scholarly journals Regression Test Suite Reduction using an Hybrid Technique Based on BCO And Genetic Algorithm

2013 ◽  
pp. 26-33
Author(s):  
Bharti Suri ◽  
Isha Mangal ◽  
Varun Srivastava
Keyword(s):  
Optimization Problems ◽  
Minimum Time ◽  
Test Suite ◽  
Test Case ◽  
Hybrid Technique ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Test Suite Reduction ◽  
The Given ◽  
Swarm Intelligence Algorithm

Regression testing is a maintenance activity that is performed to ensure the validity of modified software. The activity takes a lot of time to run the entire test suite and is very expensive. Thus it becomes a necessity to choose the minimum set of test cases with the ability to cover all the faults in minimum time. The paper presents a new test case reduction hybrid technique based on Genetic algorithms(GA) and bee colony optimization (BCO) .GA is an evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. BCO is a swarm intelligence algorithm. The proposed approach adopts the behavior of bees to solve the given problem. It proves to be optimistic approach which provides optimum results in minimum time.

Comparison of Selected Swarm Intelligence Algorithms in Student Courses Recommendation Application

2014 ◽  
Vol 24 (01) ◽  
pp. 91-109 ◽  
Author(s):  
Janusz Sobecki
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Problem Space ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Grade Prediction ◽  
Bee Colony Optimization ◽  
Swarm Intelligence Algorithm

In this paper a comparison of a few swarm intelligence algorithms applied in recommendation of student courses is presented. Swarm intelligence algorithms are nowadays successfully used in many areas, especially in optimization problems. To apply each swarm intelligence algorithm in recommender systems a special representation of the problem space is necessary. Here we present the comparison of efficiency of grade prediction of several evolutionary algorithms, such as: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Intelligent Weed Optimization (IWO), Bee Colony Optimization (BCO) and Bat Algorithm (BA).

scholarly journals Test Case Optimization for Enhancing System Software Quality using Genetic Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 68-75
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Test Suite ◽  
Test Case ◽  
Product Lines ◽  
Reduction Techniques ◽  
Mutation Operators ◽  
Effective Manner ◽  
Software Programs ◽  
Test Suite Reduction

Software Product Lines (SPLs) embraces an enormous capacity of feature mixtures which cause challenges for evaluating software programs. Testsuite optimization plays major role to develope the quality of SPLs. In combinatorial testing (CT), pair wise fault coverage maximization and test case reduction accomplishes a substantial role for shrinking the testing cost of software programs. Many research works have been developed and designed for CT using different test suite reduction techniques. However Fuzzy clustering and TSRSO techniques do not provide a finest solution for test suite optimization problem. For that, Genetic Algorithm (GA) Technique is recommended and designed for test suite reduction in CT. Metaheuristic genetic algorithm delivers optimum solution in an effective manner. GA chooses and consolidates the testcases in a testsuite based on some principles such that maximum faults covered with minimum execution time. In Proposed GA, finest individuals are nominated for reproduction in order to create descendants of the succeeding generation. In addition, GA is a superior type of evolutionary algorithms generate finest solutions to optimization problems using selection, crossover and mutation operators. Consequently, GA is applied for resolving test suite reduction problem in CT

Test Suite Optimization Using Chaotic Firefly Algorithm in Software Testing

2017 ◽  
Vol 8 (4) ◽  
pp. 41-57 ◽  
Author(s):  
Abhishek Pandey ◽  
Soumya Banerjee
Keyword(s):  
Software Testing ◽  
Firefly Algorithm ◽  
Chaotic Behavior ◽  
Automatic Generation ◽  
Test Suite ◽  
Test Case ◽  
Branch Coverage ◽  
Bee Colony ◽  
Novel Approach ◽  
Test Suite Optimization

