Compilation Error Repair: For the Student Programs, From the Student Programs
By Umair Z. Ahmed, Pawan Kumar, Amey Karkare, Purushottam Kar, Sumit Gulwani. 2018
100 citations recorded by the source corpus.
Subfields: Computer Science Applications, Information Systems, Software, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Science Applications
- Shirish K. Shevade
- Rahul Gupta
- Aditya Kanade
- Percy Liang
- Michihiro Yasunaga
- Denys Poshyvanyk
- Sumit Gulwani
- Charles Sutton
In Information Systems
- Percy Liang
- Michihiro Yasunaga
- Charles Sutton
- Michele Tufano
- Miltiadis Allamanis
- Daniel Tarlow
- Denys Poshyvanyk
- Rishabh Singh
In Software
- Percy Liang
- Michihiro Yasunaga
- Rishabh Singh
- Daniel Tarlow
- Charles Sutton
- Aditya Kanade
- Michele Tufano
- Shirish K. Shevade
In Natural Language Processing
- Percy Liang
- Michihiro Yasunaga
- Charles Sutton
- Daniel Tarlow
- Miltiadis Allamanis
- Luke S. Zettlemoyer
- Rishabh Singh
- Michele Tufano
Citations in the corpus
- Break-It-Fix-It: Unsupervised Learning for Program Repair (2021, methodology): “, dropping tokens) and prepare a synthetic paired data 〈perturbed code, good code〉 to train a fixer (Gupta et al., 2017; Ahmed et al., 2018; Yasunaga & Liang, 2020).”
- Graph-based, Self-Supervised Program Repair from Diagnostic Feedback (2020, background): “There is increasing interest in automatic correction of introductory programming assignments (Pu et al., 2016; Parihar et al., 2017; Ahmed et al., 2018).”
- SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair (2018, methodology): “TRACER is another program repair tool for fixing compiler errors which outperforms DeepFix in terms of success rate [24].”
- Learning to Fix Build Errors with Graph2Diff Neural Networks (2019, background): “xed by multiple passes. The input is the whole program, and variables are renamed to reduce the vocabulary. Then, the model takes the input and predicts the line number along with the bug fix. TRACER [1] followed the same idea and improved upon DeepFix. The most significant changes compared to DeepF”
- Repairing Bugs in Python Assignments Using Large Language Models (2022, background): “Prior automated program repair systems for student programming assignments have generally been implemented using purely symbolic [9]–[12] or purely neural [8], [13] techniques.”
- Neural Attribution for Semantic Bug-Localization in Student Programs (2019, background): “Several recent works proposed deep learning based techniques for automated syntactic error repair in student programs [11, 6, 3, 13].”