Deep Learning for Code: a Collection of Papers December, 2021

In this post, I try to list every ML/DL paper that targets code understanding, code representation, and bug finding. Despite my efforts to compile an exhaustive list, there are definitely ones that I have missed. Please email me if you find a paper that is missing. I will keep this post up-to-date as I continue my studies.

Every top-level category consists of the papers that were published in a specific year. Under some of the paper descriptions, I have added a few keywords about the ideas and tasks.

2021

Language-agnostic representation learning of source code from structure and context
Zugner, Daniel and Kirschstein, Tobias and Catasta, Michele and Leskovec, Jure and Gunnemann, Stephan
arXiv preprint arXiv:2103.11318
ICLR
```
{model: , tasks: , representation: , highlights: }
```
Evaluating large language models trained on code
Chen, Mark and Tworek, Jerry and Jun, Heewoo and Yuan, Qiming and Ponde, Henrique and Kaplan, Jared and Edwards, Harri and Burda, Yura and Joseph, Nicholas and Brockman, Greg and others
arXiv preprint arXiv:2107.03374
More with less: Exploring how to use deep learning effectively through semi-supervised learning for automatic bug detection in student code.
Shi, Yang and Mao, Ye and Barnes, Tiffany and Chi, Min and Price, Thomas W
In Proceedings of the 14th International Conference on Educational Data Mining (EDM)
Fast and memory-efficient neural code completion
Svyatkovskiy, Alexey and Lee, Sebastian and Hadjitofi, Anna and Riechert, Maik and Franco, Juliana Vicente and Allamanis, Miltiadis
2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR)
```
{model: , tasks: code completion, representation: embeds subtokens, highlights: }
```

CCMC: Code Completion with a Memory Mechanism and a Copy Mechanism
Yang, Hao and Kuang, Li
Evaluation and Assessment in Software Engineering

{model: transformer-XL, tasks: , representation: sequence of AST nodes in in-order DFS fashion, highlights: long-range dependencies but consumes a lot of memory and compute resources}

Studying the usage of text-to-text transfer transformer to support code-related tasks
Mastropaolo, Antonio and Scalabrino, Simone and Cooper, Nathan and Palacio, David Nader and Poshyvanyk, Denys and Oliveto, Rocco and Bavota, Gabriele
2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

InferCode: Self-Supervised Learning of Code Representations by Predicting Subtrees
Bui, Nghi DQ and Yu, Yijun and Jiang, Lingxiao
2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

{model: Tree-based CNN, tasks: , representation: subtrees of an AST--traverses AST and for specific nodes such as stmts extracts subtree rooted at the visited node then makes a vocab of subtrees-->so the model's job would be to predict subtrees given an AST, highlights: unsupervised so helps with scarcity of labeled data, pretraining}

PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code
Spirin, Egor and Bogomolov, Egor and Kovalenko, Vladimir and Bryksin, Timofey
arXiv preprint arXiv:2103.12778
A Survey on Software Defect Prediction Using Deep Learning
Akimova, Elena N and Bersenev, Alexander Yu and Deikov, Artem A and Kobylkin, Konstantin S and Konygin, Anton V and Mezentsev, Ilya P and Misilov, Vladimir E
Multidisciplinary Digital Publishing Institute
A large-scale benchmark for few-shot program induction and synthesis
Alet, Ferran and Lopez-Contreras, Javier and Koppel, James and Nye, Maxwell and Solar-Lezama, Armando and Lozano-Perez, Tomas and Kaelbling, Leslie and Tenenbaum, Joshua
International Conference on Machine Learning
On the Effectiveness of Deep Vulnerability Detectors to Simple Stupid Bug Detection
Hua, Jiayi and Wang, Haoyu
2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR)
BERT2Code: Can Pretrained Language Models be Leveraged for Code Search?
Ishtiaq, Abdullah Al and Hasan, Masum and Haque, Md and Anjum, Mahim and Mehrab, Kazi Sajeed and Muttaqueen, Tanveer and Hasan, Tahmid and Iqbal, Anindya and Shahriyar, Rifat
arXiv preprint arXiv:2104.08017
```
{model: code2vec/codebert to embed source code and a simple NN with 2 hidden layers to embed NL query, tasks: , representation: , highlights: learns a mapping between NL embeddings and code embeddings}
```
On the generalizability of Neural Program Models with respect to semantic-preserving program transformations
Rabin, Md Rafiqul Islam and Bui, Nghi DQ and Wang, Ke and Yu, Yijun and Jiang, Lingxiao and Alipour, Mohammad Amin
Information and Software Technology
```
{model: , tasks: , representation: , highlights: robustness study}
```
Do Transformers Really Perform Bad for Graph Representation?
Ying, Chengxuan and Cai, Tianle and Luo, Shengjie and Zheng, Shuxin and Ke, Guolin and He, Di and Shen, Yanming and Liu, Tie-Yan
arXiv preprint arXiv:2106.05234
```
{model: transformer for graphs, tasks: , representation: , highlights: they call it Graphormer}
```
TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer
Berabi, Berkay and He, Jingxuan and Raychev, Veselin and Vechev, Martin
International Conference on Machine Learning (PMLR)
```
{model: transformer, tasks: fixing code errors, representation: sequence of tokens, features: }
```
Generating Adversarial Computer Programs using Optimized Obfuscations
Srikant, Shashank and Liu, Sijia and Mitrovska, Tamara and Chang, Shiyu and Fan, Quanfu and Zhang, Gaoyuan and O'Reilly, Una-May
arXiv preprint arXiv:2103.11882
```
{model: , tasks: , representation: , highlights: focuses on adversarial robustness}
```

Self-Supervised Bug Detection and Repair
Allamanis, Miltiadis and Jackson-Flux, Henry and Brockschmidt, Marc
arXiv preprint arXiv:2105.12787

{model: GNN, tasks: , representation: AST augmented with control and dataflow edges, features: defines the graph as entities and relations, works better than CuBERT and GREAT on real bugs, self-supervised.}

How could Neural Networks understand Programs?
Peng, Dinglan and Zheng, Shuxin and Li, Yatao and Ke, Guolin and He, Di and Liu, Tie-Yan
arXiv preprint arXiv:2105.04297
```
{model: transformer, tasks: , representation: control flow of LLVM IR, highlights: }
```

Code prediction by feeding trees to transformers
Kim, Seohyun and Zhao, Jinman and Tian, Yuchi and Chandra, Satish
2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

{model: transformer, tasks: , representation: 1) token sequence or 2) AST node sequence in pre-order fashion or 3) root-to-leaf paths, highlights: makes the transformer aware of the syntactic structure of code i.e. improves self attention block similar to GREAT}

