Common data types like dates, addresses, phone numbers and tables can have multiple textual representations, and many heavily-used languages, such as SQL, come in several dialects. These variations can cause data to be misinterpreted, leading to silent data corruption, failure of data processing systems, or even security vulnerabilities. Saggitarius is a new language and system designed to help programmers reason about the format of data, by describing grammatical domains---that is, sets of context-free grammars that describe the many possible representations of a datatype. We describe the design of Saggitarius via example and provide a relational semantics. We show how Saggitarius may be used to analyze a data set: given example data, it uses an algorithm based on semi-ring parsing and MaxSAT to infer which grammar in a given domain best matches that data. We evaluate the effectiveness of the algorithm on a benchmark suite of 110 example problems, and we demonstrate that our system typically returns a satisfying grammar within a few seconds with only a small number of examples. We also delve deeper into a more extensive case study on using Saggitarius for CSV dialect detection. Despite being general-purpose, we find that Saggitarius offers comparable results to hand-tuned, specialized tools; in the case of CSV, it infers grammars for 84% of benchmarks within 60 seconds, and has comparable accuracy to custom-built dialect detection tools.

Lexers and parsers are attractive targets for attackers because they often sit at the boundary between a software system's internals and the outside world. Formally verified lexers can reduce the attack surface of these systems, thus making them more secure.

Parsers are security-critical components of many software systems, and verified parsing therefore has a key role to play in secure software design. However, existing verified parsers for context-free grammars are limited in their expressiveness, termination properties, or performance characteristics. They are only compatible with a restricted class of grammars, they are not guaranteed to terminate on all inputs, or they are not designed to be performant on grammars for real-world programming languages and data formats.

Big-step and small-step are two popular flavors of operational semantics. Big-step is often seen as a more natural transcription of informal descriptions, as well as being more convenient for some applications such as interpreter generation or optimization verification. Small-step allows reasoning about non-terminating computations, concurrency and interactions. It is also generally preferred for reasoning about type systems. Instead of having to manually specify equivalent semantics in both styles for different applications, it would be useful to choose one and derive the other in a systematic or, preferably, automatic way. Transformations of small-step semantics into big-step have been investigated in various forms by Danvy and others. However, it appears that a corresponding transformation from big-step to small-step semantics has not had the same attention. We present a fully automated transformation that maps big-step evaluators written in direct style to their small-step counterparts. Many of the steps in the transformation, which include CPS-conversion, defunctionalisation, and various continuation manipulations, mirror those used by Danvy and his co-authors. For many standard languages, including those with either call-by-value or call-by-need and those with state, the transformation produces small-step semantics that are close in style to handwritten ones. We evaluate the applicability and correctness of the approach on 20 languages with a range of features.

An LL(1) parser is a recursive descent algorithm that uses a single token of lookahead to build a grammatical derivation for an input sequence. We present an LL(1) parser generator that, when applied to grammar G, produces an LL(1) parser for G if such a parser exists. We use the Coq Proof Assistant to verify that the generator and the parsers that it produces are sound and complete, and that they terminate on all inputs without using fuel parameters. As a case study, we extract the tool’s source code and use it to generate a JSON parser. The generated parser runs in linear time; it is two to four times slower than an unverified parser for the same grammar.

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.

Quotient lenses are bidirectional transformations whose correctness laws are “loosened” by specified equivalence relations, allowing inessential details in concrete data formats to be suppressed. For example, a programmer could use a quotient lens to define a transformation that ignores the order of fields in XML data, so that two XML files with the same fields but in different orders would be considered the same, allowing a single, simple program to handle them both. Building on a recently published algorithm for synthesizing plain bijective lenses from high-level specifications, we show how to synthesize bijective quotient lenses in three steps. First, we introduce quotient regular expressions (QREs), annotated regular expressions that conveniently mark inessential aspects of string data formats; each QRE specifies, simulteneously, a regular language and an equivalence relation on it. Second, we introduce QRE lenses , i.e., lenses mapping between QREs. Our key technical result is a proof that every QRE lens can be transformed into a functionally equivalent lens that canonizes source and target data just at the “edges” and that uses a bijective lens to map between the respective canonical elements; no internal canonization occurs in a lens in this normal form. Third, we leverage this normalization theorem to synthesize QRE lenses from a pair of QREs and example input-output pairs, reusing earlier work on synthesizing plain bijective lenses. We have implemented QREs and QRE lens synthesis as an extension to the bidirectional programming language Boomerang. We evaluate the effectiveness of our approach by synthesizing QRE lenses between various real-world data formats in the Optician benchmark suite.

