Re-factoring based Program Repair applied to Programming Assignments
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 Times are displayed in time zone: Tijuana, Baja California change
|10:40 - 11:00|
|Apricot: A Weight-Adaptation Approach to Fixing Deep Learning Models|
|11:00 - 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 SingaporePre-print
|11:20 - 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 MichiganPre-print
|11:40 - 12:00|
|Astor: Exploring the Design Space of Generate-and-Validate Program Repair beyond GenProg|
Journal First Presentations
Matias MartinezUniversité Polytechnique Hauts-de-France, Martin MonperrusKTH Royal Institute of TechnologyPre-print
|12:00 - 12:10|
|PraPR: Practical Program Repair via Bytecode Mutation|
|12:10 - 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 MichiganPre-print