Software testing is time consuming and a costly activity. Effective generation of test cases is necessary in order to perform rigorous testing. There exist various techniques for effective test case generation. These techniques are based on various test adequacy criteria such as statement coverage, branch coverage etc. Automatic generation of test data has been the primary focus of software testing research in recent past. In this paper a novel approach based on chaotic behavior of firefly algorithm is proposed for test suite optimization. Test suite optimization problem is modeled in the framework of firefly algorithm. An Algorithm for test optimization based on firefly algorithm is also proposed. Experiments are performed on some benchmark Program and simulation results are compared for ABC algorithm, ACO algorithm, GA with Chaotic firefly algorithm. Major research findings are that chaotic firefly algorithm outperforms other bio inspired algorithm such as artificial bee colony, Ant colony optimization and Genetic Algorithm in terms of Branch coverage in software testing.

Significance of Biologically Inspired Optimization Techniques in Real-Time Applications

2017 ◽  
pp. 150-180
Author(s):  
Sushruta Mishra ◽  
Brojo Kishore Mishra ◽  
Hrudaya Kumar Tripathy
Keyword(s):  
Real Time ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Biologically Inspired ◽  
Swarm Optimization ◽  
Genetic Search ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

The techniques inspired from the nature based evolution and aggregated nature of social colonies have been promising and shown excellence in handling complicated optimization problems thereby gaining huge popularity recently. These methodologies can be used as an effective problem solving tool thereby acting as an optimizing agent. Such techniques are called Bio inspired computing. Our study surveys the recent advances in biologically inspired swarm optimization methods and Evolutionary methods, which may be applied in various fields. Four real time scenarios are demonstrated in the form of case studies to show the significance of bio inspired algorithms. The techniques that are illustrated here include Differential Evolution, Genetic Search, Particle Swarm optimization and artificial bee Colony optimization. The results inferred by implanting these techniques are highly encouraging.

Automated and Optimized Software Test Suite Generation Technique for Structural Testing

2016 ◽  
Vol 26 (01) ◽  
pp. 1-13 ◽  
Author(s):  
M. S. Geetha Devasena ◽  
G. Gopu ◽  
M. L. Valarmathi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Test Data ◽  
Structural Testing ◽  
Test Suite ◽  
Test Case ◽  
Hybrid Technique ◽  
Generation Process ◽  
Swarm Optimization ◽  
Test Suite Generation

Software testing consumes 50% of total software development cost. Test case design gains central importance in testing activity with respect to quality. The manual test suite generation is a time consuming and tedious task which needs automation. Unit testing is normally done in stringent time schedules by the developers or rarely by testers. In structural testing, it is not possible to check exhaustively all possible test data and the quality of test is dependent heavily on the performance of single developer or tester. Thus automation and optimization is required in generating test data to assist developer or tester with the selection of appropriate test data. A novel hybrid technique is developed to automate the test suite generation process for branch coverage criteria using evolutionary testing. The hybrid technique applies both Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to automatically generate test data. This technique improves efficiency and effectiveness of test case generation process when compared to applying Genetic Algorithm or Particle Swarm Optimization alone. The performance of proposed technique is evaluated and is observed that hybrid technique reduces the number of iterations by 47% when compared to GA and PSO applied separately and it reduces the execution time by 52% than GA and 48% than PSO.

A HYBRID ARTIFICIAL BEE COLONY OPTIMIZATION AND QUANTUM EVOLUTIONARY ALGORITHM FOR CONTINUOUS OPTIMIZATION PROBLEMS

2010 ◽  
Vol 20 (01) ◽  
pp. 39-50 ◽  
Author(s):  
HAI-BIN DUAN ◽  
CHUN-FANG XU ◽  
ZHI-HUI XING
Keyword(s):  
Evolutionary Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Experimental Comparison ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Comparison Results ◽  
Bee Colony Optimization ◽  
Continuous Optimization Problems

In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems.