CLSEBERT: Contrastive Learning for Syntax Enhanced Code Pre-Trained Model
Wang, Xin and Wang, Yasheng and Zhou, Pingyi and Xiao, Meng and Wang, Yadao and Li, Li and Liu, Xiao and Wu, Hao and Liu, Jin and Jiang, Xi
arXiv preprint arXiv:2108.04556
```
{model: , tasks: , representation: AST as sequence, highlights: pretraining, noise invariant code representation using contrastive learning by introducing noise into input sequence at training time, focus on robustness}
```
CoTexT: Multi-task Learning with Code-Text Transformer
Phan, Long and Tran, Hieu and Le, Daniel and Nguyen, Hieu and Anibal, James and Peltekian, Alec and Ye, Yanfang
arXiv preprint arXiv:2105.08645
```
{model: transformer, tasks: , representation: sequence of tokens, highlights: focuses on NL-PL tasks, pretraining}
```
A Mocktail of Source Code Representations
Vagavolu, Dheeraj and Swarna, Karthik Chandra and Chimalakonda, Sridhar
arXiv preprint arXiv:2106.10918
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{model: , tasks: , representation: AST+CFG+PDG, highlights: an extension of code2vec}
```
Program Synthesis with Large Language Models
Austin, Jacob and Odena, Augustus and Nye, Maxwell and Bosma, Maarten and Michalewski, Henryk and Dohan, David and Jiang, Ellen and Cai, Carrie and Terry, Michael and Le, Quoc and Sutton, Charles
arXiv preprint arXiv:2108.07732
Automatic Code Generation using Pre-Trained Language Models
Ottens, Lizi and Perez, Luis and Viswanathan, Sudharshan
arXiv preprint arXiv:2102.10535
SySeVR: A framework for using deep learning to detect software vulnerabilities
Li, Zhen and Zou, Deqing and Xu, Shouhuai and Jin, Hai and Zhu, Yawei and Chen, Zhaoxuan
IEEE Transactions on Dependable and Secure Computing

2020

Structural language models of code
Alon, Uri and Sadaka, Roy and Levy, Omer and Yahav, Eran
International Conference on Machine Learning
```
{model: , tasks: code generation, representation: paths from the root and leaves in AST, features: copy mechanism}
```
DL-Droid: Deep learning based android malware detection using real devices
Alzaylaee, Mohammed K and Yerima, Suleiman Y and Sezer, Sakir
Computers & Security, Elsevier
```
{model: , tasks: malware detection, representation: , features: hand-engineered and heuristic-based}
```
DRAST--A Deep Learning and AST Based Approach for Bug Localization
Sangle, Shubham and Muvva, Sandeep and Chimalakonda, Sridhar and Ponnalagu, Karthikeyan and Venkoparao, Vijendran Gopalan
arXiv preprint arXiv:2011.03449
```
{model: , tasks: , representation: AST, highlights: }
```
Backdoors in neural models of source code
Ramakrishnan, Goutham and Albarghouthi, Aws
arXiv preprint arXiv:2006.06841
```
{model: , tasks: , representation: , highlights: adversarial robustness}
```
Adversarial examples for models of code
Yefet, Noam and Alon, Uri and Yahav, Eran
Proceedings of the ACM on Programming Languages (OOPSLA)
```
{model: , tasks: , representation: , highlights: adversarial robustness}
```
Semantic robustness of models of source code
Ramakrishnan, Goutham and Henkel, Jordan and Wang, Zi and Albarghouthi, Aws and Jha, Somesh and Reps, Thomas
arXiv preprint arXiv:2002.03043
```
{model: , tasks: , representation: , highlights: focuses on semantic robustness and training with semantic-preserving code transformations}
```
Software vulnerability detection using deep neural networks: A survey
Lin, Guanjun and Wen, Sheng and Han, Qing-Long and Zhang, Jun and Xiang, Yang
Proceedings of the IEEE
Approaches for Representing Software as Graphs for Machine Learning Applications
Romanov, Vitaly and Ivanov, Vladimir and Succi, Giancarlo
2020 International Computer Symposium (ICS)
TranS^3: A transformer-based framework for unifying code summarization and code search
Wang, Wenhua and Zhang, Yuqun and Zeng, Zhengran and Xu, Guandong
arXiv preprint arXiv:2003.03238
```
{model: transformer, tasks: code search and summarization, representation: AST, highlights: unifying framework for both code searching and summarization}
```
Multi-task learning based pre-trained language model for code completion
Liu, Fang and Li, Ge and Zhao, Yunfei and Jin, Zhi
Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering
```
{model: transformer, tasks: code completion, representation: , highlights: pretraining}
```

Language Modelling for Source Code with Transformer-XL
Dowdell, Thomas and Zhang, Hongyu
arXiv preprint arXiv:2007.15813

{model: transformer-XL, tasks: , representation: , highlights: language modeling for source code, increase context size}

Big code != big vocabulary: Open-vocabulary models for source code
