The dataset of the paper titled "Context-Aware Code Change Embedding for Better Patch Correctness Assessment"

The dataset of the paper titled "Context-Aware Code Change Embedding for Better Patch Correctness Assessment".

This is the online repository of the paper "Context-Aware Code Change Embedding for Better Patch Correctness Assessment". We release the source code of Cache, the patches used in our evaluation, as well as the experiment results.

Patches: Two patch benchmarks included in our study.

Small: The 1,183 deduplicated patches from Tian's ASE20 paper and Wang's ASE20 paper.

Large: The patches collected by ourselves, which is consist of totally 49,694 patches from RepairThemAll and ManySStuBs.

Results

RQ1: The detailed result files in RQ1, which are named by the format of [model]_[classifier].csv. For example, the file named BERT_DT.csv in the folder Small means that this file is the result of patches from Small dataset embedded by BERT and classified by Decision Tree.

Small: The detailed result files on Small dataset.

Large: The detailed result files on Large dataset.

Cross: The detailed result files of representation learning techniques when training on Large dataset and testing on Small dataset.

RQ2: The detailed result files in RQ2.

Wang_Cache.csv: The detailed result of Cache on the dataset from Wang's ASE20 paper.

ODS_Cache.csv: The datailed result of Cache on the dataset from Xiong's ICSE18 paper. We directly compare against the results reported by the authors of ODS on 139 patches from Xiong's paper since the data and source code of ODS is unavailable.

Table_5_Effectiveness_APCA.xlsx: The detailed version of Table 5 in the paper.

Table_6_Effectiveness_ODS.xlsx: The detailed version of Table 6 in the paper.

Source: The source code and lib for running Cache. Guidance for replicating our study is available at source/Readme.md. We will build a homepage for Cache on GitHub upon acceptance.