Remove redundant and error-prone bibkey.

Remove outdated taxonomies.

Author: mallamanis

_includes/sidebar.html

@@ -17,8 +17,6 @@ <h1>
    <a class="sidebar-nav-item{% if page.url == "/tsne-viz.html" %} active{% endif %}" href="{% link tsne-viz.html %}">2D Map of Papers</a>
    <a class="sidebar-nav-item{% if page.url == "/topic-viz.html" %} active{% endif %}" href="{% link topic-viz.html %}">Topic-based Explorer</a>
 
-   <a class="sidebar-nav-item{% if page.url == "/base-taxonomy/" %} active{% endif %}" href="{% link base-taxonomy/index.md %}">Core Taxonomy</a>
-
 
   <a class="sidebar-nav-item{% if page.url == "/resources.html" %} active{% endif %}" href="{% link resources.md %}">Resources, Courses &#38; Events</a>
   <a class="sidebar-nav-item{% if page.url == "/contributing.html" %} active{% endif %}" href="{% link contributing.markdown %}">Contributing</a>

_layouts/publication.html

@@ -11,7 +11,6 @@ <h5>{{ page.authors }}. {{ page.conference }} {{ page.year }}</h5>
     {% endfor %}
     &nbsp;<a href='http://scholar.google.com/scholar?q={{ page.title }}' target="_blank"><img  style="display: inline; margin: 0;" src="/public/media/google-scholar.png"/></a>
     &nbsp;<a href='https://www.semanticscholar.org/search?q={{ page.title }}' target="_blank"><img style="display: inline; margin: 0;" src="/public/media/semscholar.png"/></a>
-    &nbsp;<a href='http://academic.microsoft.com/#/search?iq={{ page.title | uri_escape }}' target="_blank"><img style="display: inline; margin: 0;" src="/public/media/ms-academic.png"/></a>
     <br/>
     {% for tag in page.tags %}
       <tag><a href="/tags.html#{{ tag }}">{{ tag }}</a></tag>
@@ -29,7 +28,7 @@ <h6>Similar Work</h6>
   <script>  
     $(document).ready(
       function() {
-        $.getJSON('/publications-metadata/{{ page.bibkey }}.json', function(data) {
+        $.getJSON("/publications-metadata/{{ page.path | replace_first: '_publications/', '' | replace: '.markdown', '' }}.json", function(data) {
           num_papers = data.length;
           html = "";
           for (let i=0; i < num_papers; i++) {

_publications/abdelaziz2020graph4code.markdown

@@ -4,7 +4,6 @@ title: "Graph4Code: A Machine Interpretable Knowledge Graph for Code"
 authors: Ibrahim Abdelaziz, Julian Dolby, James P. McCusker, Kavitha Srinivas
 conference:
 year: 2020
-bibkey: abdelaziz2020graph4code
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/2002.09440"}
    - {name: "Website", url: "https://wala.github.io/graph4code/"}

_publications/agashe2019julce.markdown

@@ -4,7 +4,6 @@ title: "JuICe: A Large Scale Distantly Supervised Dataset for Open Domain Contex
 authors: Rajas Agashe, Srinivasan Iyer, Luke Zettlemoyer
 conference:
 year: 2019
-bibkey: agashe2019julce
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/1910.02216"}
    - {name: "Dataset", url: "https://drive.google.com/file/d/1xWDV__5hjTWVuJlXD42Ar7nkjU2hRTic/view?usp=sharing"}

_publications/aggarwal2015using.markdown

@@ -4,7 +4,6 @@ title: "Using Machine Translation for Converting Python 2 to Python 3 Code"
 authors: Karan Aggarwal, Mohammad Salameh, Abram Hindle
 conference: 
 year: 2015
-bibkey: aggarwal2015using
 tags: ["migration"]
 ---
 In this paper, we have tried to use Statistical machine translation in order to convert Python 2 code to Python 3 code. We use data from two projects and achieve a high BLEU score. We also investigate the cross-project training and testing to analyze the errors so as to ascertain differences with previous case. We have described a pilot study on modeling programming languages as natural language to build translation models on the lines of natural languages. This can be further worked on to translate between versions of a programming language or cross-programming-languages code translation.

