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

While machine learning (ML) models play an increasingly prevalent role in many software engineering tasks, their prediction accuracy is often problematic. When these models do mispredict, it can be very difficult to isolate the cause. In this paper, we propose a technique that aims to facilitate the debugging process of trained statistical models. Given an ML model and a labeled data set, our method produces an interpretable characterization of the data on which the model performs particularly poorly. The output of our technique can be useful for understanding limitations of the training data or the model itself; it can also be useful for ensembling if there are multiple models with different strengths. We evaluate our approach through case studies and illustrate how it can be used to improve the accuracy of predictive models used for software engineering tasks within Facebook.

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