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.

scholarly journals Research on the Computer Software Testing Method Based on Multiple Platforms

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Yongfang Sun ◽  
Jianjun Li
Keyword(s):  
Software Development ◽  
Software Testing ◽  
Computer Software ◽  
Test Accuracy ◽  
Testing Methods ◽  
Modern Life ◽  
Testing Method ◽  
Software Companies ◽  
And Control

Informationization plays an important role in modern life and production. And various software is one of the bases for it. Before it goes into service, software needs to go through many steps, including software development, design, etc. In software development, test is the key to identify and control bugs and errors in the software. Therefore, software companies often test the software to ensure that it is qualified. In recent years, more attention has been paid to a multi-platform computer software testing method, which can make up for defects in traditional testing methods to improve test accuracy. Firstly, this paper illustrates the connotation and features of software testing. Secondly, common software testing platforms and their requirements are analyzed. Finally, this paper proposes software testing method based on multiple platforms.

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.

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