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" under review by ISSTA2021. 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 <em><code>[model]_[classifier].csv</code></em>. For example, the file named <em><code>BERT_DT.csv</code></em> in the folder <em><code>Small</code></em> means that this file is the result of patches from <strong>Small</strong> dataset embedded by <strong>BERT</strong> and classified by <strong>Decision Tree</strong>. 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 <strong>Table 5</strong> in the paper. Table_6_Effectiveness_ODS.xlsx: The detailed version of <strong>Table 6</strong> in the paper. Source: The source code and lib for running Cache. Guidance for replicating our study is available at <em><code>source/Readme.md</code></em>. We will build a homepage for Cache on GitHub upon acceptance.本数据集对应论文《面向更优补丁正确性评估的上下文感知代码变更嵌入》（Context-Aware Code Change Embedding for Better Patch Correctness Assessment），该论文目前正在被ISSTA 2021（International Symposium on Software Testing and Analysis 2021）审阅。我们公开了Cache的源代码、评估所用的补丁集，以及全部实验结果。 补丁集：本研究包含两类补丁基准。Small基准：来自Tian于ASE20（Automated Software Engineering 2020）论文与Wang于ASE20论文中经过去重的1183个补丁。Large基准：由我们自行收集的补丁集，共计49694个，源自RepairThemAll与ManySStuBs项目。 实验结果（RQ1）：对应研究问题1的详细结果文件，命名格式为<em><code>[model]_[classifier].csv</code></em>。例如，<em><code>Small</code></em>文件夹下的<em><code>BERT_DT.csv</code></em>文件，表示该文件为使用<strong>BERT</strong>对<strong>Small</strong>数据集的补丁进行嵌入，并通过<strong>决策树(Decision Tree)</strong>分类得到的实验结果。其中：<em><code>Small</code></em>文件夹：针对Small数据集的详细实验结果文件；<em><code>Large</code></em>文件夹：针对Large数据集的详细实验结果文件；<em><code>Cross</code></em>文件夹：采用表征学习技术，以Large数据集为训练集、Small数据集为测试集得到的详细实验结果文件。 实验结果（RQ2）：对应研究问题2的详细结果文件：<em><code>Wang_Cache.csv</code></em>：Cache模型在Wang于ASE20论文的数据集上的详细实验结果；<em><code>ODS_Cache.csv</code></em>：Cache模型在Xiong于ICSE18（International Conference on Software Engineering 2018）论文的数据集上的详细实验结果。由于ODS的数据集与源代码无法获取，我们直接与原作者在Xiong论文的139个补丁上报告的结果进行了对比。 <strong>Table_5_Effectiveness_APCA.xlsx</strong>：论文中表5的详细版本；<strong>Table_6_Effectiveness_ODS.xlsx</strong>：论文中表6的详细版本。 源代码目录：包含运行Cache所需的源代码与依赖库。复现本研究的操作指南详见<em><code>source/Readme.md</code></em>。待论文被录用后，我们将在GitHub平台搭建Cache的专属主页。