NoFAQ: synthesizing command repairs from examples

D’Antoni, Loris; Singh, Rishabh; Vaughn, Michael

doi:10.1145/3106237.3106241

Cited by 23 publications

(19 citation statements)

References 31 publications

(51 reference statements)

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“…Extending specifications with more properties would allow for a variety of expressive analyses-extending the syntactic checks of ShellCheck [33] and SecureCode [15], the man-page-directed listings of ExplainShell [32], or the purely textual model of NoFAQ [16]. Two promising directions: identifying errors and command misuse in a shell script, and a shell tutor (B3, U1).…”

Section: Incremental Computationmentioning

confidence: 99%

Unix shell programming

Greenberg

Καλλάς

Vasilakis

2021

Proceedings of the Workshop on Hot Topics in Operating Systems

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The Unix shell is a powerful, ubiquitous, and reviled tool for managing computer systems. The shell has been largely ignored by academia and industry. While many replacement shells have been proposed, the Unix shell persists. Two recent threads of formal and practical research on the shell enable new approaches. We can help manage the shell's essential shortcomings (dynamism, power, and abstruseness) and address its inessential ones. Improving the shell holds much promise for development, ops, and data processing. CCS CONCEPTS• Software and its engineering → Scripting languages; Compilers; Operating systems.

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Section: Incremental Computationmentioning

confidence: 99%

Unix shell programming

Greenberg

Καλλάς

Vasilakis

2021

Proceedings of the Workshop on Hot Topics in Operating Systems

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“…Synthesizing Transformations by Example. Another related area of work is code transformation techniques, such as LASE [33], Genesis [27], NoFAQ [8], and REFAZER [41], which can extract and synthesize syntactic transformations from given examples. These techniques could potentially be used for program repair.…”

Section: Related Workmentioning

confidence: 99%

DeepDelta: learning to repair compilation errors

Mesbah

Rice

Johnston

et al. 2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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Programmers spend a substantial amount of time manually repairing code that does not compile. We observe that the repairs for any particular error class typically follow a pattern and are highly mechanical. We propose a novel approach that automatically learns these patterns with a deep neural network and suggests program repairs for the most costly classes of build-time compilation failures. We describe how we collect all build errors and the human-authored, in-progress code changes that cause those failing builds to transition to successful builds at Google. We generate an AST di from the textual code changes and transform it into a domain-specic language called Delta that encodes the change that must be made to make the code compile. We then feed the compiler diagnostic information (as source) and the Delta changes that resolved the diagnostic (as target) into a Neural Machine Translation network for training. For the two most prevalent and costly classes of Java compilation errors, namely missing symbols and mismatched method signatures, our system called DD, generates the correct repair changes for 19,314 out of 38,788 (50%) of unseen compilation errors. The correct changes are in the top three suggested xes 86% of the time on average.

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“…In this paper, we focus on augmenting programming-by-example (PBE) techniques that use examples as the specification. Recently, PBE has been pursued for various tasks, e.g., data extraction [33], data filtering [61], data visualization [59], string transformations [22], table transformations [23,14], automated code repair [12,50], map-reduce program generation [54], grammar generation [2], and implementing network configurations [51,65].…”

Section: Related Workmentioning

confidence: 99%

Interactive Program Synthesis by Augmented Examples

Zhang

Lowmanstone

Wang

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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Mark parts of an input as literal or general 2 Annotate parts of synthesized programs to be desired or undesired 3 View other similar examples and corner cases 4 Figure 1: A user interactively synthesizes a regular expression (regex) that accepts phone numbers starting with an optional '+'. After 1) adding some positive and negative examples, the user 2) marks the + sign as literal and the following numbers as a general class of digits to specify how individual characters should be treated by the synthesizer. To refine the synthesis result, the user can either 3) directly mark subexpressions as desired or undesired in the final regex, or 4) ask for additional examples to validate and enhance her understanding of a regex candidate as well as identify and label counterexamples, therefore adding them to the set of user-provided input-output examples.

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NoFAQ: synthesizing command repairs from examples

Cited by 23 publications

References 31 publications

Unix shell programming

Unix shell programming

DeepDelta: learning to repair compilation errors

Interactive Program Synthesis by Augmented Examples

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