scholarly journals Diversifying Focused Testing for Unit Testing

2021 ◽  
Vol 30 (4) ◽  
pp. 1-24
Author(s):  
Héctor D. Menéndez ◽  
Gunel Jahangirova ◽  
Federica Sarro ◽  
Paolo Tonella ◽  
David Clark
Keyword(s):  
Search Algorithm ◽  
Symbolic Execution ◽  
Specific Area ◽  
Test Suite ◽  
Constraint Solving ◽  
Test Case ◽  
Unit Testing ◽  
Bug Detection ◽  
Mutation Score ◽  
The Given

Software changes constantly, because developers add new features or modifications. This directly affects the effectiveness of the test suite associated with that software, especially when these new modifications are in a specific area that no test case covers. This article tackles the problem of generating a high-quality test suite to cover repeatedly a given point in a program, with the ultimate goal of exposing faults possibly affecting the given program point. Both search-based software testing and constraint solving offer ready, but low-quality, solutions to this: Ideally, a maximally diverse covering test set is required, whereas search and constraint solving tend to generate test sets with biased distributions. Our approach, Diversified Focused Testing (DFT), uses a search strategy inspired by GödelTest. We artificially inject parameters into the code branching conditions and use a bi-objective search algorithm to find diverse inputs by perturbing the injected parameters, while keeping the path conditions still satisfiable. Our results demonstrate that our technique, DFT, is able to cover a desired point in the code at least 90% of the time. Moreover, adding diversity improves the bug detection and the mutation killing abilities of the test suites. We show that DFT achieves better results than focused testing, symbolic execution, and random testing by achieving from 3% to 70% improvement in mutation score and up to 100% improvement in fault detection across 105 software subjects.

Significance of Biologically Inspired Optimization Techniques in Real-Time Applications

2020 ◽  
pp. 224-248
Author(s):  
Sushruta Mishra ◽  
Brojo Kishore Mishra ◽  
Hrudaya Kumar Tripathy
Keyword(s):  
Real Time ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Biologically Inspired ◽  
Swarm Optimization ◽  
Genetic Search ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization

The techniques inspired from the nature based evolution and aggregated nature of social colonies have been promising and shown excellence in handling complicated optimization problems thereby gaining huge popularity recently. These methodologies can be used as an effective problem solving tool thereby acting as an optimizing agent. Such techniques are called Bio inspired computing. Our study surveys the recent advances in biologically inspired swarm optimization methods and Evolutionary methods, which may be applied in various fields. Four real time scenarios are demonstrated in the form of case studies to show the significance of bio inspired algorithms. The techniques that are illustrated here include Differential Evolution, Genetic Search, Particle Swarm optimization and artificial bee Colony optimization. The results inferred by implanting these techniques are highly encouraging.

scholarly journals Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-21 ◽  
Author(s):  
Lianbo Ma ◽  
Hanning Chen ◽  
Kunyuan Hu ◽  
Yunlong Zhu
Keyword(s):  
Radio Frequency Identification ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Complete Solution ◽  
Network Planning ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Mutation Operators ◽  
Planning Optimization ◽  
Bee Colony Optimization

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

Metaheuristic Techniques for Test Case Generation

2022 ◽  
pp. 1043-1058
Author(s):  
Rashmi Rekha Sahoo ◽  
Mitrabinda Ray
Keyword(s):  
Software Testing ◽  
Selection Procedure ◽  
Primary Objective ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Metaheuristic Techniques

The primary objective of software testing is to locate bugs as many as possible in software by using an optimum set of test cases. Optimum set of test cases are obtained by selection procedure which can be viewed as an optimization problem. So metaheuristic optimizing (searching) techniques have been immensely used to automate software testing task. The application of metaheuristic searching techniques in software testing is termed as Search Based Testing. Non-redundant, reliable and optimized test cases can be generated by the search based testing with less effort and time. This article presents a systematic review on several meta heuristic techniques like Genetic Algorithms, Particle Swarm optimization, Ant Colony Optimization, Bee Colony optimization, Cuckoo Searches, Tabu Searches and some modified version of these algorithms used for test case generation. The authors also provide one framework, showing the advantages, limitations and future scope or gap of these research works which will help in further research on these works.

Sign in / Sign up

Export Citation Format

Share Document