Using Traceability to Enhance Mutation Analysis Dedicated to Model Transformation

Techniques initially used for programs require modifications to be properly used with to model transformation characteristics. Mutation analysis is one of these techniques. It aims to qualify a test data set by analyzing the execution results of intentionally faulty program versions. If the degree of qualification is not satisfactory, the test data set has to be improved. This step is currently relatively fastidious and manually performed. In this paper, we propose an approach based on traceability mechanisms to ease the test model set improvement in the mutation analysis process. A benchmark shows that the part of the input model to change is automatically and quickly identified. A new model is then created in order to raise the quality of the test data set.

I. Introduction

When a program written in has not the expected behavior or is erroneous, the programmers look for the faults in their program. Indeed, they trust in the compiler. This latter has been largely tested because a fault in a compiler may spread over lot of programs since a compiler is used many times to justify the efforts relative to its development. Similarly, model transformations that form the skeleton of model based system development and so enable to generate code from high level model specifications have to be largely tested and trustworthy.