization, Differential Evolution and Artificial Bee Colony algorithm are presented to establish the significance of the study.

An Efficient Method to Generate Test Data for Software Structural Testing Using Artificial Bee Colony Optimization Algorithm

2017 ◽  
Vol 27 (06) ◽  
pp. 951-966 ◽  
Author(s):  
Zohreh Karimi Aghdam ◽  
Bahman Arasteh
Keyword(s):  
Software Testing ◽  
Test Data ◽  
Large Scale ◽  
Artificial Bee Colony ◽  
Fitness Function ◽  
Structural Testing ◽  
Branch Coverage ◽  
Bee Colony ◽  
Coverage Criterion

Software testing is a process for determining the quality of software system. Many small and medium-sized software projects can be manually tested. Nevertheless, due to the widespread extension of software in large-scale projects, testing them will be highly time consuming and costly. Hence, automated software testing (AST) is considered to be as a solution which can ease and simplify heavy and cumbersome tasks involved in software testing. For AST, certain data are needed through which the quality of systems can be evaluated. In this paper, an artificial bee colony (ABC) algorithm was used for solving the issue of test data generation and branch coverage criterion was used as a fitness function for optimizing the proposed solutions. For doing comparisons, seven well-known and traditional programs in the literature were used as benchmarks. The experimental results indicate that our method, on average, outperforms simulated annealing, genetic algorithm, particle swarm optimization and ant colony optimization based on the following four criteria: 99.99% average branch coverage, 99.94% success rate, 3.59 average convergence generation and 0.18[Formula: see text]ms average execution time.

Automatic Test Data Generation Using Bio-Inspired Algorithms

2021 ◽  
pp. 848-866
Author(s):  
Madhumita Panda ◽  
Sujata Dash
Keyword(s):  
Test Data ◽  
Artificial Bee Colony Algorithm ◽  
Cuckoo Search ◽  
Metaheuristic Algorithms ◽  
Test Data Generation ◽  
Data Generation ◽  
Bee Colony ◽  
Path Coverage ◽  
Automatic Test Data Generation ◽  
Generate Test Data

This chapter presents an overview of some widely accepted bio-inspired metaheuristic algorithms which would be helpful in solving the problems of software testing. Testing is an integral part of the software development process. A sizable number of Nature based algorithms coming under the per- view of metaheuristics have been used by researchers to solve practical problems of different disciplines of engineering and computer science, and software engineering. Here an exhaustive review of metaheuristic algorithms which have been employed to optimize the solution of test data generation for past 20 -30 years is presented. In addition to this, authors have reviewed their own work has been developed particularly to generate test data for path coverage based testing using Cuckoo Search and Gravitational Search algorithms. Also, an extensive comparison with the results obtained using Genetic Algorithms, Particle swarm optimization, Differential Evolution and Artificial Bee Colony algorithm are presented to establish the significance of the study.

scholarly journals PERFORMANCE ANALYSIS OF TEST DATA GENERATION FOR PATH COVERAGE BASED TESTING USING THREE METAHEURISTIC ALGORITHMS

2014 ◽  
pp. 68-75
Author(s):  
MADHUMITA PANDA ◽  
PARTHA PRATIM SARANGI
Keyword(s):  
Genetic Algorithms ◽  
Differential Evolution ◽  
Test Data ◽  
Artificial Bee Colony ◽  
Test Data Generation ◽  
Data Generation ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Path Coverage ◽  
Bee Colony Optimization

This paper discusses an approach to generate test data for path coverage based testing using Genetic Algorithms, Differential Evolution and Artificial Bee Colony optimization algorithms. Control flow graph and cyclomatic complexity of the example program has been used to find out the number of feasible paths present in the program and it is compared with the actual no of paths covered by the evolved test cases using those meta-heuristic algorithms. Genetic Algorithms, Artificial Bee Colony optimization and Differential Evolution are acting here as meta-heuristic search paradisms for path coverage based test data generation. Finally the performance of the test data generation using three meta-heuristic optimization algorithms are empirically evaluated and compared.

Test Data Generation and Selection Using Levy Flight-Based Firefly Algorithm

2021 ◽  
Vol 9 (2) ◽  
pp. 18-34
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Test Data ◽  
Firefly Algorithm ◽  
Optimization Problem ◽  
Structural Testing ◽  
Software Test ◽  
Testing Time ◽  
Test Data Generation ◽  
Data Generation ◽  
Test Suite Optimization ◽  
Test Optimization

This article discusses the application of an improved version of the firefly algorithm for the test suite optimization problem. Software test optimization refers to optimizing test data generation and selection for structural testing criteria for white box testing. This will subsequently reduce the two most costly activities performed during testing: time and cost. Recently, various search-based approaches proved very interesting results for the software test optimization problem. Also, due to no free lunch theorem, scientists are continuously searching for more efficient and convergent methods for the optimization problem. In this paper, firefly algorithm is modified in a way that local search ability is improved. Levy flights are incorporated into the firefly algorithm. This modified algorithm is applied to the software test optimization problem. This is the first application of Levy-based firefly algorithm for software test optimization. Results are shown and compared with some existing metaheuristic approaches.

AUTOMATIC PRODUCTION OF TEST DATA WITH A MULTIPLE BATCH-OPTIMISTIC METHOD

2009 ◽  
Vol 18 (01) ◽  
pp. 61-80 ◽  
Author(s):  
ANASTASIS A. SOFOKLEOUS ◽  
ANDREAS S. ANDREOU
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Test Data ◽  
Automatic Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Generation System ◽  
Automatic Production ◽  
Coverage Criterion ◽  
Testing Coverage

Recent research on software testing focuses on integrating techniques, such as computational intelligence, with special purpose software tools so as to minimize human effort, reduce costs and automate the testing process. This work proposes a complete software testing framework that utilizes a series of specially designed genetic algorithms to generate automatically test data with reference to the edge/condition testing coverage criterion. The framework utilizes a program analyzer, which examines the program's source code and builds dynamically program models for automatic testing, and a test data generation system that utilizes genetic algorithms to search the input space and determine a near to optimum set of test cases with respect to the testing coverage criterion. The performance of the framework is evaluated on a pool of programs consisting of both standard and random-generated programs. Finally, the proposed test data generation system is compared against other similar approaches and the results are discussed.

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