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Michele Tufano
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2020 – today
- 2024
- [i25]Anisha Agarwal, Aaron Chan, Shubham Chandel, Jinu Jang, Shaun Miller, Roshanak Zilouchian Moghaddam, Yevhen Mohylevskyy, Neel Sundaresan, Michele Tufano:
Copilot Evaluation Harness: Evaluating LLM-Guided Software Programming. CoRR abs/2402.14261 (2024) - [i24]Michele Tufano, Anisha Agarwal, Jinu Jang, Roshanak Zilouchian Moghaddam, Neel Sundaresan:
AutoDev: Automated AI-Driven Development. CoRR abs/2403.08299 (2024) - 2023
- [c27]Matthew Jin
, Syed Shahriar
InferFix: End-to-End Program Repair with LLMs. ESEC/SIGSOFT FSE 2023: 1646-1656 - [i23]Kevin Moran, Ali Yachnes, George Purnell, Junayed Mahmud, Michele Tufano, Carlos Bernal-Cárdenas, Denys Poshyvanyk, Zach H'Doubler:
An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation. CoRR abs/2301.01224 (2023) - [i22]Matthew Jin, Syed Shahriar, Michele Tufano, Xin Shi, Shuai Lu, Neel Sundaresan, Alexey Svyatkovskiy:
InferFix: End-to-End Program Repair with LLMs. CoRR abs/2303.07263 (2023) - [i21]Michele Tufano, Shubham Chandel, Anisha Agarwal, Neel Sundaresan, Colin B. Clement:
Predicting Code Coverage without Execution. CoRR abs/2307.13383 (2023) - [i20]Benjamin Steenhoek, Michele Tufano, Neel Sundaresan, Alexey Svyatkovskiy:
Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation. CoRR abs/2310.02368 (2023) - 2022
- [j7]Camilo Escobar-Velásquez
Enabling Mutant Generation for Open- and Closed-Source Android Apps. IEEE Trans. Software Eng. 48(2): 186-208 (2022) - [c26]Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Neel Sundaresan:
Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers. AST@ICSE 2022: 54-64 - [c25]Michele Tufano, Shao Kun Deng, Neel Sundaresan, Alexey Svyatkovskiy:
METHODS2TEST: A dataset of focal methods mapped to test cases. MSR 2022: 299-303 - [c24]Andrei Zlotchevski, Dawn Drain, Alexey Svyatkovskiy, Colin B. Clement, Neel Sundaresan, Michele Tufano:
Exploring and evaluating personalized models for code generation. ESEC/SIGSOFT FSE 2022: 1500-1508 - [c23]Kevin Moran, Ali Yachnes, George Purnell, Junayed Mahmud, Michele Tufano, Carlos Bernal-Cárdenas, Denys Poshyvanyk
An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation. SANER 2022: 514-525 - [i19]Michele Tufano, Shao Kun Deng, Neel Sundaresan, Alexey Svyatkovskiy:
Methods2Test: A dataset of focal methods mapped to test cases. CoRR abs/2203.12776 (2022) - [i18]Andrei Zlotchevski, Dawn Drain, Alexey Svyatkovskiy, Colin B. Clement, Neel Sundaresan, Michele Tufano:
Exploring and Evaluating Personalized Models for Code Generation. CoRR abs/2208.13928 (2022) - 2021
- [j6]Zimin Chen
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair. IEEE Trans. Software Eng. 47(9): 1943-1959 (2021) - [c22]Colin B. Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, Alexey Svyatkovskiy:
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy. EMNLP (1) 2021: 4713-4722 - [c21]Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin B. Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, Ming Zhou:
GraphCodeBERT: Pre-training Code Representations with Data Flow. ICLR 2021 - [c20]Rosalia Tufano
Towards Automating Code Review Activities. ICSE 2021: 163-174 - [c19]Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin B. Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, Shujie Liu:
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation. NeurIPS Datasets and Benchmarks 2021 - [i17]Rosalia Tufano, Luca Pascarella, Michele Tufano, Denys Poshyvanyk, Gabriele Bavota:
Towards Automating Code Review Activities. CoRR abs/2101.02518 (2021) - [i16]Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin B. Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, Ming Gong, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, Shujie Liu:
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation. CoRR abs/2102.04664 (2021) - [i15]Colin B. Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, Alexey Svyatkovskiy:
Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy. CoRR abs/2109.08780 (2021) - 2020
- [c18]Michele Tufano, Jason Kimko, Shiya Wang, Cody Watson, Gabriele Bavota
DeepMutation: a neural mutation tool. ICSE (Companion Volume) 2020: 29-32 - [c17]Cody Watson, Michele Tufano, Kevin Moran, Gabriele Bavota
On learning meaningful assert statements for unit test cases. ICSE 2020: 1398-1409 - [i14]Michele Tufano, Jason Kimko, Shiya Wang, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Denys Poshyvanyk:
DeepMutation: A Neural Mutation Tool. CoRR abs/2002.04760 (2020) - [i13]Cody Watson, Michele Tufano, Kevin Moran, Gabriele Bavota, Denys Poshyvanyk:
On Learning Meaningful Assert Statements for Unit Test Cases. CoRR abs/2002.05800 (2020) - [i12]Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan:
Unit Test Case Generation with Transformers. CoRR abs/2009.05617 (2020) - [i11]Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Neel Sundaresan:
Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers. CoRR abs/2009.05634 (2020) - [i10]Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin B. Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, Ming Zhou:
GraphCodeBERT: Pre-training Code Representations with Data Flow. CoRR abs/2009.08366 (2020)
2010 – 2019
- 2019
- [j5]Michele Tufano
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation. ACM Trans. Softw. Eng. Methodol. 28(4): 19:1-19:29 (2019) - [c16]Michele Tufano, Jevgenija Pantiuchina, Cody Watson, Gabriele Bavota
On learning meaningful code changes via neural machine translation. ICSE 2019: 25-36 - [c15]Michele Tufano, Hitesh Sajnani, Kim Herzig:
Towards predicting the impact of software changes on building activities. ICSE (NIER) 2019: 49-52 - [c14]Carlos Bernal-Cárdenas, Kevin Moran, Michele Tufano, Zichang Liu, Linyong Nan, Zhehan Shi, Denys Poshyvanyk
Guigle: a GUI search engine for Android apps. ICSE (Companion Volume) 2019: 71-74 - [c13]Michele Tufano, Cody Watson, Gabriele Bavota
Learning How to Mutate Source Code from Bug-Fixes. ICSME 2019: 301-312 - [c12]Martin White, Michele Tufano, Matias Martinez
Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities. SANER 2019: 479-490 - [i9]Carlos Bernal-Cárdenas, Kevin Moran, Michele Tufano, Zichang Liu, Linyong Nan, Zhehan Shi, Denys Poshyvanyk:
Guigle: A GUI Search Engine for Android Apps. CoRR abs/1901.00891 (2019) - [i8]Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-Noël Pouchet, Denys Poshyvanyk, Martin Monperrus:
SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair. CoRR abs/1901.01808 (2019) - [i7]Michele Tufano, Hitesh Sajnani, Kim Herzig:
Towards Predicting the Impact of Software Changes on Building Activities. CoRR abs/1901.07142 (2019) - [i6]Michele Tufano, Jevgenija Pantiuchina, Cody Watson, Gabriele Bavota, Denys Poshyvanyk:
On Learning Meaningful Code Changes via Neural Machine Translation. CoRR abs/1901.09102 (2019) - 2018
- [c11]Kevin Moran, Michele Tufano, Carlos Bernal-Cárdenas, Mario Linares Vásquez, Gabriele Bavota
MDroid+: a mutation testing framework for android. ICSE (Companion Volume) 2018: 33-36 - [c10]Jevgenija Pantiuchina, Gabriele Bavota
Towards just-in-time refactoring recommenders. ICPC 2018: 312-315 - [c9]Michele Tufano, Cody Watson, Gabriele Bavota
An empirical investigation into learning bug-fixing patches in the wild via neural machine translation. ASE 2018: 832-837 - [c8]Michele Tufano, Cody Watson, Gabriele Bavota
Deep learning similarities from different representations of source code. MSR 2018: 542-553 - [c7]Mario Linares Vásquez, Christopher Vendome, Michele Tufano, Denys Poshyvanyk
How developers micro-optimize Android apps (journal-first abstract). SANER 2018: 470 - [i5]Kevin Moran, Michele Tufano, Carlos Bernal-Cárdenas, Mario Linares Vásquez, Gabriele Bavota, Christopher Vendome, Massimiliano Di Penta, Denys Poshyvanyk:
MDroid+: A Mutation Testing Framework for Android. CoRR abs/1802.04749 (2018) - [i4]Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk:
An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation. CoRR abs/1812.08693 (2018) - [i3]Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk:
Learning How to Mutate Source Code from Bug-Fixes. CoRR abs/1812.10772 (2018) - 2017
- [j4]Mario Linares Vásquez
How developers micro-optimize Android apps. J. Syst. Softw. 130: 1-23 (2017) - [j3]Michele Tufano, Gabriele Bavota
An empirical study on developer-related factors characterizing fix-inducing commits. J. Softw. Evol. Process. 29(1) (2017) - [j2]Michele Tufano, Fabio Palomba
There and back again: Can you compile that snapshot? J. Softw. Evol. Process. 29(4) (2017) - [j1]Michele Tufano
When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away). IEEE Trans. Software Eng. 43(11): 1063-1088 (2017) - [c6]Mario Linares Vásquez
Enabling mutation testing for Android apps. ESEC/SIGSOFT FSE 2017: 233-244 - [i2]Martin White, Michele Tufano, Matias Martinez, Martin Monperrus, Denys Poshyvanyk:
Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities. CoRR abs/1707.04742 (2017) - [i1]Mario Linares Vásquez, Gabriele Bavota, Michele Tufano, Kevin Moran, Massimiliano Di Penta, Christopher Vendome, Carlos Bernal-Cárdenas, Denys Poshyvanyk:
Enabling Mutation Testing for Android Apps. CoRR abs/1707.09038 (2017) - 2016
- [c5]Michele Tufano, Fabio Palomba
An empirical investigation into the nature of test smells. ASE 2016: 4-15 - [c4]Martin White, Michele Tufano, Christopher Vendome, Denys Poshyvanyk
Deep learning code fragments for code clone detection. ASE 2016: 87-98 - 2015
- [c3]Michele Tufano, Fabio Palomba
When and Why Your Code Starts to Smell Bad. ICSE (1) 2015: 403-414 - [c2]Fabio Palomba
Extract Package Refactoring in ARIES. ICSE (2) 2015: 669-672 - [c1]Fabio Palomba
Landfill: An Open Dataset of Code Smells with Public Evaluation. MSR 2015: 482-485
Coauthor Index
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last updated on 2024-09-18 00:17 CEST by the dblp team
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