Automatic Test Data Generation using Metaheuristic Cuckoo Search Algorithm

2015 ◽  
Vol 5 (2) ◽  
pp. 16-29 ◽  
Author(s):  
Madhumita Panda ◽  
Partha Pratim Sarangi ◽  
Sujata Dash
Keyword(s):  
Test Data ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Cuckoo Search Algorithm ◽  
Benchmark Problems ◽  
Unit Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
High Convergence Rate

The proposed work emphasizes on the automated process of test data generation for unit testing of structured programs, targeting complete path coverage of the software under test. In recent years, Cuckoo Search (CS) has been successfully applied in many engineering applications because of its high convergence rate to the global solution. The authors motivated with the performance of Cuckoo search, utilized it to generate test suits for the standard benchmark problems, covering entire search space of the input data in less iterations. The experimental results reveal that the proposed approach covers entire search space generating test data for all feasible paths of the problem in few number of generations. It is observed that proposed approach gives promising results and outperforms other reported algorithms and it can be an alternative approach in the field of test data generation.

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.

A Survey of the Cuckoo Search and Its Applications in Real-World Optimization Problems

2017 ◽  
pp. 541-555 ◽  
Author(s):  
Christopher Expósito-Izquierdo ◽  
Airam Expósito-Márquez
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Population Based ◽  
Cuckoo Search Algorithm ◽  
Breeding Strategy ◽  
Egg Laying ◽  
Practical Applications ◽  
Wide Range

The chapter at hand seeks to provide a general survey of the Cuckoo Search Algorithm and its most highlighted variants. The Cuckoo Search Algorithm is a relatively recent nature-inspired population-based meta-heuristic algorithm that is based upon the lifestyle, egg laying, and breeding strategy of some species of cuckoos. In this case, the Lévy flight is used to move the cuckoos within the search space of the optimization problem to solve and obtain a suitable balance between diversification and intensification. As discussed in this chapter, the Cuckoo Search Algorithm has been successfully applied to a wide range of heterogeneous optimization problems found in practical applications over the last few years. Some of the reasons of its relevance are the reduced number of parameters to configure and its ease of implementation.

scholarly journals An Analysis on the Applicability of Meta-Heuristic Searching Techniques for Automated Test Data Generation in Automatic Programming Assessment

2019 ◽  
Vol 16 (2(SI)) ◽  
pp. 0515 ◽  
Author(s):  
Musa Et al.
Keyword(s):  
Test Data ◽  
Comparative Evaluation ◽  
Dynamic Testing ◽  
Cuckoo Search ◽  
Automatic Programming ◽  
Generation Process ◽  
Test Data Generation ◽  
Data Generation ◽  
Automated Test ◽  
Automated Test Data Generation

Automatic Programming Assessment (APA) has been gaining lots of attention among researchers mainly to support automated grading and marking of students’ programming assignments or exercises systematically. APA is commonly identified as a method that can enhance accuracy, efficiency and consistency as well as providing instant feedback on students’ programming solutions. In achieving APA, test data generation process is very important so as to perform a dynamic testing on students’ assignment. In software testing field, many researches that focus on test data generation have demonstrated the successful of adoption of Meta-Heuristic Search Techniques (MHST) so as to enhance the procedure of deriving adequate test data for efficient testing. Nonetheless, thus far the researches on APA have not yet usefully exploited the techniques accordingly to include a better quality program testing coverage. Therefore, this study has conducted a comparative evaluation to identify any applicable MHST to support efficient Automated Test Data Generation (ATDG) in executing a dynamic-functional testing in APA. Several recent MHST are included in the comparative evaluation combining both the local and global search algorithms ranging from the year of 2000 until 2018. Result of this study suggests that the hybridization of Cuckoo Search with Tabu Search and lévy flight as one of promising MHST to be applied, as it’s outperforms other MHST with regards to number of iterations and range of inputs.

Using Cuckoo Search Algorithm for Hybrid Flow Shop Scheduling Problems Under Makespan Criterion

2018 ◽  
pp. 379-400
Author(s):  
M. K. Marichelvam ◽  
Ömür Tosun
Keyword(s):  
Parallel Machines ◽  
Flow Shop ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Benchmark Problems ◽  
Scheduling Problems ◽  
Hybrid Flow Shop Scheduling

In this chapter, cuckoo search algorithm (CSA) is used to solve the multistage hybrid flow shop (HFS) scheduling problems with parallel machines. The objective is the minimization of makespan. The HFS scheduling problems are proved to be strongly non-deterministic polynomial time-hard (NP-hard). Proposed CSA algorithm has been tested on benchmark problems addressed in the literature against other well-known algorithms. The results are presented in terms of percentage deviation (PD) of the solution from the lower bound. The results indicate that the proposed CSA algorithm is quite effective in reducing makespan because average PD is observed as 1.531, whereas the next best algorithm has result of average PD of 2.295, which is, in general, nearly 50% worse, and other algorithms start from 2.645.

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.

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