Blogs (1) >>
ASE 2019
Sun 10 - Fri 15 November 2019 San Diego, California, United States
Wed 13 Nov 2019 11:00 - 11:20 at Cortez 2&3 - Program Repair Chair(s): Yingfei Xiong

Automated program repair has been used to provide feedback for incorrect student programming assignments, since program repair captures the code modification needed to make a given buggy program pass a given test-suite. Existing student feedback generation techniques are limited because they either require manual effort in the form of providing an error model, or require a large number of correct student submissions to learn from, or suffer from lack of scalability and accuracy.

In this work, we propose a fully automated approach for generating student program repairs in real-time. This is achieved by first re-factoring all available correct solutions to semantically equivalent solutions. Given an incorrect program, we match the program with the closest matching refactored program based on its control flow structure. Subsequently, we infer the input-output specifications of the incorrect program’s basic blocks from the executions of the correct program’s aligned basic blocks. Finally, these specifications are used to modify the blocks of the incorrect program via search-based synthesis.

Our dataset consists of almost 1,800 real-life incorrect Python program submissions from 361 students for an introductory programming course at a large public university. Our experimental results suggest that our method is more effective and efficient than recently proposed feedback generation approaches. About 30% of the patches produced by our tool Refactory are smaller than those produced by the state-of-art tool Clara, and can be produced given fewer correct solutions (often a single correct solution) and in a shorter time. We opine that our method is applicable not only to programming assignments, and could be seen as a general-purpose program repair method that can achieve good results with just a single correct reference solution.

Wed 13 Nov

10:40 - 12:20: Papers - Program Repair at Cortez 2&3
Chair(s): Yingfei XiongPeking University
ase-2019-papers10:40 - 11:00
Apricot: A Weight-Adaptation Approach to Fixing Deep Learning Models
Hao ZhangCity University of Hong Kong, Wing-Kwong ChanCity University of Hong Kong, Hong Kong
ase-2019-papers11:00 - 11:20
Re-factoring based Program Repair applied to Programming Assignments
Yang HuThe University of Texas at Austin, Umair Z. AhmedNational University of Singapore, Sergey MechtaevUniversity College London, Ben LeongNational University of Singapore, Abhik RoychoudhuryNational University of Singapore
ase-2019-papers11:20 - 11:40
InFix: Automatically Repairing Novice Program Inputs
Madeline EndresUniversity of Michigan, Georgios SakkasUniversity of California, San Diego, Benjamin CosmanUniversity of California at San Diego, USA, Ranjit JhalaUniversity of California, San Diego, Westley WeimerUniversity of Michigan
ase-2019-Journal-First-Presentations11:40 - 12:00
Astor: Exploring the Design Space of Generate-and-Validate Program Repair beyond GenProg
Matias MartinezUniversité Polytechnique Hauts-de-France, Martin MonperrusKTH Royal Institute of Technology
ase-2019-Demonstrations12:00 - 12:10
PraPR: Practical Program Repair via Bytecode Mutation
Ali GhanbariThe University of Texas at Dallas, Lingming ZhangThe University of Texas at Dallas
ase-2019-papers12:10 - 12:20
Understanding Automatically-Generated Patches Through Symbolic Invariant Differences
Padraic CashinArizona State University, Cari MartinezUniversity of New Mexico, Stephanie ForrestArizona State University, Westley WeimerUniversity of Michigan