Karampatsis, Rafael-Michael and Babii, Hlib and Robbes, Romain and Sutton, Charles and Janes, Andrea
2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE)
Scelmo: Source code embeddings from language models
Karampatsis, Rafael-Michael and Sutton, Charles
arXiv preprint arXiv:2004.13214
DeepVS: an efficient and generic approach for source code modelling usage
Hussain, Yasir and Huang, Zhiqiu and Zhou, Yu and Wang, Senzhang
Electronics Letters, Wiley Online Library
Dlfix: Context-based code transformation learning for automated program repair
Li, Yi and Wang, Shaohua and Nguyen, Tien N
Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering
```
{model: tree-based LSTM, tasks: , representation: AST, highlights: learning transformations to fix code instead of seq2seq}
```
Compiler-based graph representations for deep learning models of code
Brauckmann, Alexander and Goens, Andr{
'es and Ertel, Sebastian and Castrillon, Jeronimo
Proceedings of the 29th International Conference on Compiler Construction
```
{model: GNN, tasks: , representation: AST and control flow edges, highlights: }
```
Deep learning for source code modeling and generation: Models, applications, and challenges
Le, Triet HM and Chen, Hao and Babar, Muhammad Ali
ACM Computing Surveys (CSUR)
Duplicate bug report detection and classification system based on deep learning technique
Kukkar, Ashima and Mohana, Rajni and Kumar, Yugal and Nayyar, Anand and Bilal, Muhammad and Kwak, Kyung-Sup
IEEE Access
A self-attentional neural architecture for code completion with multi-task learning
Liu, Fang and Li, Ge and Wei, Bolin and Xia, Xin and Fu, Zhiyi and Jin, Zhi
Proceedings of the 28th International Conference on Program Comprehension
```
{model: , tasks: code completion, representation: AST nodes as an ordered sequences to root, highlights: }
```
A transformer-based approach for source code summarization
Ahmad, Wasi Uddin and Chakraborty, Saikat and Ray, Baishakhi and Chang, Kai-Wei
arXiv preprint arXiv:2005.00653
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{model: transformer, tasks: code summarization, representation: pairwise relationships between tokens based on AST, features: long-range}
```
Software defect prediction via transformer
Zhang, Qihang and Wu, Bin
2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)
```
{model: transformer, tasks: , representation: AST, highlights: }
```
Adversarial robustness for code
Bielik, Pavol and Vechev, Martin
International Conference on Machine Learning (PMLR)
```
{model: , tasks: , representation: , highlights: focus on adversarial robustness of code}
```
Modular tree network for source code representation learning
Wang, Wenhan and Li, Ge and Shen, Sijie and Xia, Xin and Jin, Zhi
ACM Transactions on Software Engineering and Methodology (TOSEM)
```
{model: tree-LSTM, tasks: , representation: AST, highlights: it is a modular tree network extracted from AST}
```
IntelliCode Compose: Code generation using transformer
Svyatkovskiy, Alexey and Deng, Shao Kun and Fu, Shengyu and Sundaresan, Neel
Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
```
{model: transformer, tasks: code generation, representation: , highlights: they call the model GPT-C}
```
Automated vulnerability detection in source code using minimum intermediate representation learning
Li, Xin and Wang, Lu and Xin, Yang and Yang, Yixian and Chen, Yuling
Applied Sciences, Multidisciplinary Digital Publishing Institute
Hoppity: Learning graph transformations to detect and fix bugs in programs
Dinella, Elizabeth and Dai, Hanjun and Li, Ziyang and Naik, Mayur and Song, Le and Wang, Ke
International Conference on Learning Representations (ICLR)
```
{model: GNN, tasks: fixing bugs, representation: AST with subtoken cache, highlights: }
```
CodeBERT: A pre-trained model for programming and natural languages
Feng, Zhangyin and Guo, Daya and Tang, Duyu and Duan, Nan and Feng, Xiaocheng and Gong, Ming and Shou, Linjun and Qin, Bing and Liu, Ting and Jiang, Daxin and others
arXiv preprint arXiv:2002.08155
```
{model: transformer, tasks: code search, representation: , highlights: bimodal pretrained model for PL/NL}
```
GraphcodeBERT: Pre-training code representations with data flow
Guo, Daya and Ren, Shuo and Lu, Shuai and Feng, Zhangyin and Tang, Duyu and Liu, Shujie and Zhou, Long and Duan, Nan and Svyatkovskiy, Alexey and Fu, Shengyu and others
arXiv preprint arXiv:2009.08366
```
{model: transformer, tasks: code refinement, representation: , highlights: pretraining, similar to codebert but uses dataflow info at pretraining}
```

