Automated Generation and Evaluation of Dataflow-Based Test Data for Object-Oriented Software

2005 ◽  
pp. 212-226 ◽  
Author(s):  
Norbert Oster
Keyword(s):  
Test Data ◽  
Object Oriented ◽  
Automated Generation

AUTOMATED GENERATION OF TEST TRAJECTORIES FOR EMBEDDED FLIGHT CONTROL SYSTEMS

2002 ◽  
Vol 12 (02) ◽  
pp. 175-200 ◽  
Author(s):  
BOJAN CUKIC ◽  
BRIAN J. TAYLOR ◽  
HARSHINDER SINGH
Keyword(s):  
Linear Regression ◽  
Control Systems ◽  
Test Data ◽  
Flight Control ◽  
Performance Metrics ◽  
Large Set ◽  
Data Generation ◽  
Statistical Regression ◽  
Automated Generation ◽  
Independent Variables

Automated generation of test cases is a prerequisite for fast testing. Whereas the research in automated test data generation addressed the creation of individual test points, test trajectory generation has attracted limited attention. In simple terms, a test trajectory is defined as a series of data points, with each (possibly multidimensional) point relying upon the value(s) of previous point(s). Many embedded systems use data trajectories as inputs, including closed-loop process controllers, robotic manipulators, nuclear monitoring systems, and flight control systems. For these systems, testers can either handcraft test trajectories, use input trajectories from older versions of the system or, perhaps, collect test data in a high fidelity system simulator. While these are valid approaches, they are expensive and time-consuming, especially if the assessment goals require many tests. We developed a framework for expanding a small, conventionally developed set of test trajectories into a large set suitable, for example, for system safety assurance. Statistical regression is the core of this framework. The regression analysis builds a relationship between controllable independent variables and closely correlated dependent variables, which represent test trajectories. By perturbing the independent variables, new test trajectories are generated automatically. Our approach has been applied in the safety assessment of a fault tolerant flight control system. Linear regression, multiple linear regression, and autoregressive techniques are compared. The performance metrics include the speed of test generation and the percentage of "acceptable" trajectories, measured by the domain specific reasonableness checks.

On the Automated Generation of Program Test Data

1976 ◽  
Vol SE-2 (4) ◽  
pp. 293-300 ◽  
Author(s):  
C.V. Ramamoorthy ◽  
S.-B.F. Ho ◽  
W.T. Chen
Keyword(s):  
Test Data ◽  
Automated Generation ◽  
Program Test

A Analysis and Research on the Methods of Object Oriented Software Testing

2013 ◽  
Vol 709 ◽  
pp. 616-619
Author(s):  
Jing Chen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Test Data ◽  
Object Oriented ◽  
Automatic Generation ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Path Coverage ◽  
Parameter Values

This paper proposes a genetic algorithm-based method to generate test cases. This method provides information for test case generation using state machine diagrams. Its feature is realizing automation through fewer generated test cases. In terms of automatic generation of test data based on path coverage, the goal is to build a function that can excellently assess the generated test data and guide the genetic algorithms to find the targeting parameter values.

Evolutionary Approaches to Test Data Generation for Object-Oriented Software

2018 ◽  
pp. 162-194
Author(s):  
Ana Filipa Nogueira ◽  
José Carlos Bregieiro Ribeiro ◽  
Francisco Fernández de Vega ◽  
Mário Alberto Zenha-Rela
Keyword(s):  
Test Data ◽  
Relevant Literature ◽  
Object Oriented ◽  
Data Generation ◽  
Open Problems ◽  
Branch Coverage ◽  
Evolutionary Testing ◽  
Generate Test Data ◽  
Tools And Techniques ◽  
Future Work

In object-oriented evolutionary testing, metaheuristics are employed to select or generate test data for object-oriented software. Techniques that analyse program structures are predominant among the panoply of studies available in current literature. For object-oriented evolutionary testing, the common objective is to reach some coverage criteria, usually in the form of statement or branch coverage. This chapter explores, reviews, and contextualizes relevant literature, tools, and techniques in this area, while identifying open problems and setting ground for future work.

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