SOFTWARE TESTING USING GENETIC ALGORITHMS

2010 ◽  
pp. 297-313
Author(s):  
M. RAY ◽  
D. P. MOHAPATRA
Keyword(s):  
Genetic Algorithms ◽  
Software Testing

Implementation of Heuristic Technique and Genetic Algorithms in Shikaku Puzzle Problem

2011 ◽  
Vol 58-60 ◽  
pp. 1860-1865 ◽  
Author(s):  
Samuel Lukas ◽  
Arnold Aribowo ◽  
Steven Christian Halim
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Computer Software ◽  
Simple Rule ◽  
Rectangular Grid ◽  
Heuristic Technique ◽  
Logic Puzzle

Shikaku is a logic puzzle published by Nikoli at 2005. Shikaku has a very simple rule. This puzzle is played on a rectangular grid. Some of the squares in the grid are numbered. The main objective is to create partitions inside the grid. Each partition must have exactly one number, and the number represents the area of the partition. Then the partition’s shape must be a rectangular or a square. The aim of this research is discussing how can computer software be able to solve the Shikaku problem by implementing heuristic technique and genetics algorithms. Initially the Shikaku problem is inputted into the system. Firstly, the software will solve the problem by applying heuristics methods with some logic rules. All logic rules are created and implemented into the software so that the software can minimize the partitions possibilities to the problem. If this heuristics method still can not solve the problem then genetic algorithms will be executed to find the solution. This paper elaborates from how the problem be modelled and also be implemented until software testing to ensure that the solver worked as expected. The implementation consists of a virtual puzzle board with three different size, genetic algorithms parameters, and ability to create, save, load, and solve puzzle. Software testing is conducted to find how fast the system can solve the problem.

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.

Towards Migrating Genetic Algorithms for Test Data Generation to the Cloud

2012 ◽  
pp. 113-135 ◽  
Author(s):  
Sergio Di Martino ◽  
Filomena Ferrucci ◽  
Valerio Maggio ◽  
Federica Sarro
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Test Data ◽  
Optimization Problems ◽  
Research Area ◽  
Test Data Generation ◽  
Data Generation ◽  
Cloud Infrastructure ◽  
Computational Resources ◽  
Mapreduce Paradigm

Search-Based Software Testing is a well-established research area, whose goal is to apply meta-heuristic approaches, like Genetic Algorithms, to address optimization problems in the testing domain. Even if many interesting results have been achieved in this field, the heavy computational resources required by these approaches are limiting their practical application in the industrial domain. In this chapter, the authors propose the migration of Search-Based Software Testing techniques to the Cloud aiming to improve their performance and scalability. Moreover, they show how the use of the MapReduce paradigm can support the parallelization of Genetic Algorithms for test data generation and their migration in the Cloud, thus relieving software company from the management and maintenance of the overall IT infrastructure and developers from handling the communication and synchronization of parallel tasks. Some preliminary results are reported, gathered by a proof-of-concept developed on the Google’s Cloud Infrastructure.

scholarly journals Software Testing Using Genetic Algorithms

2016 ◽  
Vol 7 (2) ◽  
pp. 21-33 ◽  
Author(s):  
Akshat Sharma ◽  
Rishon Patani ◽  
Ashish Aggarwal
Keyword(s):  
Genetic Algorithms ◽  
Software Testing

Genetic Algorithms in Test Design Automation

2014 ◽  
Vol 693 ◽  
pp. 153-158 ◽  
Author(s):  
Michal Sroka ◽  
Roman Nagy ◽  
Dominik Fisch
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Design Automation ◽  
State Of The Art ◽  
Embedded Software ◽  
Test Design ◽  
Test Case ◽  
Current State ◽  
Development Costs ◽  
Case Design

Automation in the software testing process has significant impact on the overall software development in industry. Therefore, any automation in software testing has huge influence on overall development costs. The present article reviews the current state of the art of test case design automation via genetic algorithms. Three approaches applied in software testing are described with regards to their applicability in the testing of embedded software.

Improved Genetic Algorithms for Software Testing Cases Generation

2013 ◽  
Vol 380-384 ◽  
pp. 1464-1468
Author(s):  
Shun Kun Yang ◽  
Fu Ping Zeng
Keyword(s):  
Genetic Algorithms ◽  
Software Testing ◽  
Fitness Function ◽  
Automatic Generation ◽  
Improved Genetic Algorithm ◽  
Dynamic Adjustment ◽  
Genetic Operators ◽  
Adaptive Genetic Algorithms ◽  
Crossover And Mutation ◽  
Testing Coverage

In order to realize the adaptive Genetic Algorithms to balance the contradiction between algorithm convergence rate and algorithm accuracy for automatic generation of software testing cases, improved Genetic Algorithms is proposed for different aspects. Orthogonal method and Equivalence partitioning are employed together to make the initial testing population more effective with more reasonable coverage; Genetic operators of Crossover and Mutation is defined adaptively by the dynamic adjustment according to multi-objective Fitness function, which can guide the testing process more properly and realize the biggest testing coverage to find more defects as far as possible. Finally, the improved Genetic Algorithm are compared and analyzed by testing one benchmark program to verify its feasibility and effectiveness.

An Approach to Generate Test Cases by Multi-Path Based on Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3976-3979
Author(s):  
Yu Liu ◽  
Feng Qin Wang ◽  
Xiu Li Zhao
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Software Testing ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Test Environment ◽  
Quality And Reliability ◽  
Classification Procedures

Software testing is important to ensure the quality and reliability of the software.The improvement on the automation of test case generation is the entire key to improve the automation of the testing process.It helps a lot in the generation of test cases to construct multi-path model.It is based on genetic algorithm with three parts which are the test environment construction, the genetic algorithms and the operating environment.It’s feasibility and efficiency is verified by triangle classification procedures.

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