_publications/ahmad2020transformer.markdown

@@ -4,7 +4,6 @@ title: "A Transformer-based Approach for Source Code Summarization"
 authors: Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
 conference: ACL
 year: 2020
-bibkey: ahmad2020transformer
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/2005.00653"}
    - {name: "Code", url: "https://github.com/wasiahmad/NeuralCodeSum"}

_publications/ahmad2021unified.markdown

@@ -4,7 +4,6 @@ title: "Unified Pre-training for Program Understanding and Generation"
 authors: Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
 conference: NAACL
 year: 2021
-bibkey: ahmad2021unified
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/2103.06333"}
 tags: ["pretraining", "Transformer"]

_publications/ahmed2019learning.markdown

@@ -4,7 +4,6 @@ title: "Learning Lenient Parsing & Typing via Indirect Supervision"
 authors: Toufique Ahmed, Vincent Hellendoorn, Premkumar Devanbu
 conference:
 year: 2019
-bibkey: ahmed2019learning
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/1910.05879"}
 tags: ["types"]

_publications/alet2021largescale.markdown

@@ -4,7 +4,6 @@ title: A large-scale benchmark for few-shot program induction and synthesis
 authors: Ferran Alet, Javier Lopez-Contreras,  James Koppel,  Maxwell Nye,   Armando Solar-Lezama,  Tomas Lozano-Perez,  Leslie Kaelbling, Joshua Tenenbaum
 conference: ICML
 year: 2021
-bibkey: alet2021largescale
 additional_links:
    - {name: "PMLR", url: "http://proceedings.mlr.press/v139/alet21a.html"}
    - {name: "website", url: "https://lis.csail.mit.edu/progres"}

_publications/allamanis2013mining.markdown

@@ -4,7 +4,6 @@ title: "Mining Source Code Repositories at Massive Scale Using Language Modeling
 authors: Miltiadis Allamanis, Charles Sutton
 conference: MSR
 year: 2013
-bibkey: allamanis2013mining
 additional_links:
    - {name: "PDF", url: "http://homepages.inf.ed.ac.uk/csutton/publications/msr2013.pdf"}
    - {name: "data", url: "http://groups.inf.ed.ac.uk/cup/javaGithub/"}

_publications/allamanis2014learning.markdown

@@ -4,7 +4,6 @@ title: Learning Natural Coding Conventions
 authors: Miltiadis Allamanis, Earl T. Barr, Christian Bird, Charles Sutton
 conference: FSE
 year: 2014
-bibkey: allamanis2014learning
 additional_links:
    - {name: "PDF", url: "http://homepages.inf.ed.ac.uk/csutton/publications/naturalize.pdf"}
    - {name: "ArXiV", url: "http://arxiv.org/abs/1402.4182"}

_publications/allamanis2014mining.markdown

@@ -4,7 +4,6 @@ title: "Mining Idioms from Source Code"
 authors: Miltiadis Allamanis, Charles Sutton
 conference: FSE
 year: 2014
-bibkey: allamanis2014mining
 additional_links:
    - {name: "PDF", url: "http://homepages.inf.ed.ac.uk/csutton/publications/idioms.pdf"}
    - {name: "ArXiV", url: "http://arxiv.org/abs/1404.0417"}

_publications/allamanis2015bimodal.markdown

@@ -4,7 +4,6 @@ title: A Bimodal Modelling of Source Code and Natural Language
 authors: Miltiadis Allamanis, Daniel Tarlow, Andrew Gordon, Yi Wei
 conference: ICML
 year: 2015
-bibkey: allamanis2015bimodal
 additional_links:
    - {name: "Supplementary Material", url: "https://miltos.allamanis.com/publicationfiles/allamanis2015bimodal/supplementary.pdf"}
    - {name: "Presentation Video", url: "http://videolectures.net/icml2015_allamanis_natural_language/"}

_publications/allamanis2015suggesting.markdown

@@ -4,7 +4,6 @@ title: Suggesting Accurate Method and Class Names
 authors: Miltiadis Allamanis, Earl T. Barr, Christian Bird, Charles Sutton
 conference: FSE
 year: 2015
-bibkey: allamanis2015suggesting
 additional_links:
    - {name: "PDF", url: "http://homepages.inf.ed.ac.uk/csutton/publications/accurate-method-and-class.pdf"}
    - {name: "website", url: "http://groups.inf.ed.ac.uk/cup/naturalize"}

_publications/allamanis2016convolutional.markdown

@@ -4,7 +4,6 @@ title: A Convolutional Attention Network for Extreme Summarization of Source Cod
 authors: Miltiadis Allamanis, Hao Peng, Charles Sutton
 conference: ICML
 year: 2016
-bibkey: allamanis2016convolutional
 additional_links:
    - {name: "website", url: "http://groups.inf.ed.ac.uk/cup/codeattention/"}
    - {name: "code", url: "https://github.com/mast-group/convolutional-attention"}

