• Ammar Osaiweran

Software refactoring is a vital process that ensures the long-term maintainability and evolvability of software systems. However, for large and complex systems, it can be a complex and error-prone task. This work proposes a method of refactoring, guided by model learning and model-based testing, to ensure the correctness of software after the refactoring process. Model learning is used to learn a system under refactoring by constructing models that are used as input for the automatic generation of test cases. These tests are then executed on the system before and after refactoring, to ensure equivalence of behavior and to safeguard that no errors are introduced due to refactoring. The method is applied to a real industrial case and is demonstrated in this paper via application to a simple vending machine system. In this paper, we discuss the strengths and limitations encountered when applying the method to complex industrial applications.

software engineering, model-based development, software refactoring, software re-engineering, model-based testing, model transformation, software quality, software metrics application, software development, L* algorithm, Mealy state machine

"Integrating model learning with software refactoring in industry", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.f105-f116, March-2023, Available :http://www.jetir.org/papers/JETIR2303511.pdf

"Integrating model learning with software refactoring in industry", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppf105-f116, March-2023, Available at : http://www.jetir.org/papers/JETIR2303511.pdf