Agile validation of model transformations using compound F-Alloy specifications

Abstract Model transformations play a key role in model driven software engineering approaches. Validation of model transformations is crucial for the quality assurance of software systems to be constructed. The relational logic based specification language Alloy and its accompanying tool the Alloy Analyzer have been used in the past to validate properties of model transformations. However Alloy based analysis of transformations suffers from several limitations. On one hand, it is time consuming and does not scale well. On the other hand, the reliance on Alloy, being a formal method, prevents the effective involvement of domain experts in the validation process which is crucial for pinpointing domain pertinent errors. Those limitations are even more severe when it comes to transformations whose input and/or output are themselves transformations (called compound transformations) because they are inherently more complex. To tackle the performance and scalability limitations, in previous work, we proposed an Alloy-based Domain Specific Language (DSL), called F-Alloy, that is tailored for model transformation specifications. Instead of pure analysis based validation, F-Alloy speeds up the validation of model transformations by applying a hybrid strategy that combines analysis with interpretation. In this paper, we formalize the notion of “hybrid analysis” and further extended it to also support efficient validation of compound transformations. To enable the effective involvement of domain experts in the validation process, we propose in this paper a new approach to model transformation validation, called Visualization-Based Validation (briefly VBV). Following VBV, representative instances of a to-be-validated model transformation are automatically generated by hybrid analysis and shown to domain experts for feedback in a visual notation that they are familiar with. We prescribe a process to guide the application of VBV to model transformations and illustrate it with a benchmark model transformation.