Automated generation of test cases from output domain of an embedded system using Genetic algorithms

Thorough testing of embedded systems is required especially when the systems are related to monitoring and controlling the mission critical and safety critical systems. The embedded systems must be tested comprehensively which include testing hardware, software and both together. Embedded systems are highly intelligent devices that are infiltrating our daily lives such as the mobile in your pocket, and wireless infrastructure behind it, routers, home theatre system, the air traffic control station etc. Software now makes up 90% of the value of these devices. In this paper, authors present different methods to test an embedded system using test cases generated through combinatorial techniques. The experimental results for testing a TMCNRS (Temperature Monitoring and Controlling Nuclear Reactor System) using test cases generated from combinatorial methods are also shown.

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MODEL CHECKING FOR VERIFICATION OF MANDATORY ACCESS CONTROL MODELS AND PROPERTIES

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s021819401100513x ◽

2011 ◽

Vol 21 (01) ◽

pp. 103-127 ◽

Cited By ~ 18

Author(s):

VINCENT C. HU ◽

D. RICHARD KUHN ◽

TAO XIE ◽

JEEHYUN HWANG

Keyword(s):

Access Control ◽

Test Cases ◽

Covering Array ◽

Model Checker ◽

Challenging Problem ◽

Automated Generation ◽

Mandatory Access Control ◽

Access Control Models ◽

Security Properties ◽

Property Verification

Mandatory access control (MAC) mechanisms control which users or processes have access to which resources in a system. MAC policies are increasingly specified to facilitate managing and maintaining access control. However, the correct specification of the policies is a very challenging problem. To formally and precisely capture the security properties that MAC should adhere to, MAC models are usually written to bridge the rather wide gap in abstraction between policies and mechanisms. In this paper, we propose a general approach for property verification for MAC models. The approach defines a standardized structure for MAC models, providing for both property verification and automated generation of test cases. The approach expresses MAC models in the specification language of a model checker and expresses generic access control properties in the property language. Then the approach uses the model checker to verify the integrity, coverage, and confinement of these properties for the MAC models and finally generates test cases via combinatorial covering array for the system implementations of the models.

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Software Test Data Generation based on Path Testing using Genetic Algorithms

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f9775.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 4466-4473

Keyword(s):

Genetic Algorithms ◽

Test Data ◽

Iterative Algorithms ◽

Continuous Functions ◽

Software Test ◽

Test Cases ◽

Test Data Generation ◽

Data Generation ◽

Optimal Solutions ◽

Automated Test

Test data generation is the task of constructing test cases for predicting the acceptability of novel or updated software. Test data could be the original test suite taken from previous run or imitation data generated afresh specifically for this purpose. The simplest way of generating test data is done randomly but such test cases may not be competent enough in detecting all defects and bugs. In contrast, test cases can also be generated automatically and this has a number of advantages over the conventional manual method. Genetic Algorithms, one of the automation techniques, are iterative algorithms and apply basic operations repeatedly in greed for optimal solutions or in this case, test data. By finding out the most error-prone path using such test cases one can reduce the software development cost and improve the testing efficiency. During the evolution process such algorithms pass on the better traits to the next generations and when applied to generations of software test data they produce test cases that are closer to optimal solutions. Most of the automated test data generators developed so far work well only for continuous functions. In this study, we have used Genetic Algorithms to develop a tool and named it TG-GA (Test Data Generation using Genetic Algorithms) that searches for test data in a discontinuous space. The goal of the work is to analyze the effectiveness of Genetic Algorithms in automated test data generation and to compare its performance over random sampling particularly for discontinuous spaces.

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