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ESEC/FSE 2021
Thu 19 - Sat 28 August 2021 Clowdr Platform

Initially developed for natural language processing (NLP), Transformers are now widely used for source code processing, due to the format similarity between source code and text. In contrast to natural language, source code is strictly structured, i.e., it follows the syntax of the programming language. Several recent works develop Transformer modifications for capturing syntactic information in source code. The drawback of these works is that they do not compare to each other and consider different tasks. In this work, we conduct a thorough empirical study of the capabilities of Transformers to utilize syntactic information in different tasks. We consider three tasks (code completion, function naming and bug fixing) and re-implement different syntax-capturing modifications in a unified framework. We show that Transformers are able to make meaningful predictions based purely on syntactic information and underline the best practices of taking the syntactic information into account for improving the performance of the model.

Wed 25 Aug

Displayed time zone: Athens change

09:00 - 10:00
SE & AI—Machine Learning for Software Engineering 2Research Papers +12h
Chair(s): Michael Pradel University of Stuttgart, Saikat Chakraborty Columbia University
09:00
10m
Paper
Empirical Study of Transformers for Source Code
Research Papers
Nadezhda Chirkova HSE University, Sergey Troshin HSE University
DOI
09:10
10m
Paper
Explaining Mispredictions of Machine Learning Models using Rule Induction
Research Papers
Jürgen Cito TU Vienna; Facebook, Isil Dillig University of Texas at Austin, Seohyun Kim Facebook, Vijayaraghavan Murali Facebook, Satish Chandra Facebook
DOI
09:20
10m
Paper
Generalizable and Interpretable Learning for Configuration Extrapolation
Research Papers
Yi Ding Massachusetts Institute of Technology, Ahsan Pervaiz University of Chicago, Michael Carbin Massachusetts Institute of Technology, Henry Hoffmann University of Chicago
DOI
09:30
30m
Live Q&A
Q&A (SE & AI—Machine Learning for Software Engineering 2)
Research Papers

21:00 - 22:00
SE & AI—Machine Learning for Software Engineering 2Research Papers
Chair(s): Kelly Lyons University of Toronto, Phuong T. Nguyen University of L’Aquila
21:00
10m
Paper
Empirical Study of Transformers for Source Code
Research Papers
Nadezhda Chirkova HSE University, Sergey Troshin HSE University
DOI
21:10
10m
Paper
Explaining Mispredictions of Machine Learning Models using Rule Induction
Research Papers
Jürgen Cito TU Vienna; Facebook, Isil Dillig University of Texas at Austin, Seohyun Kim Facebook, Vijayaraghavan Murali Facebook, Satish Chandra Facebook
DOI
21:20
10m
Paper
Generalizable and Interpretable Learning for Configuration Extrapolation
Research Papers
Yi Ding Massachusetts Institute of Technology, Ahsan Pervaiz University of Chicago, Michael Carbin Massachusetts Institute of Technology, Henry Hoffmann University of Chicago
DOI
21:30
30m
Live Q&A
Q&A (SE & AI—Machine Learning for Software Engineering 2)
Research Papers