deploy: 3e5f875

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@@ -121,11 +121,9 @@ <h4 id="-browse-papers-by-tag">🏷 Browse Papers by Tag</h4>
 
 <tag><a href="/tags.html#adversarial">adversarial</a></tag>
 <tag><a href="/tags.html#API">API</a></tag>
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paper-abstracts.json

@@ -1 +1 @@
-[["wang2020cocogum", "CoCoGUM: Contextual Code Summarization with Multi-Relational GNN on UMLs"], ["haque2020improved", "Improved Automatic Summarization of Subroutines via Attention to File Context"], ["ye2020leveraging", "Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning"], ["shido2019automatic", "Automatic Source Code Summarization with Extended Tree-LSTM"]]
+[["wang2020cocogum", "CoCoGUM: Contextual Code Summarization with Multi-Relational GNN on UMLs"], ["haque2020improved", "Improved Automatic Summarization of Subroutines via Attention to File Context"], ["ye2020leveraging", "Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning"], ["fernandes2019structured", "Structured Neural Summarization"]]

papers.html

@@ -1 +1 @@
-[["fried2022incoder", "InCoder: A Generative Model for Code Infilling and Synthesis"], ["deze2022bridging", "Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding"], ["bavishi2019autopandas", "AutoPandas: neural-backed generators for program synthesis"], ["richardson2017code2text", "The Code2Text Challenge: Text Generation in Source Code Libraries"]]
+[["fried2022incoder", "InCoder: A Generative Model for Code Infilling and Synthesis"], ["deze2022bridging", "Bridging Pre-trained Models and Downstream Tasks for Source Code Understanding"], ["bavishi2019autopandas", "AutoPandas: neural-backed generators for program synthesis"], ["kurbatova2020recommendation", "Recommendation of Move Method Refactoring Using Path-Based Representation of Code"]]

publications-metadata/ahmad2020transformer.json

@@ -1 +1 @@
-[["karampatsis2020scelmo", "SCELMo: Source Code Embeddings from Language Models"], ["bui2018hierarchical", "Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code"], ["cummins2020programl", "ProGraML: Graph-based Deep Learning for Program Optimization and Analysis"], ["peng2021how", "How could Neural Networks understand Programs?"]]
+[["karampatsis2020scelmo", "SCELMo: Source Code Embeddings from Language Models"], ["rabin2020demystifying", "Towards Demystifying Dimensions of Source Code Embeddings"], ["cummins2020programl", "ProGraML: Graph-based Deep Learning for Program Optimization and Analysis"], ["bui2018hierarchical", "Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code"]]

publications-metadata/bavarian2022efficient.json

@@ -1 +1 @@
-[["nguyen2017exploring", "Exploring API Embedding for API Usages and Applications"], ["nguyen2016mapping", "Mapping API Elements for Code Migration with Vector Representations"], ["karampatsis2020scelmo", "SCELMo: Source Code Embeddings from Language Models"], ["nguyen2014statistical", "Statistical Learning Approach for Mining API Usage Mappings for Code Migration"]]
+[["nguyen2017exploring", "Exploring API Embedding for API Usages and Applications"], ["nguyen2016mapping", "Mapping API Elements for Code Migration with Vector Representations"], ["karampatsis2020scelmo", "SCELMo: Source Code Embeddings from Language Models"], ["rabin2020demystifying", "Towards Demystifying Dimensions of Source Code Embeddings"]]

publications-metadata/bennun2018neural.json

@@ -1 +1 @@
-[["nguyen2019graph", "Graph-based Mining of In-the-Wild, Fine-grained, Semantic Code Change Patterns"], ["pravilov2021unsupervised", "Unsupervised Learning of General-Purpose Embeddings for Code Changes"], ["chakraborty2021multimodal", "On Multi-Modal Learning of Editing Source Code"], ["tufano2019learning", "On Learning Meaningful Code Changes via Neural Machine Translation"]]
+[["pravilov2021unsupervised", "Unsupervised Learning of General-Purpose Embeddings for Code Changes"], ["nguyen2019graph", "Graph-based Mining of In-the-Wild, Fine-grained, Semantic Code Change Patterns"], ["chakraborty2021multimodal", "On Multi-Modal Learning of Editing Source Code"], ["tufano2019learning", "On Learning Meaningful Code Changes via Neural Machine Translation"]]

publications-metadata/bui2018hierarchical.json

@@ -1 +1 @@
-[["wang2019learning", "Learning Scalable and Precise Representation of Program Semantics"], ["jiang2021treebert", "TreeBERT: A Tree-Based Pre-Trained Model for Programming Language"], ["siow2022learning", "Learning Program Semantics with Code Representations: An Empirical Study"], ["guo2022unixcoder", "UniXcoder: Unified Cross-Modal Pre-training for Code Representation"]]
+[["jiang2021treebert", "TreeBERT: A Tree-Based Pre-Trained Model for Programming Language"], ["wang2019learning", "Learning Scalable and Precise Representation of Program Semantics"], ["siow2022learning", "Learning Program Semantics with Code Representations: An Empirical Study"], ["guo2022unixcoder", "UniXcoder: Unified Cross-Modal Pre-training for Code Representation"]]

publications-metadata/chakraborty2018tree2tree.json

@@ -1 +1 @@
-[["karampatsis2020scelmo", "SCELMo: Source Code Embeddings from Language Models"], ["bennun2018neural", "Neural Code Comprehension: A Learnable Representation of Code Semantics"], ["guo2017semantically", "Semantically enhanced software traceability using deep learning techniques"], ["kanade2020pretrained", "Pre-trained Contextual Embedding of Source Code"]]
+[["karampatsis2020scelmo", "SCELMo: Source Code Embeddings from Language Models"], ["bennun2018neural", "Neural Code Comprehension: A Learnable Representation of Code Semantics"], ["guo2017semantically", "Semantically enhanced software traceability using deep learning techniques"], ["rabin2020demystifying", "Towards Demystifying Dimensions of Source Code Embeddings"]]