We propose a novel approach to the view update problem for tree-structured data: a domainspecific programming language in which all expressions denote bi-directional transformations on trees. In one direction, these transformations—dubbed lenses—map a “concrete ” tree into a simplified “abstract view”; in the other, they map a modified abstract view, together with the original concrete tree, to a correspondingly modified concrete tree. Our design emphasizes both robustness and ease of use, guaranteeing strong well-behavedness and totality properties for welltyped lenses. We identify a natural mathematical space of well-behaved bi-directional transformations over arbitrary structures, study definedness and continuity in this setting, and state a precise connection with the classical theory of “update translation under a constant complement ” from databases. We then instantiate this semantic framework in the form of a collection of lens combinators that can be assembled to describe transformations on trees. These combinators include familiar constructs from functional programming (composition, mapping, projection, conditionals, recursion) together with some novel primitives for manipulating trees (splitting, pruning, copying, merging, etc.). We illustrate the expressiveness of these combinators by developing a number of bi-directional listprocessing transformations as derived forms. An extended example shows how our combinators can be used to define a lens that translates between a native HTML representation of browser bookmarks and a generic abstract bookmark format.

A testability transformation is a source-to-source transformation that aims to improve the ability of a given test generation method to generate test data for the original program. This paper

(QVT) standard as applied to the specification of bidirectional transformations between models. We discuss what is meant by bidirectional transformations, and the model-driven development scenarios in which they are needed. We analyse the fundamental requirements on tools which support such transformations, and discuss some semantic issues which arise. We argue that a considerable amount of basic research is needed before suitable tools will be fully realisable, and suggest directions for this future research.

This paper presents a novel editor supporting interactive refinement in the development of structured documents. The user performs a sequence of editing operations on the document view, and the editor automatically derives an efficient and reliable document source and a transformation that produces the document view. The editor is unique in its programmability, in the sense that transformation can be obtained through editing operations. The main tricks behind are the utilization of the view-updating technique developed in the database community, and a new bidirectional transformation language that cannot only describe the relationship between the document source and its view, but also data dependency in the view.

Bidirectional transformation is a pair of transformations: a view function and a backward transformation. A view function maps one data structure called source onto another called view. The corresponding backward transformation reflects changes in the view to the source. Its practically useful applications include replicated data synchronization, presentation-oriented editor development, tracing software development, and view updating in the database community. However, developing a bidirectional transformation is hard, because one has to give two mappings that satisfy the bidirectional properties for system consistency. In this paper, we propose a new framework for bidirectionalization that can automatically generate a useful backward transformation from a view function while guaranteeing that the two transformations satisfy the bidirectional properties. Our framework is based on two known approaches to bidirectionalization, namely the

There are now a number of bidirectional programming languages, where every program can be read both as a forward transformation mapping one data structure to another and as a reverse transformation mapping an edited output back to a correspondingly edited input. Besides parsimony—the two related transformations are described by just one expression—such languages are attractive because they promise strong behavioral laws about how the two transformations fit together—e.g., their composition is the identity function. It has repeatedly been observed, however, that such laws are actually a bit too strong: in practice, we do not want them “on the nose, ” but only up to some equivalence, allowing inessential details, such as whitespace, to be modified after a round trip. Some bidirectional languages loosen their laws in this way, but only for specific, baked-in equivalences. In this work, we propose a general theory of quotient lenses— bidirectional transformations that are well behaved modulo equivalence relations controlled by the programmer. Semantically, quotient lenses are a natural refinement of lenses, which we have studied in previous work. At the level of syntax, we present a rich set of constructs for programming with canonizers and for quotienting lenses by canonizers. We track equivalences explicitly, with the type of every quotient lens specifying the equivalences it respects. We have implemented quotient lenses as a refinement of the bidirectional string processing language Boomerang. We present a number of useful primitive canonizers for strings, and give a simple extension of Boomerang’s regular-expression-based type system to statically typecheck quotient lenses. The resulting language is an expressive tool for transforming real-world, ad-hoc data formats. We demonstrate the power of our notation by developing an extended example based on the UniProt genome database format and illustrate the generality of our approach by showing how uses of quotienting in other bidirectional languages can be translated into our notation.

Security views are a flexible and effective means of controlling access to confidential information. Rather than allowing untrusted users to access the source data directly, they can instead be provided with a restricted view, from which all confidential information has been removed. The program that generates the view effectively embodies a confidentiality policy for the underlying source data. However, this approach has a significant drawback: it prevents users from updating the data in the view. To address the “view update problem ” in general, a number of bidirectional languages have been proposed. Programs in these languages—often called lenses—can be run in two directions: read from left to right, they map sources to views; read from right to left, they map updated views back to updated sources. However, existing bidirectional languages do not deal adequately with security issues. In particular, they do not provide a way to ensure the integrity of data in the source as it is manipulated by untrusted users of the view. We propose a novel framework of secure lenses that addresses these shortcomings. We first enrich the types of basic lenses with equivalence relations capturing notions of confidentiality and integrity and formulate the essential security conditions on source data as non-interference properties. We then offer a concrete instantiation of our framework in the domain of string transformations, developing concrete syntax for security-annotated regular expressions as well as a collection of bidirectional string combinators with annotated expressions as their types.

Bidirectional programming languages have been proposed as a practical approach to the view update problem. Programs in these languages, often called lenses, can be read in two ways— from left to right as functions mapping sources to views, and from right to left as functions mapping updated views back to updated sources. Lenses address the view update problem by making it possible to define a view and its associated update policy together. One issue that has not received sufficient attention in the design of bidirectional languages is alignment. In general, to correctly propagate an update to a view, a lens needs to match up the pieces of the edited view with corresponding pieces of the underlying source. Unfortunately, existing bidirectional languages are extremely limited in their treatment of alignment—they only support simple strategies that do not suffice for many examples of practical interest. In this paper, we propose a novel framework of matching lenses that extends basic lenses with new mechanisms for calculating and using alignments. We enrich the types of lenses with “chunks ” that identify the locations of data that should be re-aligned after updates, and we formulate refined behavioral laws that capture essential constraints on the handling of chunks. To demonstrate the utility of our approach, we develop a core language of matching lenses for string data, and we extend it with primitives for describing a number of useful alignment heuristics.

Invertible programming occurs in the area of data conversion where it is required that the conversion in one direction is the inverse of the other. For that purpose, we introduce bidirectional arrows (biarrows). The bi-arrow class is an extension of Haskell’s arrow class with an extra combinator that changes the direction of computation. The advantage of the use of bi-arrows for invertible programming is the preservation of invertibility properties using the biarrow combinators. Programming with bi-arrows in a polytypic or generic way exploits this the most. Besides bidirectional polytypic examples, including invertible serialization, we give the definition of a monadic bi-arrow transformer, which we use to construct a bidirectional parser/pretty printer.

Abstract. Pattern matching is advantageous for understanding and reasoning about function definitions, but it tends to tightly couple the interface and implementation of a datatype. Significant effort has been invested in tackling this loss of modularity; however, decoupling patterns from concrete representations while maintaining soundness of reasoning has been a challenge. Inspired by the development of invertible programming, we propose an approach to abstract datatypes based on a rightinvertible language rinv—every function has a right (or pre-) inverse. We show how this new design is able to permit a smooth incremental transition from programs with algebraic datatypes and pattern matching, to ones with proper encapsulation (implemented as abstract datatypes), while maintaining simple and sound reasoning.