Bidirectional transformations between different data representations occur frequently in modern software systems. They appear as serializers and deserializers, as parsers and pretty printers, as database views and view updaters, and as a multitude of different kinds of ad hoc data converters. Manually building bidirectional transformations---by writing two separate functions that are intended to be inverses---is tedious and error prone. A better approach is to use a domain-specific language in which both directions can be written as a single expression. However, these domain-specific languages can be difficult to program in, requiring programmers to manage fiddly details while working in a complex type system. We present an alternative approach. Instead of coding transformations manually, we synthesize them from declarative format descriptions and examples. Specifically, we present Optician, a tool for type-directed synthesis of bijective string transformers. The inputs to Optician are a pair of ordinary regular expressions representing two data formats and a few concrete examples for disambiguation. The output is a well-typed program in Boomerang (a bidirectional language based on the theory of lenses). The main technical challenge involves navigating the vast program search space efficiently. In particular, and unlike most prior work on type-directed synthesis, our system operates in the context of a language with a rich equivalence relation on types (the theory of regular expressions). Consequently, program synthesis requires search in two dimensions: First, our synthesis algorithm must find a pair of "syntactically compatible types," and second, using the structure of those types, it must find a type- and example-compliant term. Our key insight is that it is possible to reduce the size of this search space without losing any computational power by defining a new language of lenses designed specifically for synthesis. The new language is free from arbitrary function composition and operates only over types and terms in a new disjunctive normal form. We prove (1) our new language is just as powerful as a more natural, compositional, and declarative language and (2) our synthesis algorithm is sound and complete with respect to the new language. We also demonstrate empirically that our new language changes the synthesis problem from one that admits intractable solutions to one that admits highly efficient solutions, able to synthesize intricate lenses between complex file formats in seconds. We evaluate Optician on a benchmark suite of 39 examples that includes both microbenchmarks and realistic examples derived from other data management systems including Flash Fill, a tool for synthesizing string transformations in spreadsheets, and Augeas, a tool for bidirectional processing of Linux system configuration files.

Although laziness enables beautiful code, it comes with non-trivial performance costs. The ghc compiler for Haskell has optimizations to reduce those costs, but the optimizations are not sufficient. As a result, Haskell also provides a variety of strictness annotations so that users can indicate program points where an expression should be evaluated eagerly. Skillful use of those annotations is a black art, known only to expert Haskell programmers. In this paper, we introduce AUTOBAHN, a tool that uses genetic algorithms to automatically infer strictness annotations that improve program performance on representative inputs. Users examine the suggested annotations for soundness and can instruct AUTOBAHN to automatically produce modified sources. Experiments on 60 programs from the NoFib benchmark suite show that AUTOBAHN can infer annotation sets that improve runtime performance by a geometric mean of 8.5%. Case studies show AUTOBAHN can reduce the live size of a GC simulator by 99% and infer application-specific annotations for Aeson library code. A 10-fold cross-validation study shows the AUTOBAHN -optimized GC simulator generally outperforms a version optimized by an expert.

File systems are often used to store persistent application data, but manipulating file systems using standard APIs can be difficult for programmers. Forest is a domain-specific language that bridges the gap between the on-disk and in-memory representations of file system data. Given a high-level specification of the structure, contents, and properties of a collection of directories, files, and symbolic links, the Forest compiler generates tools for loading, storing, and validating that data. Unfortunately, the initial implementation of Forest offered few mechanisms for controlling cost-e.g., the run-time system could load gigabytes of data, even if only a few bytes were needed. This paper introduces Incremental Forest (iForest), an extension to Forest with an explicit delay construct that programmers can use to precisely control costs. We describe the design of iForest using a series of running examples, present a formal semantics in a core calculus, and define a simple cost model that accurately characterizes the resources needed to use a given specification. We propose skins, which allow programmers to modify the delay structure of a specification in a compositional way, and develop a static type system for ensuring compatibility between specifications and skins. We prove the soundness and completeness of the type system and a variety of algebraic properties of skins. We describe an OCaml implementation and evaluate its performance on applications developed in collaboration with watershed hydrologists.

