deploy: c6c7c03

Author: mallamanis

index.html

@@ -149,14 +149,20 @@ <h4 id="-browse-papers-by-tag">🏷 Browse Papers by Tag</h4>
 <tag><a href="/tags.html#information extraction">information extraction</a></tag>
 <tag><a href="/tags.html#interpretability">interpretability</a></tag>
 <tag><a href="/tags.html#language model">language model</a></tag>
+<tag><a href="/tags.html#large language models">large language models</a></tag>
 <tag><a href="/tags.html#logging">logging</a></tag>
 <tag><a href="/tags.html#memorization">memorization</a></tag>
+<tag><a href="/tags.html#metrics">metrics</a></tag>
 <tag><a href="/tags.html#migration">migration</a></tag>
 <tag><a href="/tags.html#naming">naming</a></tag>
+<tag><a href="/tags.html#natural language generation">natural language generation</a></tag>
+<tag><a href="/tags.html#natural language processing">natural language processing</a></tag>
 <tag><a href="/tags.html#optimization">optimization</a></tag>
 <tag><a href="/tags.html#pattern mining">pattern mining</a></tag>
 <tag><a href="/tags.html#pretraining">pretraining</a></tag>
 <tag><a href="/tags.html#program analysis">program analysis</a></tag>
+<tag><a href="/tags.html#program synthesis">program synthesis</a></tag>
+<tag><a href="/tags.html#question answering">question answering</a></tag>
 <tag><a href="/tags.html#refactoring">refactoring</a></tag>
 <tag><a href="/tags.html#repair">repair</a></tag>
 <tag><a href="/tags.html#representation">representation</a></tag>

paper-abstracts.json

@@ -1 +1 @@
-[["shrivastava2020repository", "Repository-Level Prompt Generation for Large Language Models of Code"], ["ye2020leveraging", "Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning"], ["hindle2012naturalness", "On the Naturalness of Software"], ["bui2021efficient", "Self-Supervised Contrastive Learning for Code Retrieval and Summarization via Semantic-Preserving Transformations"]]
+[["shrivastava2020repository", "Repository-Level Prompt Generation for Large Language Models of Code"], ["ye2020leveraging", "Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning"], ["lomshakov2023fine", "Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets"], ["hindle2012naturalness", "On the Naturalness of Software"]]

papers.html

@@ -1 +1 @@
-[["dinella2022toga", "TOGA: A Neural Method for Test Oracle Generation"], ["tufano2020unit", "Unit Test Case Generation with Transformers"], ["pradel2020neural", "Neural Software Analysis"], ["tufano2018learning", "Learning How to Mutate Source Code from Bug-Fixes"]]
+[["dinella2022toga", "TOGA: A Neural Method for Test Oracle Generation"], ["tufano2020unit", "Unit Test Case Generation with Transformers"], ["pradel2020neural", "Neural Software Analysis"], ["kovalchuk2023test", "Test-based and metric-based evaluation of code generation models for practical question answering"]]

publications-metadata/ahmed2033improving.json

@@ -1 +1 @@
-[["nguyen2014statistical", "Statistical Learning Approach for Mining API Usage Mappings for Code Migration"], ["nguyen2016mapping", "Mapping API Elements for Code Migration with Vector Representations"], ["chen2019mining", "Mining Likely Analogical APIs across Third-Party Libraries via Large-Scale Unsupervised API Semantics Embedding"], ["wong2021leveraging", "Leveraging Language to Learn Program Abstractions and Search Heuristics"]]
+[["nguyen2014statistical", "Statistical Learning Approach for Mining API Usage Mappings for Code Migration"], ["nguyen2016mapping", "Mapping API Elements for Code Migration with Vector Representations"], ["chen2019mining", "Mining Likely Analogical APIs across Third-Party Libraries via Large-Scale Unsupervised API Semantics Embedding"], ["gu2016deep", "Deep API Learning"]]

publications-metadata/bareiss2022code.json

@@ -1 +1 @@
-[["cerulo2013hidden", "A Hidden Markov Model to Detect Coded Information Islands in Free Text"], ["kushman2013using", "Using Semantic Unification to Generate Regular Expressions from Natural Language"], ["gvero2015synthesizing", "Synthesizing Java expressions from free-form queries"], ["abdelaziz2020graph4code", "Graph4Code: A Machine Interpretable Knowledge Graph for Code"]]
+[["cerulo2013hidden", "A Hidden Markov Model to Detect Coded Information Islands in Free Text"], ["kushman2013using", "Using Semantic Unification to Generate Regular Expressions from Natural Language"], ["gvero2015synthesizing", "Synthesizing Java expressions from free-form queries"], ["richardson2018polyglot", "Polyglot Semantic Parsing in APIs"]]

publications-metadata/bavishi2019autopandas.json

@@ -1 +1 @@
-[["lee2021cotraining", "Co-Training for Commit Classification"], ["jain2020contrastive", "Contrastive Code Representation Learning"], ["shi2022cv4code", "CV4Code: Sourcecode Understanding via Visual Code Representations"], ["liu2018neural", "Neural-Machine-Translation-Based Commit Message Generation: How Far Are We?"]]
+[["lee2021cotraining", "Co-Training for Commit Classification"], ["jain2020contrastive", "Contrastive Code Representation Learning"], ["shi2022cv4code", "CV4Code: Sourcecode Understanding via Visual Code Representations"], ["hoang2020cc2vec", "CC2Vec: Distributed Representations of Code Changes"]]

publications-metadata/cerulo2015irish.json

@@ -1 +1 @@
-[["sahu2022learning", "Learning to Answer Semantic Queries over Code"], ["ling2020deep", "Deep Graph Matching and Searching for Semantic Code Retrieval"], ["liu2019neural", "Neural query expansion for code search"], ["yan2020are", "Are the Code Snippets What We Are Searching for? A Benchmark and an Empirical Study on Code Search with Natural-Language Queries"]]
+[["sahu2022learning", "Learning to Answer Semantic Queries over Code"], ["lomshakov2023fine", "Fine-Tuning Large Language Models for Answering Programming Questions with Code Snippets"], ["ling2020deep", "Deep Graph Matching and Searching for Semantic Code Retrieval"], ["liu2019neural", "Neural query expansion for code search"]]

publications-metadata/commit2vec2019lozoya.json

@@ -1 +1 @@
-[["reid2022learning", "Learning to Model Editing Processes"], ["patil2022exploring", "Exploring Dimensions of Generalizability and Few-shot Transfer for Text-to-SQL Semantic Parsing"], ["elnaggar2021codetrans", "CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing"], ["bui2021infercode", "InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees"]]
+[["reid2022learning", "Learning to Model Editing Processes"], ["patil2022exploring", "Exploring Dimensions of Generalizability and Few-shot Transfer for Text-to-SQL Semantic Parsing"], ["elnaggar2021codetrans", "CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing"], ["hellendoorn2017deep", "Are Deep Neural Networks the Best Choice for Modeling Source Code?"]]