Surpassing Threshold Barriers: Evaluating the Efficacy of Nature-Inspired Algorithms in Detecting Applied Refactorings

This page is provided as supplementary material for the article: Surpassing Threshold Barriers: Evaluating the Efficacy of Nature-Inspired Algorithms in Detecting Applied Refactorings. More specifically, we provide the results, the refactorings detected by each tool, as a downloadable JSON file. 

Results.json: You can access the results comparing ACA with RefDetect, RefactoringMiner, NSGA-II, and the Greedy algorithm.

• Overview of JSON Template: Each entry in this file provides details about commits, including their SHA-1 hash, GitHub URL, and an exhaustive list of refactorings identified in that specific commit. Refactorings are defined by their type (e.g., 'RenameClass') and are accompanied by detailed descriptions of the refactoring. Additionally, the entry specifies the tools that detected the refactoring, such as 'ACA' and 'RefactoringMiner.' The validation field indicates whether the refactoring was confirmed, using 'TP' for true positive and 'FP' for false positive. Optional comment information may be also included.

Times.xlsx: The Excel file containing the time taken by each tool to identify refactoring in each commit.

Results.xlsx: The Excel file contains precision, recall, and F-score metrics for all identified refactoring types.