Networked embedded systems are ubiquitous in modern society. Examples include SCADA systems that manage physical infrastructure, medical devices such as pacemakers and insulin pumps, and vehicles such as airplanes and automobiles. Such devices are connected to networks for a variety of compelling reasons, including the ability to access diagnostic information conveniently, perform software updates, provide innovative features, and lower costs. Researchers and hackers have shown that these kinds of networked embedded systems are vulnerable to remote attacks and that such attacks can cause physical damage and can be hidden from monitors [1, 4].

Despite the advances made by modern parsing strategies such as PEG, LL(*), GLR, and GLL, parsing is not a solved problem. Existing approaches suffer from a number of weaknesses, including difficulties supporting side-effecting embedded actions, slow and/or unpredictable performance, and counter-intuitive matching strategies. This paper introduces the ALL(*) parsing strategy that combines the simplicity, efficiency, and predictability of conventional top-down LL(k) parsers with the power of a GLR-like mechanism to make parsing decisions. The critical innovation is to move grammar analysis to parse-time, which lets ALL(*) handle any non-left-recursive context-free grammar. ALL(*) is O(n4) in theory but consistently performs linearly on grammars used in practice, outperforming general strategies such as GLL and GLR by orders of magnitude. ANTLR 4 generates ALL(*) parsers and supports direct left-recursion through grammar rewriting. Widespread ANTLR 4 use (5000 downloads/month in 2013) provides evidence that ALL(*) is effective for a wide variety of applications.

We describe an approach for synthesizing data representations for concurrent programs. Our compiler takes as input a program written using concurrent relations and synthesizes a representation of the relations as sets of cooperating data structures as well as the placement and acquisition of locks to synchronize concurrent access to those data structures. The resulting code is correct by construction: individual relational operations are implemented correctly and the aggregate set of operations is serializable and deadlock free. The relational specification also permits a high-level optimizer to choose the best performing of many possible legal data representations and locking strategies, which we demonstrate with an experiment autotuning a graph benchmark.

The goal of the PADS project, which started in 2001, is to make it easier for data analysts to extract useful information from ad hoc data files. This paper does not report new results, but rather gives an overview of the project and how it helps bridge the gap between the unmanaged world of ad hoc data and the managed world of typed programming languages and databases. In particular, the paper reviews the design of PADS data description languages, describes the generated parsing tools and discusses the importance of meta-data. It also sketches the formal semantics, discusses useful tools and how can they can be generated automatically from PADS descriptions, and describes an inferencing system that can learn useful PADS descriptions from positive examples of the data format.

A filestore is a structured collection of data files housed in a conventional hierarchical file system. Many applications use filestores as a poor-man's database, and the correct execution of these applications requires that the collection of files, directories, and symbolic links stored on disk satisfy a variety of precise invariants. Moreover, all of these structures must have acceptable ownership, permission, and timestamp attributes. Unfortunately, current programming languages do not provide support for documenting assumptions about filestores, detecting errors in them, or safely loading from and storing to them.

We consider the problem of specifying combinations of data structures with complex sharing in a manner that is both declarative and results in provably correct code. In our approach, abstract data types are specified using relational algebra and functional dependencies. We describe a language of decompositions that permit the user to specify different concrete representations for relations, and show that operations on concrete representations soundly implement their relational specification. It is easy to incorporate data representations synthesized by our compiler into existing systems, leading to code that is simpler, correct by construction, and comparable in performance to the code it replaces.

Despite the power of Parser Expression Grammars (PEGs) and GLR, parsing is not a solved problem. Adding nondeterminism (parser speculation) to traditional LL and LR parsers can lead to unexpected parse-time behavior and introduces practical issues with error handling, single-step debugging, and side-effecting embedded grammar actions. This paper introduces the LL(*) parsing strategy and an associated grammar analysis algorithm that constructs LL(*) parsing decisions from ANTLR grammars. At parse-time, decisions gracefully throttle up from conventional fixed k>=1 lookahead to arbitrary lookahead and, finally, fail over to backtracking depending on the complexity of the parsing decision and the input symbols. LL(*) parsing strength reaches into the context-sensitive languages, in some cases beyond what GLR and PEGs can express. By statically removing as much speculation as possible, LL(*) provides the expressivity of PEGs while retaining LL's good error handling and unrestricted grammar actions. Widespread use of ANTLR (over 70,000 downloads/year) shows that it is effective for a wide variety of applications.

