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Quick fixing ATL transformations with speculative analysis

Abstract:

Model transformations are central components of most model-based software projects. While ensuring their correctness is vital to guarantee the quality of the solution, current transformation tools provide limited support to statically detect and fix errors. In this way, the identification of errors and their correction are nowadays mostly manual activities which incur in high costs. The aim of this work is to improve this situation. Recently, we developed a static analyser that combines program analysis and constraint solving to identify errors in ATL model transformations. In this paper, we present a novel method and system that uses our analyser to propose suitable quick fixes for ATL transformation errors, notably some non-trivial, transformation-specific ones. Our approach supports speculative analysis to help developers select the most appropriate fix by creating a dynamic ranking of fixes, reporting on the consequences of applying a quick fix, and providing a pre-visualization of each quick fix application. The approach integrates seamlessly with the ATL editor. Moreover, we provide an evaluation based on existing faulty transformations built by a third party, and on automatically generated transformation mutants, which are then corrected with the quick fixes of our catalogue.