Automatic patch generation by learning correct code

Long, Fan; Rinard, Martin

doi:10.1145/2914770.2837617

Cited by 87 publications

(43 citation statements)

References 37 publications

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“…Search-Based APR: Weimer et al [2009] proposed GenProg which first uses the genetic algorithm to search possible useful sentences to generate patches. After the opening work of Weimer et al [2009], other search algorithms, e.g., random search in [Qi et al 2014], have been used for repairing, and methods [Long and Rinard 2015] [Long and Rinard 2016] for selecting candidate patches have also been well studied.…”

Section: Related Workmentioning

confidence: 99%

Efficient automated repair of high floating-point errors in numerical libraries

Chen

Mao

et al. 2019

Proc. ACM Program. Lang.

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Floating point computation is by nature inexact, and numerical libraries that intensively involve floating-point computations may encounter high floating-point errors. Due to the wide use of numerical libraries, it is highly desired to reduce high floating-point errors in them. Using higher precision will degrade performance and may also introduce extra errors for certain precision-specific operations in numerical libraries. Using mathematical rewriting that mostly focuses on rearranging floating-point expressions or taking Taylor expansions may not fit for reducing high floating-point errors evoked by ill-conditioned problems that are in the nature of the mathematical feature of many numerical programs in numerical libraries. In this paper, we propose a novel approach for efficient automated repair of high floating-point errors in numerical libraries. Our main idea is to make use of the mathematical feature of a numerical program for detecting and reducing high floating-point errors. The key components include a detecting method based on two algorithms for detecting high floating-point errors and a repair method for deriving an approximation of a mathematical function to generate patch to satisfy a given repair criterion. We implement our approach by constructing a new tool called AutoRNP. Our experiments are conducted on 20 numerical programs in GNU Scientific Library (GSL). Experimental results show that our approach can efficiently repair (with 100% accuracy over all randomly sampled points) high floating-point errors for 19 of the 20 numerical programs. CCS Concepts: • Mathematics of computing → Mathematical software; Computations in finite fields; • Theory of computation → Numeric approximation algorithms; • Software and its engineering → Search-based software engineering;

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Section: Related Workmentioning

confidence: 99%

Efficient automated repair of high floating-point errors in numerical libraries

Chen

Mao

et al. 2019

Proc. ACM Program. Lang.

View full text Add to dashboard Cite

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“…Prophet [27] mines a database of successful patches and uses these patches to repair defects in large, real-world applications. However, it is unclear how this approach would be applicable to our educational setting.…”

Section: Related Workmentioning

confidence: 99%

Automated clustering and program repair for introductory programming assignments

2018

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Providing feedback on programming assignments is a tedious task for the instructor, and even impossible in large Massive Open Online Courses with thousands of students. Previous research has suggested that program repair techniques can be used to generate feedback in programming education. In this paper, we present a novel fully automated program repair algorithm for introductory programming assignments. The key idea of the technique, which enables automation and scalability, is to use the existing correct student solutions to repair the incorrect attempts. We evaluate the approach in two experiments: (I) We evaluate the number, size and quality of the generated repairs on 4,293 incorrect student attempts from an existing MOOC. We find that our approach can repair 97% of student attempts, while 81% of those are small repairs of good quality. (II) We conduct a preliminary user study on performance and repair usefulness in an interactive teaching setting. We obtain promising initial results (the average usefulness grade 3.4 on a scale from 1 to 5), and conclude that our approach can be used in an interactive setting. CCS Concepts • Applied computing → Computerassisted instruction; • Software and its engineering → Software testing and debugging;

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“…Automatic program repair. Aside from the researches for repairing students' programs, there also have been many recent researches for fixing bugs in large software, e.g., Kim et al 2013;Kneuss et al 2015;Könighofer and Bloem 2011;Le Goues et al 2012;Long and Rinard 2016;Nguyen et al 2013;. The techniques for repairing students' programs and developers' software are complementary; while the former can generate relatively complicated patches for small programs, the latter usually produces small patches for large programs.…”

Section: Related Workmentioning

confidence: 99%

Automatic diagnosis and correction of logical errors for functional programming assignments

Lee

Song

et al. 2018

Proc. ACM Program. Lang.

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We present FixML, a system for automatically generating feedback on logical errors in functional programming assignments. As functional languages have been gaining popularity, the number of students enrolling functional programming courses has increased significantly. However, the quality of feedback, in particular for logical errors, is hardly satisfying. To provide personalized feedback on logical errors, we present a new errorcorrection algorithm for functional languages, which combines statistical error-localization and type-directed program synthesis enhanced with components reduction and search space pruning using symbolic execution. We implemented our algorithm in a tool, called FixML, and evaluated it with 497 students' submissions from 13 exercises, including not only introductory but also more advanced problems. Our experimental results show that our tool effectively corrects various and complex errors: it fixed 43% of the 497 submissions in 5.4 seconds on average and managed to fix a hard-to-find error in a large submission, consisting of 154 lines. We also performed user study with 18 undergraduate students and confirmed that our system actually helps students to better understand their programming errors. CCS Concepts: • Software and its engineering → Automatic programming; Functional languages;

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Automatic patch generation by learning correct code

Cited by 87 publications

References 37 publications

Efficient automated repair of high floating-point errors in numerical libraries

Efficient automated repair of high floating-point errors in numerical libraries

Automated clustering and program repair for introductory programming assignments

Automatic diagnosis and correction of logical errors for functional programming assignments

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