Software maintenance

Software engineering

Variety (cybernetics)

Software system

Code refactoring

Commit

Artificial intelligence

Set (abstract data type)

Programming language

Code (set theory)

Program comprehension

Software

Computer science

Software development

Database

IEEE Transactions on Software Engineering

Refactoring detection is crucial for a variety of applications and tasks: (i) empirical studies about code evolution, (ii) tools for library API migration, (iii) code reviews and change comprehension. However, recent research has questioned the accuracy of the state-of-the-art refactoring mining tools, which poses threats to the reliability of the detected refactorings. Moreover, the majority of refactoring mining tools depend on code similarity thresholds. Finding universal threshold values that can work well for all projects, regardless of their architectural style, application domain, and development practices is extremely challenging. Therefore, in a previous work [N. Tsantalis, M. Mansouri, L. M. Eshkevari, D. Mazinanian, and D. Dig, Accurate and efficient refactoring detection in commit history, in 40th International Conference on Software Engineering, 2018, pp. 483–494], we introduced the first refactoring mining tool that does not require any code similarity thresholds to operate. In this work, we extend our tool to support low-level refactorings that take place within the body of methods. To evaluate our tool, we created one of the most accurate, complete, and representative refactoring oracles to date, including 7,226 true instances for 40 different refactoring types detected by one (minimum) up to six (maximum) different tools, and validated by one up to four refactoring experts. Our evaluation showed that our approach achieves the highest average precision (99.6 percent) and recall (94 percent) among all competitive tools, and on median is 2.6 times faster than the second faster competitive tool.

RefactoringMiner 2.0