2019

Synthetic datasets for neural program synthesis
Shin, Richard and Kant, Neel and Gupta, Kavi and Bender, Christopher and Trabucco, Brandon and Singh, Rishabh and Song, Dawn
arXiv preprint arXiv:1912.12345
2019
PathMiner: a library for mining of path-based representations of code
Kovalenko, Vladimir and Bogomolov, Egor and Bryksin, Timofey and Bacchelli, Alberto
2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR)
2019
A hybrid deep learning image-based analysis for effective malware detection
Venkatraman, Sitalakshmi and Alazab, Mamoun and Vinayakumar, R
Journal of Information Security and Applications, Elsevier
2019
Pre-trained language model representations for language generation
Edunov, Sergey and Baevski, Alexei and Auli, Michael
arXiv preprint arXiv:1903.09722
2019
Multi-modal attention network learning for semantic source code retrieval
Wan, Yao and Shu, Jingdong and Sui, Yulei and Xu, Guandong and Zhao, Zhou and Wu, Jian and Yu, Philip S
arXiv preprint arXiv:1909.13516
2019
A zero-positive learning approach for diagnosing software performance regressions
Alam, Mejbah and Gottschlich, Justin and Tatbul, Nesime and Turek, Javier S and Mattson, Tim and Muzahid, Abdullah
Advances in Neural Information Processing Systems
2019
code2vec: Learning distributed representations of code
Alon, Uri and Zilberstein, Meital and Levy, Omer and Yahav, Eran
Proceedings of the ACM on Programming Languages
```
{model: , tasks: , representation: pairwise paths between AST terminal nodes, highlights: }
```
Deep-autocoder: Learning to complete code precisely with induced code tokens
Hu, Xing and Men, Rui and Li, Ge and Jin, Zhi
2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)
```
{model: LSTM, tasks: , representation: AST, highlights: learn language models over code corpus}
```
Pythia: AI-assisted code completion system
Svyatkovskiy, Alexey and Zhao, Ying and Fu, Shengyu and Sundaresan, Neel
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
```
{model: LSTM, tasks: code completion, representation: serialized AST, highlights: }
```
Maybe deep neural networks are the best choice for modeling source code
Karampatsis, Rafael-Michael and Sutton, Charles
arXiv preprint arXiv:1903.05734
Structural language models for any-code generation
Alon, Uri and Sadaka, Roy and Levy, Omer and Yahav, Eran
A novel neural source code representation based on abstract syntax tree
Zhang, Jian and Wang, Xu and Zhang, Hongyu and Sun, Hailong and Wang, Kaixuan and Liu, Xudong
2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)
```
{model: RNN, tasks: , representation: AST split into a sequence of small stmt-level subtrees, highlights: }
```
IdBench: Evaluating Semantic Representations of Identifier Names in Source Code
Wainakh, Yaza and Rauf, Moiz and Pradel, Michael
arXiv preprint arXiv:1910.05177
Neural program repair by jointly learning to localize and repair
Vasic, Marko and Kanade, Aditya and Maniatis, Petros and Bieber, David and Singh, Rishabh
arXiv preprint arXiv:1904.01720
Open vocabulary learning on source code with a graph-structured cache
Cvitkovic, Milan and Singh, Badal and Anandkumar, Animashree
International Conference on Machine Learning (PMLR)
```
{model: GNN, tasks: , representation: augmented AST, highlights: add a graph-structural vocabulary cache to the graph--add edges from a subtoken vocab to terminal nodes}
```
A literature study of embeddings on source code
Chen, Zimin and Monperrus, Martin
arXiv preprint arXiv:1904.03061
Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks
Zhou, Yaqin and Liu, Shangqing and Siow, Jingkai and Du, Xiaoning and Liu, Yang
arXiv preprint arXiv:1909.03496
```
{model: GNN, tasks: , representation: AST, highlights: }
```

Global Relational Models of Source Code
Hellendoorn, Vincent J and Sutton, Charles and Singh, Rishabh and Maniatis, Petros and Bieber, David
International conference on learning representations

{model: transformer with attention bias, tasks: varmisuse, representation: sequence of tokens but incorporating semantically meaningful relations, highlights: longer-range dependencies compared to transformer but still limited by context-size}

