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Titile: How far have we progressed in identifying self-admitted technical debts? A comprehensive empirical study
This repository stores the source codes of the four state-of-the-art SATD comments detection approaches, and 20 Java projects whose comments were manually labeled by Maldonado et al. (10) and ourselves (10).
1. Folders Introduction
(1) MAT/dataset/ This folder stores the comments data of 20 Java projects, consisting of 40 files: 20 comments files (e.g., data--Ant.txt), 20 labels files (i.e., label--Ant).
(2) MAT/src/ This folder stores the source code of Pattern, NLP, TM, and MAT written in Java.
(3) MAT/CNN_Code/ This folder stores the source code for CNN written in Python. This code was provided by Ren et al. and we modified some code so that it can be used for cross-project predictions.
[1] X. Ren, Z. Xing, X. Xia, D. Lo, X. Wang, J. Grundy. Neural network based detection of self-admitted technical debt: From performance to explainability. ACM Transactions on Software Engineering and Methodology, 28(3), 2019: 1-45.
(4) MAT/exp_data/{approach}/ This floder stores the experimental data and classification result of a specific approach based on a specific dataset. Note that, approach is one of {Pattern, Pattern, Pattern and Pattern}.
(5) MAT/result/ This folder stores all classification results of the each approaches. In particular, MAT/result/predictions/ stores the detailed classification result for each comment of each project.