Synthesizing Quotient Lenses

Maina, Solomon; Miltner, Anders; Fisher, Kathleen; Pierce, Benjamin C.; Walker, David H.; Zdancewic, Steve

doi:10.1145/3235041

Cited by 7 publications

(7 citation statements)

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“…We also show they are expressive enough for many practical uses by adding simple symmetric lenses to the Boomerang language [Bohannon et al 2008] and applying them to a range of real-world applications. This exercise also demonstrates that simple symmetric lenses can coexist with, and be extended by, other advanced lens features provided by Boomerang, including quotient lenses Maina et al 2018] and matching lenses [Barbosa et al 2010].…”

Section: Introductionmentioning

confidence: 88%

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Synthesizing symmetric lenses

Miltner

Maina

Fisher

et al. 2019

Proc. ACM Program. Lang.

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Lenses are programs that can be run both "front to back" and "back to front, " allowing updates to either their source or their target data to be transferred in both directions. Since their introduction by Foster et al., lenses have been extensively studied, extended, and applied. Recent work has also demonstrated how techniques from type-directed program synthesis can be used to efficiently synthesize a simple class of lenses-so-called bijective lenses over string data-given a pair of types (regular expressions) and a small number of examples.We extend this synthesis algorithm to a much broader class of lenses, called simple symmetric lenses, including all bijective lenses, all of the popular category of "asymmetric" lenses, and a rich subset of the more powerful "symmetric lenses" proposed by Hofmann et al. Intuitively, simple symmetric lenses allow some information to be present on one side but not the other and vice versa. They are of independent theoretical interest, being the largest class of symmetric lenses that do not rely on persistent internal state.Synthesizing simple symmetric lenses is substantially more challenging than synthesizing bijective lenses: Since some of the information on each side can be "disconnected" from the other side, there will, in general, be many lenses that agree with a given example. To guide the search process, we use stochastic regular expressions and ideas from information theory to estimate the amount of information propagated by a candidate lens, generally preferring lenses that propagate more information, as well as user annotations marking parts of the source and target data structures as either irrelevant or essential.We describe an implementation of simple symmetric lenses and our synthesis procedure as extensions to the Boomerang language. We evaluate its performance on 48 benchmark examples drawn from Flash Fill, Augeas, the bidirectional programming literature, and electronic file format synchronization tasks. Our implementation can synthesize each of these lenses in under 30 seconds.

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Section: Introductionmentioning

confidence: 88%

“…Following existing work on bidirectional program synthesis [Maina et al 2018], we split our algorithm into two communicating search procedures. The first, Expand, navigates the space of semantically equivalent regular expressions by applying rewrite rules that preserve both semantics and probability distributions.…”

Section: Searching For Likely Lensesmentioning

confidence: 99%

Synthesizing symmetric lenses

Miltner

Maina

Fisher

et al. 2019

Proc. ACM Program. Lang.

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“…The notion of a lens [31] provides a principled, compositional solution that permits modifications to a file to be automatically transported to its putative normal form. Lenses have been synthesized at small scale from specifications and translation examples [45], [46], suggesting an approach for safely transforming files [47].…”

Section: A Lenses As Bundles and Fibrationsmentioning

confidence: 99%

The Geometry of Syntax and Semantics for Directed File Transformations

Huntsman

Robinson

2020

2020 IEEE Security and Privacy Workshops (SPW)

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“…We can also treat translation between programming languages as analogy completion using examples of semantically equivalent programs in either language. is will likely rely on a number of smaller analogies, matching common pa erns in one programming language and mapping them to idiomatic code for that pa ern in another, similar to existing work in this eld [47,50].…”

Section: Future So Ware Engineering Applicationsmentioning

confidence: 99%

Analogy-making as a Core primitive in the software engineering toolbox

Sotoudeh

Thakur

2020

Proceedings of the 2020 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Sof

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An analogy is an identi cation of structural similarities and correspondences between two objects. Computational models of analogy making have been studied extensively in the eld of cognitive science to be er understand high-level human cognition. For instance, Melanie Mitchell and Douglas Hofstadter sought to be er understand high-level perception by developing the Copycat algorithm for completing analogies between le er sequences. In this paper, we argue that analogy making should be seen as a core primitive in soware engineering. We motivate this argument by showing how complex so ware engineering problems such as program understanding and source-code transformation learning can be reduced to an instance of the analogy-making problem. We demonstrate this idea using S , a new analogy-making algorithm suitable for so ware engineering applications that adapts and extends ideas from Copycat. In particular, S reduces analogy-making to searching for a sequence of update rule applications. S uses a novel representation for mathematical structures capable of e ectively representing the wide variety of information embedded in so ware. We conclude by listing major areas of future work for S and analogy-making in so ware engineering.

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Synthesizing Quotient Lenses

Cited by 7 publications

References 0 publications

Synthesizing symmetric lenses

Synthesizing symmetric lenses

The Geometry of Syntax and Semantics for Directed File Transformations

Analogy-making as a Core primitive in the software engineering toolbox

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