Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Wed 13 Nov 2019 10:40 - 11:00 at Cortez 2&3 - Program Repair Chair(s): Yingfei Xiong

A deep learning (DL) model is inherently imprecise. To fix this problem, existing techniques retrain a given DL model over a larger training dataset or with the help of fault injected models or using the insight of failing test cases in the given DL model. In this paper, we present Apricot, a novel weight-adaptation approach to fixing DL models iteratively. Our key observation is that if the deep learning architecture of a DL model is trained over a subset of the original training dataset, the weights in the resultant reduced DL model (rDLM) can provide insights on the adjustment direction and magnitude of the weights in the original DL model to handle the test cases that the original DL model misclassify. Apricot generates a set of such reduced DL models from the original DL model. In each iteration, for each weight of the input DL model (iDLM) of that iteration, Apricot adjusts the weight of this iDLM toward the average weight of these rDLMs correctly classifying the failing test cases experienced by this iDLM and/or away from these rDLMs misclassifying the same failing test cases, followed by training the weight-adjusted iDLM over the original training dataset to generate a new iDLM for the next iteration. The experiment using five state-of-the-art DLMs shows that Apricot can increase the accuracy of these DL models by 0.35%-2.81% with an average of 1.45%. The experiment also reveals the complementary nature of these rDLMs in Apricot.

Wed 13 Nov

Displayed time zone: Tijuana, Baja California change

10:40 - 12:20
Program RepairResearch Papers / Demonstrations / Journal First Presentations at Cortez 2&3
Chair(s): Yingfei Xiong Peking University
10:40
20m
Talk
Apricot: A Weight-Adaptation Approach to Fixing Deep Learning Models
Research Papers
Hao Zhang City University of Hong Kong, Wing-Kwong Chan City University of Hong Kong, Hong Kong
11:00
20m
Talk
Re-factoring based Program Repair applied to Programming Assignments
Research Papers
Yang Hu The University of Texas at Austin, Umair Z. Ahmed National University of Singapore, Sergey Mechtaev University College London, Ben Leong National University of Singapore, Abhik Roychoudhury National University of Singapore
Pre-print
11:20
20m
Talk
InFix: Automatically Repairing Novice Program Inputs
Research Papers
Madeline Endres University of Michigan, Georgios Sakkas University of California, San Diego, Benjamin Cosman University of California at San Diego, USA, Ranjit Jhala University of California, San Diego, Westley Weimer University of Michigan
Pre-print
11:40
20m
Talk
Astor: Exploring the Design Space of Generate-and-Validate Program Repair beyond GenProg
Journal First Presentations
Matias Martinez Université Polytechnique Hauts-de-France, Martin Monperrus KTH Royal Institute of Technology
Pre-print
12:00
10m
Demonstration
PraPR: Practical Program Repair via Bytecode Mutation
Demonstrations
Ali Ghanbari The University of Texas at Dallas, Lingming Zhang The University of Texas at Dallas
12:10
10m
Talk
Understanding Automatically-Generated Patches Through Symbolic Invariant Differences
Research Papers
Padraic Cashin Arizona State University, Cari Martinez University of New Mexico, Stephanie Forrest Arizona State University, Westley Weimer University of Michigan
Pre-print