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.
Automated Test Data Generation Based on a Genetic Algorithm with Maximum Code Coverage and Population Diversity
