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4 results for “context embeddings”
Symmetric embeddings between standard contexts of lattices of integer partitions
<p>We consider lattices of integer partitions ordered by dominance, and their standard contexts as studied in formal concept analysis. The present dataset contains all 29 symmetric context embeddings of the standard context of the lattice of partitions of the integer 8 into the standard context of the lattice of 10. Further information and a detailed description of the files in the dataset can be obtained from the file context_embeddings.pdf (the code source for this file is given in context_embeddings.tex).</p>
The dataset of the paper titled "Context-Aware Code Change Embedding for Better Patch Correctness Assessment"
<p>The dataset of the paper titled "Context-Aware Code Change Embedding for Better Patch Correctness Assessment".</p> <p>This is the online repository of the paper "Context-Aware Code Change Embedding for Better Patch Correctness Assessment" under review by ASE2021. We release the source code of Cache, the patches used in our evaluation, as well as the experiment results.</p> <ul> <li> <p>Patches: Two patch benchmarks included in our study.</p> <ul> <li>Small: The 1,183 deduplicated patches from Tian's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/3/Evaluating-Representation-Learning-of-Code-Changes-for-Predicting-Patch-Correctness-i">ASE20</a> paper and Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/Automated-Patch-Correctness-Assessment-How-Far-are-We-">ASE20</a> paper.</li> <li>Large: The patches collected by ourselves, which is consist of totally 49,694 patches from <a href="https://dl.acm.org/doi/10.1145/3338906.3338911">RepairThemAll</a> and <a href="https://dl.acm.org/doi/10.1145/3379597.3387491">ManySStuBs</a>.</li> </ul> </li> <li> <p>Results</p> <ul> <li> <p>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>.</p> <ul> <li>Small: The detailed result files on Small dataset.</li> <li>Large: The detailed result files on Large dataset.</li> <li> <p>Cross: The detailed result files of representation learning techniques when training on Large dataset and testing on Small dataset.</p> </li> </ul> </li> <li> <p>RQ2: The detailed result files in RQ2.</p> <ul> <li>Wang_Cache.csv: The detailed result of Cache on the dataset from Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/Automated-Patch-Correctness-Assessment-How-Far-are-We-">ASE20</a> paper.</li> <li> <p>ODS_Cache.csv: The datailed result of Cache on the dataset from Xiong's <a href="https://dl.acm.org/doi/10.1145/3180155.3180182">ICSE18</a> 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.</p> </li> <li> <p>Table_5_Effectiveness_APCA.xlsx: The detailed version of <strong>Table 5</strong> in the paper.</p> </li> <li> <p>Table_6_Effectiveness_ODS.xlsx: The detailed version of <strong>Table 6</strong> in the paper.</p> </li> </ul> </li> </ul> </li> <li>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.</li> </ul>
The dataset of the paper titled "Context-Aware Code Change Embedding for Better Patch Correctness Assessment"
<p>This is the online repository of the paper "Context-Aware Code Change Embedding for Better Patch Correctness Assessment" under review by SANER2021. We release the source code of Cache, the patches used in our evaluation, as well as the experiment results.</p> <ul> <li> <p>Patches: Three patch benchmarks included in our study. </p> <ul> <li> <p>Tian: The patches from Tian's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/3/Evaluating-Representation-Learning-of-Code-Changes-for-Predicting-Patch-Correctness-i">ASE20</a> paper.</p> </li> <li> <p>Wang: The patches from Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/Automated-Patch-Correctness-Assessment-How-Far-are-We-">ASE20</a> paper.</p> </li> <li> <p>Cache: The patches collected by ourselves, which is consist of 17,377 deduplicated overfitting patches from <a href="https://dl.acm.org/doi/10.1145/3338906.3338911">RepairThemAll</a> and 17,377 instances from <a href="https://dl.acm.org/doi/10.1145/3379597.3387491">ManySStuBs</a>(used as correct patches).</p> </li> </ul> </li> <li> <p>Results:</p> <ul> <li> <p>RQ1: The detailed result files in RQ1, which are named by the format of <em>[model]_[classifier]</em>.csv.</p> <p>For example, the file named <strong>BERT_DT.csv</strong> in the folder <strong>Tian's_dataset</strong> means that this file is the result of patches from <strong>Tian's</strong> study embedded by <strong>BERT</strong> and classified by <strong>Decision Tree</strong>.</p> <ul> <li> <p>Tian's_dataset : The detailed result files on Tian's dataset. </p> </li> <li> <p>Cache_dataset : The detailed result files on our own dataset. </p> </li> <li> <p>Cross_dataset : The detailed result files of representation learning techniques when training on our own dataset and testing on Tian's dataset.</p> </li> </ul> </li> <li>RQ2: The detailed result files in RQ2. <ul> <li> <p>Wang_Cache.csv: The detailed result of Cache on the dataset from Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/">ASE20</a>.</p> </li> <li> <p>ODS_Cache.csv: The datailed result of Cache on the dataset from Xiong's <a href="https://dl.acm.org/doi/10.1145/3180155.3180182">ICSE18</a> 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.</p> </li> </ul> </li> </ul> </li> </ul> <p>Source: The source code and lib for running Cache is available at <a href="https://github.com/APR-Study/Cache">https://github.com/APR-Study/Cache</a>.</p>
The dataset of the paper titled "Context-Aware Code Change Embedding for Better Patch Correctness Assessment"
<p>The dataset of the paper titled "Context-Aware Code Change Embedding for Better Patch Correctness Assessment".</p> <p>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.</p> <ul> <li> <p>Patches: Two patch benchmarks included in our study.</p> <ul> <li>Small: The 1,183 deduplicated patches from Tian's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/3/Evaluating-Representation-Learning-of-Code-Changes-for-Predicting-Patch-Correctness-i">ASE20</a> paper and Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/Automated-Patch-Correctness-Assessment-How-Far-are-We-">ASE20</a> paper.</li> <li>Large: The patches collected by ourselves, which is consist of totally 49,694 patches from <a href="https://dl.acm.org/doi/10.1145/3338906.3338911">RepairThemAll</a> and <a href="https://dl.acm.org/doi/10.1145/3379597.3387491">ManySStuBs</a>.</li> </ul> </li> <li> <p>Results</p> <ul> <li> <p>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>.</p> <ul> <li>Small: The detailed result files on Small dataset.</li> <li>Large: The detailed result files on Large dataset.</li> <li> <p>Cross: The detailed result files of representation learning techniques when training on Large dataset and testing on Small dataset.</p> </li> </ul> </li> <li> <p>RQ2: The detailed result files in RQ2.</p> <ul> <li>Wang_Cache.csv: The detailed result of Cache on the dataset from Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/Automated-Patch-Correctness-Assessment-How-Far-are-We-">ASE20</a> paper.</li> <li> <p>ODS_Cache.csv: The datailed result of Cache on the dataset from Xiong's <a href="https://dl.acm.org/doi/10.1145/3180155.3180182">ICSE18</a> 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.</p> </li> <li> <p>Table_5_Effectiveness_APCA.xlsx: The detailed version of <strong>Table 5</strong> in the paper.</p> </li> <li> <p>Table_6_Effectiveness_ODS.xlsx: The detailed version of <strong>Table 6</strong> in the paper.</p> </li> </ul> </li> </ul> </li> <li>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.</li> </ul>
ScienceDex guides
Understand access before you commit
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