sk_p: a neural program corrector for MOOCs

Pu, Yewen; Narasimhan, Karthik; Solar-Lezama, Armando; Barzilay, Regina

doi:10.1145/2984043.2989222

Cited by 100 publications

(73 citation statements)

References 12 publications

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“…sk_p is a program repair technique for syntactic and semantic errors in student programs submitted to MOOCs [43]. First, it uses the previous and next statement to predict the statement in the middle, i.e., to replace the current statement.…”

Section: Related Workmentioning

confidence: 99%

SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair

Chen

Kommrusch

Tufano

et al. 2021

IIEEE Trans. Software Eng.

237

357

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This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a technique, called SEQUENCER, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary problem that occurs with big code. Our system is data-driven; we train it on 35,578 samples, carefully curated from commits to open-source repositories. We evaluate SEQUENCER on 4,711 independent real bug fixes, as well on the Defects4J benchmark used in program repair research. SEQUENCER is able to perfectly predict the fixed line for 950/4,711 testing samples, and find correct patches for 14 bugs in Defects4J benchmark. SEQUENCER captures a wide range of repair operators without any domain-specific top-down design.Index Terms-program repair; machine learning. ! Zimin Chen is currently a PhD student at KTH Royal Institute of Technology. He also received the BS and MS degree in computer science from KTH. His research interest lies in the intersection between machine learning and software engineering, especially between automatic program repair and machine learning.Steve Kommrusch is currently a PhD candidate focused on machine learning at Colorado State University. He received his BS in computer engineering from University of Illinois in 1987 and his MS in EECS from MIT in 1989. From 1989 through 2017, he worked in industry at Hewlett-Packard, National Semiconductor, and Advanced Micro Devices. Steve holds over 30 patents in the fields of computer graphics algorithms, silicon simulation and debug techniques, and silicon performance and power management. His research interests include Program Equivalence, Program Repair, and Constructivist AI using machine learning. Electrical and Computer Engineering department. He is working on patternspecific languages and compilers for scientific computing, and has designed numerous approaches using optimizing compilation to effectively map applications to CPUs, GPUs, FPGAs and System-on-Chips. His work spans a variety of domains including compiler optimization design especially in the polyhedral compilation framework, high-level synthesis for FPGAs and SoCs, and distributed computing. Previously

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Section: Related Workmentioning

confidence: 99%

SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair

Chen

Kommrusch

Tufano

et al. 2021

IIEEE Trans. Software Eng.

237

357

View full text Add to dashboard Cite

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“…Instead, Prophet [48] is a learning-based approach that uses explicitly designed code features to rank candidate repairs. Other approaches train on correct solutions (from student programs) to specific programming tasks and try to learn task-specific repair strategies [8,66]. This goal has been achieved successfully in contexts such as in massively open online courses (MOOC), where the programs are generally small and synthetic [27].…”

Section: Program Repair and The Redundancy Assumptionmentioning

confidence: 99%

An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

Tufano

Watson

Bavota

et al. 2019

ACM Trans. Softw. Eng. Methodol.

271

320

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Millions of open-source projects with numerous bug fixes are available in code repositories. This proliferation of software development histories can be leveraged to learn how to fix common programming bugs. To explore such a potential, we perform an empirical study to assess the feasibility of using Neural Machine Translation techniques for learning bug-fixing patches for real defects. First, we mine millions of bug-fixes from the change histories of projects hosted on GitHub, in order to extract meaningful examples of such bug-fixes. Next, we abstract the buggy and corresponding fixed code, and use them to train an Encoder-Decoder model able to translate buggy code into its fixed version. In our empirical investigation we found that such a model is able to fix thousands of unique buggy methods in the wild. Overall, this model is capable of predicting fixed patches generated by developers in 9-50% of the cases, depending on the number of candidate patches we allow it to generate. Also, the model is able to emulate a variety of different Abstract Syntax Tree operations and generate candidate patches in a split second.

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“…Data-Driven Production Deployment AutoGrader [23] ✗ ✓ ✗ ✗ ✗ CLARA [10] ✓ ✗ ✓ ✓ ✗ QLOSE [3] ✗ ✗ ✗ ✗ ✗ sk_p [21] ✓ ✗ ✓ ✓ ✗ REFAZER [22] ✓ ✗ ✗ ✓ ✗ CoderAssist [15] ✗ ✗ ✓ ✓ ✗ Sarfgen ✓ ✓ ✓ ✓ ✓ Table 1. Comparison of Sarfgen against the existing feedback generation approaches.…”

Section: Complex Repairsmentioning

confidence: 99%

Search, align, and repair: data-driven feedback generation for introductory programming exercises

Wang

Singh

2018

Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation

109

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This paper introduces the "Search, Align, and Repair" datadriven program repair framework to automate feedback generation for introductory programming exercises. Distinct from existing techniques, our goal is to develop an efficient, fully automated, and problem-agnostic technique for large or MOOC-scale introductory programming courses. We leverage the large amount of available student submissions in such settings and develop new algorithms for identifying similar programs, aligning correct and incorrect programs, and repairing incorrect programs by finding minimal fixes. We have implemented our technique in the Sarfgen system and evaluated it on thousands of real student attempts from the Microsoft-DEV204.1x edX course and the Microsoft Code-Hunt platform. Our results show that Sarfgen can, within two seconds on average, generate concise, useful feedback for 89.7% of the incorrect student submissions. It has been integrated with the Microsoft-DEV204.1X edX class and deployed for production use.

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sk_p: a neural program corrector for MOOCs

Cited by 100 publications

References 12 publications

SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair

SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair

An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

Search, align, and repair: data-driven feedback generation for introductory programming exercises

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