Feedback-driven semi-supervised synthesis of program transformations

While editing code, it is common for developers to make multiple related repeated edits that are all instances of a more general program transformation. Since this process can be tedious and error-prone, we study the problem of automatically learning program transformations from past edits, which ca...

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Vydané v:	Proceedings of ACM on programming languages Ročník 4; číslo OOPSLA; s. 1 - 30
Hlavní autori:	Gao, Xiang, Barke, Shraddha, Radhakrishna, Arjun, Soares, Gustavo, Gulwani, Sumit, Leung, Alan, Nagappan, Nachiappan, Tiwari, Ashish
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY, USA ACM 13.11.2020
Predmet:	Artificial intelligence Automatic programming Computing methodologies Development frameworks and environments Integrated and visual development environments Software and its engineering Software creation and management Software development techniques Software maintenance tools Software notations and tools Programming by Example Program synthesis Refactoring Program transformation
ISSN:	2475-1421, 2475-1421
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Abstract	While editing code, it is common for developers to make multiple related repeated edits that are all instances of a more general program transformation. Since this process can be tedious and error-prone, we study the problem of automatically learning program transformations from past edits, which can then be used to predict future edits. We take a novel view of the problem as a semi-supervised learning problem: apart from the concrete edits that are instances of the general transformation, the learning procedure also exploits access to additional inputs (program subtrees) that are marked as positive or negative depending on whether the transformation applies on those inputs. We present a procedure to solve the semi-supervised transformation learning problem using anti-unification and programming-by-example synthesis technology. To eliminate reliance on access to marked additional inputs, we generalize the semi-supervised learning procedure to a feedback-driven procedure that also generates the marked additional inputs in an iterative loop. We apply these ideas to build and evaluate three applications that use different mechanisms for generating feedback. Compared to existing tools that learn program transformations from edits, our feedback-driven semi-supervised approach is vastly more effective in successfully predicting edits with significantly lesser amounts of past edit data.
AbstractList	While editing code, it is common for developers to make multiple related repeated edits that are all instances of a more general program transformation. Since this process can be tedious and error-prone, we study the problem of automatically learning program transformations from past edits, which can then be used to predict future edits. We take a novel view of the problem as a semi-supervised learning problem: apart from the concrete edits that are instances of the general transformation, the learning procedure also exploits access to additional inputs (program subtrees) that are marked as positive or negative depending on whether the transformation applies on those inputs. We present a procedure to solve the semi-supervised transformation learning problem using anti-unification and programming-by-example synthesis technology. To eliminate reliance on access to marked additional inputs, we generalize the semi-supervised learning procedure to a feedback-driven procedure that also generates the marked additional inputs in an iterative loop. We apply these ideas to build and evaluate three applications that use different mechanisms for generating feedback. Compared to existing tools that learn program transformations from edits, our feedback-driven semi-supervised approach is vastly more effective in successfully predicting edits with significantly lesser amounts of past edit data.
ArticleNumber	219
Author	Soares, Gustavo Tiwari, Ashish Gao, Xiang Radhakrishna, Arjun Barke, Shraddha Gulwani, Sumit Leung, Alan Nagappan, Nachiappan
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Snippet	While editing code, it is common for developers to make multiple related repeated edits that are all instances of a more general program transformation. Since...
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SubjectTerms	Artificial intelligence Automatic programming Computing methodologies Development frameworks and environments Integrated and visual development environments Software and its engineering Software creation and management Software development techniques Software maintenance tools Software notations and tools
SubjectTermsDisplay	Computing methodologies -- Artificial intelligence Software and its engineering -- Software creation and management -- Software development techniques -- Automatic programming Software and its engineering -- Software notations and tools -- Development frameworks and environments -- Integrated and visual development environments Software and its engineering -- Software notations and tools -- Software maintenance tools
Title	Feedback-driven semi-supervised synthesis of program transformations
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