Fixing Multiple Type Errors in Model Transformations with Alternative Oracles to Test Cases

TL;DR

This paper presents an approach to automatically fix multiple type errors in model transformations by exploring patch spaces guided by objectives to reduce errors and preserve behavior, using evolutionary algorithms.

Contribution

It introduces a novel method combining basic edit operations and heuristics to correct type errors without predefined patches or behavior guards.

Findings

Automatically corrected over 82% of type errors in two case studies.

Corrected over 56% of errors in the third case study.

Effective use of NSGA-II for guiding patch search in model transformations.

Abstract

This paper addresses the issue of correcting type errors in model transformations in realistic scenarios where neither predefined patches nor behavior-safe guards such as test suites are available. Instead of using predefined patches targeting isolated errors of specific categories, we propose to explore the space of possible patches by combining basic edit operations for model transformation programs. To guide the search, we define two families of objectives: one to limit the number of type errors and the other to preserve the transformation behavior. To approximate the latter, we study two objectives: minimizing the number of changes and keeping the changes local. Additionally, we define four heuristics to refine candidate patches to increase the likelihood of correcting type errors while preserving the transformation behavior. We implemented our approach for the ATL language using the evolutionary algorithm NSGA-II, and performed an evaluation based on three published case studies. The evaluation results show that our approach was able to automatically correct on average more than82% of type errors for two cases and more than 56% for the third case.