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85
A Type System for HigherOrder Modules
, 2003
"... We present a type theory for higherorder modules that accounts for many central issues in module system design, including translucency, applicativity, generativity, and modules as firstclass values. Our type system harmonizes design elements from previous work, resulting in a simple, economical ac ..."
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Cited by 88 (27 self)
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We present a type theory for higherorder modules that accounts for many central issues in module system design, including translucency, applicativity, generativity, and modules as firstclass values. Our type system harmonizes design elements from previous work, resulting in a simple, economical account of modular programming. The main unifying principle is the treatment of abstraction mechanisms as computational effects. Our language is the first to provide a complete and practical formalization of all of these critical issues in module system design.
Types for Modules
, 1998
"... The programming language Standard ML is an amalgam of two, largely orthogonal, languages. The Core language expresses details of algorithms and data structures. The Modules language expresses the modular architecture of a software system. Both languages are statically typed, with their static and dy ..."
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Cited by 80 (13 self)
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The programming language Standard ML is an amalgam of two, largely orthogonal, languages. The Core language expresses details of algorithms and data structures. The Modules language expresses the modular architecture of a software system. Both languages are statically typed, with their static and dynamic semantics specified by a formal definition.
Why: A MultiLanguage MultiProver Verification Tool
"... This article introduces the verification tool Why. This tool produces verification conditions from annotated programs given as input. ..."
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Cited by 45 (0 self)
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This article introduces the verification tool Why. This tool produces verification conditions from annotated programs given as input.
A Theory of Mixin Modules: Basic and Derived Operators
 Mathematical Structures in Computer Science
, 1996
"... Mixins are modules in which some components are deferred , i.e. their definition has to be provided by another module. Moreover, differently from parameterized modules (like ML functors), mixin modules can be mutually dependent and their composition supports redefinition of components (overriding). ..."
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Cited by 39 (13 self)
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Mixins are modules in which some components are deferred , i.e. their definition has to be provided by another module. Moreover, differently from parameterized modules (like ML functors), mixin modules can be mutually dependent and their composition supports redefinition of components (overriding). In this paper, we present a formal model of mixins and their basic composition operators. These operators can be viewed as a kernel language with clean semantics in which to express more complex operators of existing modular languages, including variants of inheritance in object oriented programming. Our formal model is given in an "institution independent" way, i.e. is parameterized by the semantic framework modeling the underlying core language. Introduction In object oriented languages, the definition of an heir class H from a parent class P takes usually the form H = extend P by M , where M denotes a collection of definitions of components (typically methods) which are either new, or re...
Typed CrossModule Compilation
 IN PROC. 1998 ACM SIGPLAN INTERNATIONAL CONFERENCE ON FUNCTIONAL PROGRAMMING (ICFP '98
, 1998
"... Higherorder modules are very effective in structuring large programs and defining generic, reusable software components. Unfortunately, many compilation techniques for the core languages do not work across the module boundaries. As a result, few optimizing compilers support these module facilities ..."
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Cited by 36 (12 self)
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Higherorder modules are very effective in structuring large programs and defining generic, reusable software components. Unfortunately, many compilation techniques for the core languages do not work across the module boundaries. As a result, few optimizing compilers support these module facilities well. This paper exploits the semantic property of MLstyle modules to support efficient crossmodule compilation. More specifically, we present a typedirected translation of the MacQueenTofte higherorder modules into a predicative variant of the polymorphic calculus F! . Because modules can be compiled in the same way as ordinary polymorphic functions, standard typebased optimizations such as representation analysis immediately carry over to the module languages. We further show that the fulltransparency property of the MacQueenTofte system yields a near optimal crossmodule compilation framework. By propagating various static information through the module boundaries, many static pr...
A probabilistic language based upon sampling functions
 In Conference Record of the 32nd Annual ACM Symposium on Principles of Programming Languages
, 2005
"... As probabilistic computations play an increasing role in solving various problems, researchers have designed probabilistic languages which treat probability distributions as primitive datatypes. Most probabilistic languages, however, focus only on discrete distributions and have limited expressive p ..."