_publications/allamanis2017mining.markdown

@@ -4,7 +4,6 @@ title: Mining Semantic Loop Idioms from Big Code
 authors: Miltiadis Allamanis, Earl T. Barr, Christian Bird, Mark Marron, Charles Sutton
 conference: "TSE"
 year: 2017
-bibkey: allamanis2017mining
 additional_links:
    - {name: "MSR Technical Report", url: "https://www.microsoft.com/en-us/research/publication/mining-semantic-loop-idioms-big-code/"}
    - {name: "website", url: "http://groups.inf.ed.ac.uk/cup/semantic-idioms/"}

_publications/allamanis2017smartpaste.markdown

@@ -4,7 +4,6 @@ title: "SmartPaste: Learning to Adapt Source Code"
 authors: Miltiadis Allamanis, Marc Brockschmidt
 conference: ""
 year: 2017
-bibkey: allamanis2017smartpaste
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/1705.07867"}
 tags: ["representation", "variable misuse"]

_publications/allamanis2018learning.markdown

@@ -4,7 +4,6 @@ title: Learning to Represent Programs with Graphs
 authors: Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi
 conference: "ICLR"
 year: 2018
-bibkey: allamanis2018learning
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/1711.00740"}
    - {name: "GGNN Code", url: "https://github.com/Microsoft/gated-graph-neural-network-samples"}

_publications/allamanis2019adverse.markdown

@@ -4,7 +4,6 @@ title: "The Adverse Effects of Code Duplication in Machine Learning Models of Co
 authors: Miltiadis Allamanis
 conference:
 year: 2019
-bibkey: allamanis2019adverse
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/1812.06469"}
    - {name: "Dataset Errata", url: "https://dpupublicdata.blob.core.windows.net/duplicates/errata.zip"}

_publications/allamanis2020typilus.markdown

@@ -4,7 +4,6 @@ title: "Typilus: Neural Type Hints"
 authors: Miltiadis Allamanis, Earl T. Barr, Soline Ducousso, Zheng Gao
 conference: PLDI
 year: 2020
-bibkey: allamanis2020typilus
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/2004.10657"}
    - {name: "Dataset", url: "https://github.com/typilus/typilus"}

_publications/allamanis2021self.markdown

@@ -4,7 +4,6 @@ title: "Self-Supervised Bug Detection and Repair"
 authors: Miltiadis Allamanis, Henry Jackson-Flux, Marc Brockschmidt
 conference: NeurIPS
 year: 2021
-bibkey: allamanis2021self
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/2105.12787"}
 tags: ["GNN", "Transformer", "defect", "repair"]

_publications/alon2018code2seq.markdown

@@ -4,7 +4,6 @@ title: "code2seq: Generating Sequences from Structured Representations of Code"
 authors: Uri Alon, Omer Levy, Eran Yahav
 conference: ICLR
 year: 2019
-bibkey: alon2018code2seq
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/1808.01400"}
 tags: ["naming", "summarization", "representation"]

_publications/alon2018general.markdown

@@ -4,7 +4,6 @@ title: "A General Path-Based Representation for Predicting Program Properties"
 authors: Uri Alon, Meital Zilberstein, Omer Levy, Eran Yahav
 conference: PLDI
 year: 2018
-bibkey: alon2018general
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/1803.09544"}
 tags: ["naming", "representation"]

_publications/alon2019code2vec.markdown

@@ -4,7 +4,6 @@ title: "code2vec: Learning Distributed Representations of Code"
 authors: Uri Alon, Omer Levy, Eran Yahav
 conference: POPL
 year: 2019
-bibkey: alon2019code2vec
 additional_links:
    - {name: "Code", url: "https://github.com/tech-srl/code2vec"}
 tags: ["naming", "summarization", "representation"]

_publications/alon2019structural.markdown

@@ -4,7 +4,6 @@ title: "Structural Language Models for Any-Code Generation"
 authors: Uri Alon, Roy Sadaka, Omer Levy, Eran Yahav
 conference:
 year: 2019
-bibkey: alond2019structural
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/1910.00577"}
 tags: ["code generation"]

