Search Results for author: Michele Tufano

Found 13 papers, 6 papers with code

Predicting Code Coverage without Execution

1 code implementation25 Jul 2023 Michele Tufano, Shubham Chandel, Anisha Agarwal, Neel Sundaresan, Colin Clement

Using Machine Learning to amortize this expensive process could lower the cost of code coverage by requiring only the source code context, and the task of code coverage prediction can be a novel benchmark for judging the ability of models to understand code.

An Empirical Investigation into the Use of Image Captioning for Automated Software Documentation

no code implementations3 Jan 2023 Kevin Moran, Ali Yachnes, George Purnell, Junayed Mahmud, Michele Tufano, Carlos Bernal-Cárdenas, Denys Poshyvanyk, Zach H'Doubler

This paper offers one of the first comprehensive empirical investigations into the connection between GUIs and functional, natural language descriptions of software.

Image Captioning Machine Translation

Exploring and Evaluating Personalized Models for Code Generation

no code implementations29 Aug 2022 Andrei Zlotchevski, Dawn Drain, Alexey Svyatkovskiy, Colin Clement, Neel Sundaresan, Michele Tufano

Large Transformer models achieved the state-of-the-art status for Natural Language Understanding tasks and are increasingly becoming the baseline model architecture for modeling source code.

Code Generation Natural Language Understanding +1

Long-Range Modeling of Source Code Files with eWASH: Extended Window Access by Syntax Hierarchy

no code implementations EMNLP 2021 Colin B. Clement, Shuai Lu, Xiaoyu Liu, Michele Tufano, Dawn Drain, Nan Duan, Neel Sundaresan, Alexey Svyatkovskiy

While there are many efforts to extend the context window, we introduce an architecture-independent approach for leveraging the syntactic hierarchies of source code for incorporating entire file-level context into a fixed-length window.

Code Completion Code Generation +3

GraphCodeBERT: Pre-training Code Representations with Data Flow

1 code implementation ICLR 2021 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 Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, Ming Zhou

Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "where-the-value-comes-from" between variables.

Clone Detection Code Completion +7

Unit Test Case Generation with Transformers and Focal Context

1 code implementation11 Sep 2020 Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Shao Kun Deng, Neel Sundaresan

We execute the test cases, collect test coverage information, and compare them with test cases generated by EvoSuite and GPT-3, finding that our approach outperforms GPT-3 and has comparable coverage w. r. t.


Generating Accurate Assert Statements for Unit Test Cases using Pretrained Transformers

no code implementations11 Sep 2020 Michele Tufano, Dawn Drain, Alexey Svyatkovskiy, Neel Sundaresan

In this paper we present an approach to support developers in writing unit test cases by generating accurate and useful assert statements.

DeepMutation: A Neural Mutation Tool

no code implementations12 Feb 2020 Michele Tufano, Jason Kimko, Shiya Wang, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Denys Poshyvanyk

To this aim, two characteristics of mutation testing frameworks are of paramount importance: (i) they should generate mutants that are representative of real faults; and (ii) they should provide a complete tool chain able to automatically generate, inject, and test the mutants.

Fault Detection

On Learning Meaningful Code Changes via Neural Machine Translation

no code implementations25 Jan 2019 Michele Tufano, Jevgenija Pantiuchina, Cody Watson, Gabriele Bavota, Denys Poshyvanyk

We show that, when applied in a narrow enough context (i. e., small/medium-sized pairs of methods before/after the pull request changes), NMT can automatically replicate the changes implemented by developers during pull requests in up to 36% of the cases.

Machine Translation NMT +1

Learning How to Mutate Source Code from Bug-Fixes

no code implementations27 Dec 2018 Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk

Starting from code fixed by developers in the context of a bug-fix, our empirical evaluation showed that our models are able to predict mutants that resemble original fixed bugs in between 9% and 45% of the cases (depending on the model).

Software Engineering

Sorting and Transforming Program Repair Ingredients via Deep Learning Code Similarities

1 code implementation15 Jul 2017 Martin White, Michele Tufano, Matias Martinez, Martin Monperrus, Denys Poshyvanyk

We aim to reason about the repair ingredients by using code similarities to prioritize and transform statements in a codebase for patch generation.

Software Engineering

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