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Cited by 34 (0 self)
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As probabilistic computations play an increasing role in solving various problems, researchers have designed probabilistic languages which treat probability distributions as primitive datatypes. Most probabilistic languages, however, focus only on discrete distributions and have limited expressive power. This paper presents a probabilistic language, called λ○, whose expressive power is beyond discrete distributions. Rich expressiveness of λ ○ is due to its use of sampling functions, i.e., mappings from the unit interval (0.0, 1.0] to probability domains, in specifying probability distributions. As such, λ ○ enables programmers to formally express and reason about sampling methods developed in simulation theory. The use of λ ○ is demonstrated with three applications in robotics: robot localization, people tracking, and robotic mapping. All experiments have been carried out with real robots.
Mixin’ Up the ML Module System
"... ML modules provide hierarchical namespace management, as well as finegrained control over the propagation of type information, but they do not allow modules to be broken up into separately compilable, mutually recursive components. Mixin modules facilitate recursive linking of separately compiled c ..."
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Cited by 22 (9 self)
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ML modules provide hierarchical namespace management, as well as finegrained control over the propagation of type information, but they do not allow modules to be broken up into separately compilable, mutually recursive components. Mixin modules facilitate recursive linking of separately compiled components, but they are not hierarchically composable and typically do not support type abstraction. We synthesize the complementary advantages of these two mechanisms in a novel module system design we call MixML. A MixML module is like an ML structure in which some of the components are specified but not defined. In other words, it unifies the ML structure and signature languages into one. MixML seamlessly integrates hierarchical composition, translucent MLstyle data abstraction, and mixinstyle recursive linking. Moreover, the design of MixML is clean and minimalist; it emphasizes how all the salient, semantically interesting features of the ML module system (as well as several proposed extensions to it) can be understood simply as stylized uses of a small set of orthogonal underlying constructs, with mixin composition playing a central role.
A calculus for linktime compilation
, 2000
"... Abstract. We present a module calculus for studying a simple model of linktime compilation. The calculus is stratified into a term calculus, a core module calculus, and a linking calculus. At each level, we show that the calculus enjoys a computational soundness property: iftwo terms are equivalent ..."
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Cited by 22 (4 self)
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Abstract. We present a module calculus for studying a simple model of linktime compilation. The calculus is stratified into a term calculus, a core module calculus, and a linking calculus. At each level, we show that the calculus enjoys a computational soundness property: iftwo terms are equivalent in the calculus, then they have the same outcome in a smallstep operational semantics. This implies that any module transformation justified by the calculus is meaning preserving. This result is interesting because recursive module bindings thwart confluence at two levels ofour calculus, and prohibit application ofthe traditional technique for showing computational soundness, which requires confluence. We introduce a new technique, based on properties we call lift and project, thatusesa weaker notion of confluence with respect to evaluation to establish computational soundness for our module calculus. We also introduce the weak distributivity property for a transformation T operating on modules D1 and D2 linked by ⊕: T (D1 ⊕ D2) =T (T (D1) ⊕ T (D2)). We argue that this property finds promising candidates for linktime optimizations. 1
Functors for Proofs and Programs
, 2003
"... This paper presents the formal verication with the Coq proof assistant of several applicative data structures implementing nite sets. These implementations are parameterized by an ordered type for the elements, using functors from the ML module system. The verication follows closely this scheme, ..."
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Cited by 20 (3 self)
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This paper presents the formal verication with the Coq proof assistant of several applicative data structures implementing nite sets. These implementations are parameterized by an ordered type for the elements, using functors from the ML module system. The verication follows closely this scheme, using the newly Coq module system.
A probabilistic language based on sampling functions
 ACM Transactions on Programming Languages and Systems
, 2006
"... As probabilistic computations play an increasing role in solving various problems, researchers have designed probabilistic languages which treat probability distributions as primitive datatypes. Most probabilistic languages, however, focus only on discrete distributions and have limited expressive p ..."
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Cited by 16 (0 self)
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As probabilistic computations play an increasing role in solving various problems, researchers have designed probabilistic languages which treat probability distributions as primitive datatypes. Most probabilistic languages, however, focus only on discrete distributions and have limited expressive power. This article presents a probabilistic language, called λ○, whose expressive power is beyond discrete distributions. Rich expressiveness of λ ○ is due to its use of sampling functions, that is, mappings from the unit interval (0.0, 1.0] to probability domains, in specifying probability distributions. As such, λ ○ enables programmers to formally express and reason about sampling methods developed in simulation theory. The use of λ ○ is demonstrated with three applications in robotics: robot localization, people tracking, and robotic mapping. All experiments have been carried out with real robots.