deploy: b862149

Author: mallamanis

index.html

@@ -121,9 +121,11 @@ <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>
+<tag><a href="/tags.html#AST">AST</a></tag>
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 <tag><a href="/tags.html#benchmark">benchmark</a></tag>
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 <tag><a href="/tags.html#code completion">code completion</a></tag>
 <tag><a href="/tags.html#code generation">code generation</a></tag>
@@ -141,14 +143,18 @@ <h4 id="-browse-papers-by-tag">🏷 Browse Papers by Tag</h4>
 <tag><a href="/tags.html#execution">execution</a></tag>
 <tag><a href="/tags.html#feature location">feature location</a></tag>
 <tag><a href="/tags.html#fuzzing">fuzzing</a></tag>
+<tag><a href="/tags.html#generalizability">generalizability</a></tag>
+<tag><a href="/tags.html#generalization">generalization</a></tag>
 <tag><a href="/tags.html#GNN">GNN</a></tag>
 <tag><a href="/tags.html#grammar">grammar</a></tag>
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 <tag><a href="/tags.html#migration">migration</a></tag>
 <tag><a href="/tags.html#naming">naming</a></tag>
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 <tag><a href="/tags.html#optimization">optimization</a></tag>
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@@ -157,6 +163,7 @@ <h4 id="-browse-papers-by-tag">🏷 Browse Papers by Tag</h4>
 <tag><a href="/tags.html#repair">repair</a></tag>
 <tag><a href="/tags.html#representation">representation</a></tag>
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 <tag><a href="/tags.html#static analysis">static analysis</a></tag>
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@@ -168,6 +175,7 @@ <h4 id="-browse-papers-by-tag">🏷 Browse Papers by Tag</h4>
 <tag><a href="/tags.html#topic modeling">topic modeling</a></tag>
 <tag><a href="/tags.html#topic modelling">topic modelling</a></tag>
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+<tag><a href="/tags.html#transformation">transformation</a></tag>
 <tag><a href="/tags.html#Transformer">Transformer</a></tag>
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paper-abstracts.json

@@ -1 +1 @@
-[["cummins2018compiler", "Compiler Fuzzing through Deep Learning"], ["mammadli2020static", "Static Neural Compiler Optimization via Deep Reinforcement Learning"], ["brauckmann2020compy", "ComPy-Learn: A toolbox for exploring machine learning representations for compilers"], ["cummins2020programl", "ProGraML: Graph-based Deep Learning for Program Optimization and Analysis"]]
+[["mammadli2020static", "Static Neural Compiler Optimization via Deep Reinforcement Learning"], ["cummins2018compiler", "Compiler Fuzzing through Deep Learning"], ["brauckmann2020compy", "ComPy-Learn: A toolbox for exploring machine learning representations for compilers"], ["cummins2020programl", "ProGraML: Graph-based Deep Learning for Program Optimization and Analysis"]]

papers.html

@@ -1 +1 @@
-[["barone2017parallel", "A parallel corpus of Python functions and documentation strings for automated code documentation and code generation"], ["aggarwal2015using", "Using Machine Translation for Converting Python 2 to Python 3 Code"], ["gros2020code", "Code to Comment \"Translation\": Data, Metrics, Baselining & Evaluation"], ["roziere2021leveraging", "Leveraging Automated Unit Tests for Unsupervised Code Translation"]]
+[["barone2017parallel", "A parallel corpus of Python functions and documentation strings for automated code documentation and code generation"], ["aggarwal2015using", "Using Machine Translation for Converting Python 2 to Python 3 Code"], ["gros2020code", "Code to Comment \"Translation\": Data, Metrics, Baselining & Evaluation"], ["loyola2018content", "Content Aware Source Code Change Description Generation"]]

publications-metadata/brauckmann2020compiler.json

@@ -1 +1 @@
-[["gao2019neural", "A Neural Model for Method Name Generation from Functional Description"], ["allamanis2014mining", "Mining Idioms from Source Code"], ["roziere2021leveraging", "Leveraging Automated Unit Tests for Unsupervised Code Translation"], ["cambronero2019deep", "When Deep Learning Met Code Search"]]
+[["gao2019neural", "A Neural Model for Method Name Generation from Functional Description"], ["allamanis2014mining", "Mining Idioms from Source Code"], ["cambronero2019deep", "When Deep Learning Met Code Search"], ["roziere2021leveraging", "Leveraging Automated Unit Tests for Unsupervised Code Translation"]]

publications-metadata/clement2020pymt5.json

@@ -1 +1 @@
-[["mou2016convolutional", "Convolutional Neural Networks over Tree Structures for Programming Language Processing"], ["bui2018bilateral", "Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification"], ["bui2018hierarchical", "Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code"], ["nair2020funcgnn", "funcGNN: A Graph Neural Network Approach to Program Similarity"]]
+[["mou2016convolutional", "Convolutional Neural Networks over Tree Structures for Programming Language Processing"], ["bui2018bilateral", "Bilateral Dependency Neural Networks for Cross-Language Algorithm Classification"], ["bui2018hierarchical", "Hierarchical Learning of Cross-Language Mappings through Distributed Vector Representations for Code"], ["rabin2021generalizability", "On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations"]]

publications-metadata/he2021learning.json

@@ -1 +1 @@
-[["jung2021commitbert", "CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model"], ["bai2021jointly", "Jointly Learning to Repair Code and Generate Commit Message"], ["jiang2017automatically", "Automatically Generating Commit Messages from Diffs using Neural Machine Translation"], ["liu2018neural", "Neural-Machine-Translation-Based Commit Message Generation: How Far Are We?"]]
+[["jung2021commitbert", "CommitBERT: Commit Message Generation Using Pre-Trained Programming Language Model"], ["bai2021jointly", "Jointly Learning to Repair Code and Generate Commit Message"], ["jiang2017automatically", "Automatically Generating Commit Messages from Diffs using Neural Machine Translation"], ["xu2019commit", "Commit Message Generation for Source Code Changes"]]

publications-metadata/jayasundara2019treecaps.json

@@ -1 +1 @@
-[["johnson2020learning", "Learning Graph Structure With A Finite-State Automaton Layer"], ["hellendoorn2020global", "Global Relational Models of Source Code"], ["abdelaziz2020graph4code", "Graph4Code: A Machine Interpretable Knowledge Graph for Code"], ["jesse2021learning", "Learning Type Annotation: Is Big Data Enough?"]]
+[["johnson2020learning", "Learning Graph Structure With A Finite-State Automaton Layer"], ["hellendoorn2020global", "Global Relational Models of Source Code"], ["abdelaziz2020graph4code", "Graph4Code: A Machine Interpretable Knowledge Graph for Code"], ["nair2020funcgnn", "funcGNN: A Graph Neural Network Approach to Program Similarity"]]

publications-metadata/lee2021cotraining.json

@@ -1 +1 @@
-[["aye2020sequence", "Sequence Model Design for Code Completion in the Modern IDE"], ["allamanis2016convolutional", "A Convolutional Attention Network for Extreme Summarization of Source Code"], ["lu2022reacc", "ReACC: A Retrieval-Augmented Code Completion Framework"], ["li2021toward", "Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models"]]
+[["aye2020sequence", "Sequence Model Design for Code Completion in the Modern IDE"], ["lu2022reacc", "ReACC: A Retrieval-Augmented Code Completion Framework"], ["allamanis2016convolutional", "A Convolutional Attention Network for Extreme Summarization of Source Code"], ["li2021toward", "Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models"]]