Learning a static bug finder from data
Wang, Yu and Gao, Fengjuan and Wang, Linzhang and Wang, Ke
arXiv preprint arXiv:1907.05579

{model: GNN, tasks: , representation: augmented AST, highlights: split the code graph into multiple disjoint ones, suitable for more complex bugs such as null pointer deref, less accurate when handling large programs (large = 1000 nodes)}

Deep learning for bug-localization in student programs
Gupta, Rahul and Kanade, Aditya and Shevade, Shirish
arXiv preprint arXiv:1905.12454
```
{model: tree convolutional neural network, tasks: bug localization, representation: AST, highlights: }
```
A comprehensive study on deep learning bug characteristics
Islam, Md Johirul and Nguyen, Giang and Pan, Rangeet and Rajan, Hridesh
Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

2018

Deepbugs: A learning approach to name-based bug detection
Pradel, Michael and Sen, Koushik
Proceedings of the ACM on Programming Languages (OOPSLA)
```
{model: , tasks: , representation: embed only program identifiers in a list, highlights: }
```
code2seq: Generating sequences from structured representations of code
Alon, Uri and Brody, Shaked and Levy, Omer and Yahav, Eran
arXiv preprint arXiv:1808.01400
```
{model: , tasks: code captioning, representation: all pairwise paths between terminal nodes in AST, features: }
```

2017

Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning
Wang, Lisa and Sy, Angela and Liu, Larry and Piech, Chris
International Educational Data Mining Society
Pallas: Semantic-aware checking for finding deep bugs in fast path
Huang, Jian and Allen-Bond, Michael and Zhang, Xuechen
Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems
Learning to represent programs with graphs
Allamanis, Miltiadis and Brockschmidt, Marc and Khademi, Mahmoud
arXiv preprint arXiv:1711.00740
```
{model: GGNN, tasks: varmisuse, representation: AST augmented with control and dataflow, features: }
```
DeepFix: Fixing common C language errors by deep learning
Gupta, Rahul and Pal, Soham and Kanade, Aditya and Shevade, Shirish
Thirty-First AAAI Conference on Artificial Intelligence
```
{model: multi-layered seq2seq neural network with attention, tasks: bug finding and fixing, representation: token sequence, highlights: }
```
Inductive representation learning on large graphs
Hamilton, William L and Ying, Rex and Leskovec, Jure
Proceedings of the 31st International Conference on Neural Information Processing Systems
Code completion with neural attention and pointer networks
Li, Jian and Wang, Yue and Lyu, Michael R and King, Irwin
arXiv preprint arXiv:1711.09573
```
{model: , tasks: , representation: flattened AST, highlights: }
```
Program synthesis from natural language using recurrent neural networks
Lin, Xi Victoria and Wang, Chenglong and Pang, Deric and Vu, Kevin and Ernst, Michael D
University of Washington Department of Computer Science and Engineering, Seattle, WA, USA, Tech. Rep. UW-CSE-17-03-01
```
{model: RNN encoder-decoder, tasks: program synthesis from NL, representation: , highlights: generates a program template from NL sentence}
```

2016

Program synthesis using natural language
Desai, Aditya and Gulwani, Sumit and Hingorani, Vineet and Jain, Nidhi and Karkare, Amey and Marron, Mark and Roy, Subhajit
Proceedings of the 38th International Conference on Software Engineering
Neuro-symbolic program synthesis
Parisotto, Emilio and Mohamed, Abdel-rahman and Singh, Rishabh and Li, Lihong and Zhou, Dengyong and Kohli, Pushmeet
arXiv preprint arXiv:1611.01855
Summarizing source code using a neural attention model
Iyer, Srinivasan and Konstas, Ioannis and Cheung, Alvin and Zettlemoyer, Luke
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

Mantis: Automatic performance prediction for smartphone applications
Kwon, Yongin and Lee, Sangmin and Yi, Hayoon and Kwon, Donghyun and Yang, Seungjun and Chun, Byung-Gon and Huang, Ling and Maniatis, Petros and Naik, Mayur and Paek, Yunheung
USENIX Annual Technical Conference 13