Uniform boilerplate and list processing

Mitchell, Neil; Runciman, Colin

doi:10.1145/1291201.1291208

Cited by 50 publications

(54 citation statements)

References 21 publications

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“…The Uniplate and Strafunsky libraries are variations on SYB offering different interfaces but neither allows mapping over more types compared to SYB [13,15].…”

Section: Generic Programming Frameworkmentioning

confidence: 99%

Type Generic Observing

Faddegon

Chitil

2014

Lecture Notes in Computer Science

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Abstract. Observing intermediate values helps to understand what isgoing on when your program runs. Gill presented an observation method for lazy functional languages that preserves the program's semantics. However, users need to define for each type how its values are observed: a laborious task and strictness of the program can easily be affected. Here we define how any value can be observed based on the structure of its type by applying generic programming frameworks. Furthermore we present an extension to specify per observation point how much to observe of a value. We discuss especially functional values and behaviour based on class membership in generic programming frameworks.

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“…The Uniplate and Strafunsky libraries are variations on SYB offering different interfaces but neither allows mapping over more types compared to SYB [13,15].…”

Section: Generic Programming Frameworkmentioning

confidence: 99%

Type Generic Observing

Faddegon

Chitil

2014

Lecture Notes in Computer Science

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“…The present paper provides the first substantial attempt of a systematic discussion of programming errors and their relationship to strategy properties. The technique of § 4.1 to use less generic traversal schemes has also been explored in [16] in the context of devising simpler types for traversal programs and more efficient implementations. The technique of § 4.4 to statically check for reachable types is inspired by adaptive programming [18,15,12] that subjects its traversal specifications to a similar check.…”

Section: Related Workmentioning

confidence: 99%

Programming errors in traversal programs over structured data

Lämmel

Thompson

Kaiser

2013

Science of Computer Programming

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Traversal strategies provide an established means of describing automated queries, analyses, transformations, and other non-trivial computations on deeply structured data (including, most notably, data representations of software artifacts such as programs). The resulting traversal programs are prone to programming errors. We are specifically concerned with errors that go beyond classic type errors, in particular: (i) divergence of traversal, (ii) unintentional extent of traversal into data, (iii) trivial traversal results, (iv) inapplicability of the constituents of a traversal program along traversal. We deliver a taxonomy of programming errors, and start attacking some of them by refinements of traversal programming.

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“…To simplify a DSL we apply these identities from left to right wherever they occur, using the Uniplate generics library (Mitchell and Runciman 2007).…”

Section: Dsl Simplificationmentioning

confidence: 99%

“…We present the output simplification directly as we have implemented it, using the Uniplate generics library (Mitchell and Runciman 2007). All of the rules operate at the expression level, and each rule is correct individually.…”

Section: Output Simplificationmentioning

confidence: 99%

Deriving a Relationship from a Single Example

Mitchell

2010

Approaches and Applications of Inductive Programming

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Given an appropriate domain specific language (DSL), it is possible to describe the relationship between Haskell data types and many generic functions, typically type-class instances. While describing the relationship is possible, it is not always an easy task. There is an alternative -simply give one example output for a carefully chosen input, and have the relationship derived.When deriving a relationship from only one example, it is important that the derived relationship is the intended one. We identify general restrictions on the DSL, and on the provided example, to ensure a level of predictability. We then apply these restrictions in practice, to derive the relationship between Haskell data types and generic functions. We have used our scheme in the DERIVE tool, where over 60% of type classes are derived from a single example.

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Uniform boilerplate and list processing

Cited by 50 publications

References 21 publications

Type Generic Observing

Type Generic Observing

Programming errors in traversal programs over structured data

Deriving a Relationship from a Single Example

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