_publications/amodio2017neural.markdown

@@ -4,7 +4,6 @@ title: "Neural Attribute Machines for Program Generation"
 authors: Matthew Amodio, Swarat Chaudhuri, Thomas W. Reps
 conference: 
 year: 2017
-bibkey: amodio2017neural
 tags: ["grammar", "code generation", "representation"]
 ---
 Recurrent neural networks have achieved remarkable success at generating sequences with complex structures, thanks to advances that include richer embeddings of input and cures for vanishing gradients. Trained only on sequences from a known grammar, though, they can still struggle to learn rules and constraints of the grammar. Neural Attribute Machines (NAMs) are equipped with a logical machine that represents the underlying grammar, which is used to teach the constraints to the neural machine by (i) augmenting the input sequence, and (ii) optimizing a custom loss function. Unlike traditional RNNs, NAMs are exposed to the grammar, as well as samples from the language of the grammar. During generation, NAMs make significantly fewer violations of the constraints of the underlying grammar than RNNs trained only on samples from the language of the grammar.

_publications/arakelyan2020towards.markdown

@@ -4,7 +4,6 @@ title: "Towards Learning Representations of Binary Executable Files for Security
 authors: Shushan Arakelyan, Sima Arasteh, Christophe Hauser, Erik Kline, Aram Galstyan
 conference: AAAI
 year: 2020
-bibkey: arakelyan2020towards
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/2002.03388"}
 tags: ["GNN", "representation"]

_publications/ashwath2020predicting.markdown

@@ -4,7 +4,6 @@ title: Predicting Vulnerability in Large Codebases With Deep Code Representation
 authors: Anshul Tanwar, Krishna Sundaresan, Parmesh Ashwath, Prasanna Ganesan, Sathish Kumar Chandrasekaran, Sriram Ravi 
 conference: 
 year: 2020
-bibkey: ashwath2020predicting
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/2004.12783"}
 tags: ["grammar", "program analysis", "static analysis"]

_publications/aye2020learning.markdown

@@ -4,7 +4,6 @@ title: "Learning Autocompletion from Real-World Datasets"
 authors: Gareth Ari Aye, Seohyun Kim, Hongyu Li
 conference: 
 year: 2020
-bibkey: aye2020learning
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/2011.04542"}
 tags: ["autocomplete"]

_publications/aye2020sequence.markdown

@@ -4,7 +4,6 @@ title: "Sequence Model Design for Code Completion in the Modern IDE"
 authors: Gareth Ari Aye, Gail E. Kaiser
 conference: Optional
 year: 2020
-bibkey: aye2020sequence
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/2004.05249"}
 tags: ["autocomplete"]

_publications/bai2021jointly.markdown

@@ -4,7 +4,6 @@ title: "Jointly Learning to Repair Code and Generate Commit Message"
 authors: Jiaqi Bai, Long Zhou, Ambrosio Blanco, Shujie Liu, Furu Wei, Ming Zhou, Zhoujun Li
 conference: 
 year: 2021
-bibkey: bai2021jointly
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/2109.12296"}
 tags: ["edit", "Transformer"]

_publications/barone2017parallel.markdown

@@ -4,7 +4,6 @@ title: "A parallel corpus of Python functions and documentation strings for auto
 authors: Antonio Valerio Miceli Barone, Rico Sennrich
 conference:
 year: 2017
-bibkey: barone2017parallel
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/1707.02275"}
    - {name: "code", url: "https://github.com/EdinburghNLP/code-docstring-corpus"}

_publications/bavishi2017context2name.markdown

@@ -4,7 +4,6 @@ title: "Context2Name: A Deep Learning-Based Approach to Infer Natural Variable N
 authors: Rohan Bavishi, Michael Pradel, Koushik Sen
 conference: 
 year: 2017
-bibkey: bavishi2017context2name
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/1809.05193"}
 tags: ["naming"]

_publications/bavishi2019autopandas.markdown

@@ -4,7 +4,6 @@ title: "AutoPandas: neural-backed generators for program synthesis"
 authors: Rohan Bavishi, Caroline Lemieux, Roy Fox, Koushik Sen, Ion Stoica
 conference: OOPSLA
 year: 2019
-bibkey: bavishi2019autopandas
 tags: ["synthesis", "GNN", "API"]
 ---
 Developers nowadays have to contend with a growing number of APIs. While in the long-term they are very useful to developers, many modern APIs have an incredibly steep learning curve, due to their hundreds of functions handling many arguments, obscure documentation, and frequently changing semantics. For APIs that perform data transformations, novices can often provide an I/O example demonstrating the desired transformation, but may be stuck on how to translate it to the API. A programming-by-example synthesis engine that takes such I/O examples and directly produces programs in the target API could help such novices. Such an engine presents unique challenges due to the breadth of real-world APIs, and the often-complex constraints over function arguments. We present a generator-based synthesis approach to contend with these problems. This approach uses a program candidate generator, which encodes basic constraints on the space of programs. We introduce neural-backed operators which can be seamlessly integrated into the program generator. To improve the efficiency of the search, we simply use these operators at non-deterministic decision points, instead of relying on domain-specific heuristics. We implement this technique for the Python pandas library in AutoPandas. AutoPandas supports 119 pandas dataframe transformation functions. We evaluate AutoPandas on 26 real-world benchmarks and find it solves 17 of them.

