debugging model-transformation failures using dynamic tainting
Model-to-text (M2T) transforms are a class of software applications that translate a structured input into text output. The input models to such transforms are complex, and faults in the models that cause an M2T transform to generate an incorrect or incomplete output can be hard to debug. We present an approach based on dynamic tainting to assist transform users in debugging input models. The approach instruments the transform code to associate taint marks with the input-model elements, and propagate the marks to the output text. The taint marks identify the input-model elements that either contribute to an output string, or cause potentially incorrect paths to be executed through the transform, which results in an incorrect or a missing string in the output. We implemented our approach for XSL-based transforms and conducted empirical studies. Our results illustrate that the approach can significantly reduce the fault search space and, in many cases, precisely identify the input-model faults. The main benefit of our approach is that it automates, with a high degree of accuracy, a debugging task that can be tedious to perform manually.
dynamic tainting input-model faults fault localization model-driven engineering