deploy: 2c98afc

Author: mallamanis

paper-abstracts.json

@@ -1 +1 @@
-[["russell2018automated", "Automated Vulnerability Detection in Source Code Using Deep Representation Learning"], ["zhou2019devign", "Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks"], ["allamanis2017mining", "Mining Semantic Loop Idioms from Big Code"], ["chakraborty2020deep", "Deep Learning based Vulnerability Detection: Are We There Yet?"]]
+[["russell2018automated", "Automated Vulnerability Detection in Source Code Using Deep Representation Learning"], ["zhou2019devign", "Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks"], ["allamanis2017mining", "Mining Semantic Loop Idioms from Big Code"], ["kharkar2022learning", "Learning to Reduce False Positives in Analytic Bug Detectors"]]

papers.html

@@ -1 +1 @@
-[["husain2019codesearchnet", "CodeSearchNet Challenge: Evaluating the State of Semantic Code Search"], ["li2019neural", "Neural Code Search Evaluation Dataset"], ["chirkova2020empirical", "Empirical Study of Transformers for Source Code"], ["derezendemartins2020concra.md", "CoNCRA: A Convolutional Neural Network Code Retrieval Approach"]]
+[["husain2019codesearchnet", "CodeSearchNet Challenge: Evaluating the State of Semantic Code Search"], ["li2019neural", "Neural Code Search Evaluation Dataset"], ["nadeem2022codedsi", "CodeDSI: Differentiable Code Search"], ["chirkova2020empirical", "Empirical Study of Transformers for Source Code"]]

publications-metadata/ashwath2020predicting.json

@@ -1 +1 @@
-[["louis2020where", "Where should I comment my code? A dataset and model for predicting locations that need comments"], ["wang2016automatically", "Automatically Learning Semantic Features for Defect Prediction"], ["mou2014building", "Building Program Vector Representations for Deep Learning"], ["zhang2019learning", "Learning Uniform Semantic Features for Natural Language and Programming Language Globally, Locally and Sequentially"]]
+[["louis2020where", "Where should I comment my code? A dataset and model for predicting locations that need comments"], ["wang2016automatically", "Automatically Learning Semantic Features for Defect Prediction"], ["mou2014building", "Building Program Vector Representations for Deep Learning"], ["allamanis2018learning", "Learning to Represent Programs with Graphs"]]

publications-metadata/clement2021distilling.json

@@ -1 +1 @@
-[["heyman2020neural", "Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent"], ["gu2018deep", "Deep Code Search"], ["li2019neural", "Neural Code Search Evaluation Dataset"], ["zhang2021bag", "Bag-of-Words Baselines for Semantic Code Search"]]
+[["heyman2020neural", "Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent"], ["gu2018deep", "Deep Code Search"], ["li2019neural", "Neural Code Search Evaluation Dataset"], ["yao2018staqc", "StaQC: A Systematically Mined Question-Code Dataset from Stack Overflow"]]

publications-metadata/corley2015exploring.json

@@ -1 +1 @@
-[["tarlow2019learning", "Learning to Fix Build Errors with Graph2Diff Neural Networks"], ["panthaplackel2021learning", "Learning to Describe Solutions for Bug Reports Based on Developer Discussions"], ["spirin2021psiminer", "PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code"], ["bui2018cross", "Cross-Language Learning for Program Classification using Bilateral Tree-Based Convolutional Neural Networks"]]
+[["tarlow2019learning", "Learning to Fix Build Errors with Graph2Diff Neural Networks"], ["panthaplackel2021learning", "Learning to Describe Solutions for Bug Reports Based on Developer Discussions"], ["spirin2021psiminer", "PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code"], ["key2022speak", "I Speak, You Verify: Toward Trustworthy Neural Program Synthesis"]]

publications-metadata/derezendemartins2020concra.md.json

@@ -1 +1 @@
-[["bhoopchand2016learning", "Learning Python Code Suggestion with a Sparse Pointer Network"], ["nguyen2015graph", "Graph-based Statistical Language Model for Code"], ["bruch2009learning", "Learning from Examples to Improve Code Completion Systems"], ["svyatkovskiy2020fast", "Fast and Memory-Efficient Neural Code Completion"]]
+[["nguyen2015graph", "Graph-based Statistical Language Model for Code"], ["bhoopchand2016learning", "Learning Python Code Suggestion with a Sparse Pointer Network"], ["bruch2009learning", "Learning from Examples to Improve Code Completion Systems"], ["svyatkovskiy2020fast", "Fast and Memory-Efficient Neural Code Completion"]]

publications-metadata/dinella2021deepmerge.json

@@ -1 +1 @@
-[["karampatsis2019deep", "Maybe Deep Neural Networks are the Best Choice for Modeling Source Code"], ["markovtsev2017topic", "Topic modeling of public repositories at scale using names in source code"], ["hussain2019deep", "Deep Transfer Learning for Source Code Modeling"], ["aye2020sequence", "Sequence Model Design for Code Completion in the Modern IDE"]]
+[["karampatsis2019deep", "Maybe Deep Neural Networks are the Best Choice for Modeling Source Code"], ["markovtsev2017topic", "Topic modeling of public repositories at scale using names in source code"], ["hussain2019deep", "Deep Transfer Learning for Source Code Modeling"], ["karampatsis2020big", "Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code"]]

publications-metadata/franks2015cacheca.json

@@ -0,0 +1 @@
+[["nijkamp2022conversational", "A Conversational Paradigm for Program Synthesis"], ["raychev2016learning", "Learning Programs from Noisy Data"], ["murali2017bayesian", "Bayesian Sketch Learning for Program Synthesis"], ["wong2021leveraging", "Leveraging Language to Learn Program Abstractions and Search Heuristics"]]