_publications/beltramelli2017pix2code.markdown

@@ -4,7 +4,6 @@ title: "pix2code: Generating Code from a Graphical User Interface Screenshot"
 authors: Tony Beltramelli
 conference:
 year: 2017
-bibkey: beltramelli2017pix2code
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/1705.07962"}
 tags: ["code generation", "bimodal"]

_publications/bennun2018neural.markdown

@@ -4,7 +4,6 @@ title: "Neural Code Comprehension: A Learnable Representation of Code Semantics"
 authors: Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler
 conference: NeurIPS
 year: 2018
-bibkey: bennun2018neural
 tags: ["representation"]
 ---
 With the recent success of embeddings in natural language processing, research has been conducted into applying similar methods to code analysis. Most works attempt to process the code directly or use a syntactic tree representation, treating it like sentences written in a natural language. However, none of the existing methods are sufficient to comprehend program semantics robustly, due to structural features such as function calls, branching, and interchangeable order of statements. In this paper, we propose a novel processing technique to learn code semantics, and apply it to a variety of program analysis tasks. In particular, we stipulate that a robust distributional hypothesis of code applies to both human- and machine-generated programs. Following this hypothesis, we define an embedding space, inst2vec, based on an Intermediate Representation (IR) of the code that is independent of the source programming language. We provide a novel definition of contextual flow for this IR, leveraging both the underlying data- and control-flow of the program. We then analyze the embeddings qualitatively using analogies and clustering, and evaluate the learned representation on three different high-level tasks. We show that with a single RNN architecture and pre-trained fixed embeddings, inst2vec outperforms specialized approaches for performance prediction (compute device mapping, optimal thread coarsening); and algorithm classification from raw code (104 classes), where we set a new state-of-the-art.

_publications/berabi2021tfix.markdown

@@ -4,7 +4,6 @@ title: "TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer"
 authors: Berkay Berabi, Jingxuan He, Veselin Raychev, Martin Vechev
 conference: ICML
 year: 2021
-bibkey: berabi2021tfix
 additional_links:
    - {name: "Code & Dataset", url: "https://github.com/eth-sri/TFix"}
 tags: ["repair"]

_publications/bhatia2016automated.markdown

@@ -4,7 +4,6 @@ title: "Automated Correction for Syntax Errors in Programming Assignments using
 authors: Sahil Bhatia, Rishabh Singh
 conference: 
 year: 2016
-bibkey: bhatia2016automated
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/1603.06129"}
 tags: ["repair"]

_publications/bhatia2018neurosymbolic.markdown

@@ -4,7 +4,6 @@ title: "Neuro-symbolic program corrector for introductory programming assignment
 authors: Sahil Bhatia, Pushmeet Kohli, Rishabh Singh
 conference: ICSE
 year: 2018
-bibkey: bhatia2018neurosymbolic
 tags: ["repair"]
 ---
 Automatic correction of programs is a challenging problem with numerous real world applications in security, verification, and education. One application that is becoming increasingly important is the correction of student submissions in online courses for providing feedback. Most existing program repair techniques analyze Abstract Syntax Trees (ASTs) of programs, which are unfortunately unavailable for programs with syntax errors. In this paper, we propose a novel Neuro-symbolic approach that combines neural networks with constraint-based reasoning. Specifically, our method first uses a Recurrent Neural Network (RNN) to perform syntax repairs for the buggy programs; subsequently, the resulting syntactically-fixed programs are repaired using constraint-based techniques to ensure functional correctness. The RNNs are trained using a corpus of syntactically correct submissions for a given programming assignment, and are then queried to fix syntax errors in an incorrect programming submission by replacing or inserting the predicted tokens at the error location. We evaluate our technique on a dataset comprising of over 14,500 student submissions with syntax errors. Our method is able to repair syntax errors in 60% (8689) of submissions, and finds functionally correct repairs for 23.8% (3455) submissions.