In the spirit of Landin, we present a calculus of dependent types to serve as the semantic foundation for a family of languages called data description languages . Such languages, which include pads, datascript, and packettypes, are designed to facilitate programming with ad hoc data , that is, data not in well-behaved relational or xml formats. In the calculus, each type describes the physical layout and semantic properties of a data source. In the semantics, we interpret types simultaneously as the in-memory representation of the data described and as parsers for the data source. The parsing functions are robust, automatically detecting and recording errors in the data stream without halting parsing. We show the parsers are type-correct, returning data whose type matches the simple-type interpretation of the specification. We also prove the parsers are “error-correct,” accurately reporting the number of physical and semantic errors that occur in the returned data. We use the calculus to describe the features of various data description languages, and we discuss how we have used the calculus to improve pads.

This paper presents the design, theory and implementation of Gloves, a domain-specific language that allows users to specify the provenance (the derivation history starting from the origins), syntax and semantic properties of collections of distributed data sources. In particular, Gloves specifications indicate where to locate desired data, how to obtain it, when to get it or to give up trying, and what format it will be in on arrival. The Gloves system compiles such specification into a suite of data-processing tools including an archiver, a provenance tracking system, a database loading tool, an alert system, an RSS feed generator and a debugging tool. In addition, the system generates description-specific libraries so that developers can create their own applications. Gloves also provides a generic infrastructure so that advanced users can build new tools applicable to any data source with a Gloves description. We show how Gloves may be used to specify data sources from two domains: CoMon, a monitoring system for PlanetLab's 800+ nodes, and Arrakis, a monitoring system for an AT&T web hosting service. We show experimentally that our system can scale to distributed systems the size of CoMon. Finally, we provide a denotational semantics for Gloves and use this semantics to prove two important theorems. The first shows that our denotational semantics respects the typing rules for the language, while the second demonstrates that our system correctly maintains the provenance.

This special session will present a summary of the recommendations of the First SIGPLAN Workshop on Undergraduate Programming Language Curricula, held at Harvard University in May, 2008. The purpose of the workshop was to generate new recommendations for programming languages topics to be learned by all undergraduate CS majors. In this special session we will present a summary of the curriculum recommendations, why they were made, and ways of incorporating them into undergraduate CS curricula.

An ad hoc data source is any semistructured data source for which useful data analysis and transformation tools are not readily available. Such data must be queried, transformed and displayed by systems administrators, computational biologists, financial analysts and hosts of others on a regular basis. In this paper, we demonstrate that it is possible to generate a suite of useful data processing tools, including a semi-structured query engine, several format converters, a statistical analyzer and data visualization routines directly from the ad hoc data itself, without any human intervention. The key technical contribution of the work is a multi-phase algorithm that automatically infers the structure of an ad hoc data source and produces a format specification in the PADS data description language. Programmers wishing to implement custom data analysis tools can use such descriptions to generate printing and parsing libraries for the data. Alternatively, our software infrastructure will push these descriptions through the PADS compiler, creating format-dependent modules that, when linked with format-independent algorithms for analysis and transformation, result infully functional tools. We evaluate the performance of our inference algorithm, showing it scales linearlyin the size of the training data - completing in seconds, as opposed to the hours or days it takes to write a description by hand. We also evaluate the correctness of the algorithm, demonstrating that generating accurate descriptions often requires less than 5% of theavailable data.

In this demonstration, we will present LEARNPADS, a fully automatic system for generating ad hoc data processing tools. When presented with a collection of ad hoc data, the system (1) analyzes the data, (2) infers a PADS [4, 5] description, (3) generates parser, printer, validation and traversal libraries and (4) links these libraries with format-independent tool suites to form stand-alone applications. These applications provide statistical analysis, XML conversion, CSV conversion, the ability to query with the Galax XQuery engine [3], and the ability to graph selected data elements, all directly from ASCII ad hoc data without human intervention. SIGMOD attendees will see both the user experience with LEARNPADS and the internals of the multi-phase inference algorithm which lies at the heart of the system.

In the spirit of Landin, we present a calculus of dependent types to serve as the semantic foundation for a family of languages called data description languages. Such languages, which include pads, datascript, and packettypes, are designed to facilitate programming with ad hoc data, ie, data not in well-behaved relational or xml formats. In the calculus, each type describes the physical layout and semantic properties of a data source. In the semantics, we interpret types simultaneously as the in-memory representation of the data described and as parsers for the data source. The parsing functions are robust, automatically detecting and recording errors in the data stream without halting parsing. We show the parsers are type-correct, returning data whose type matches the simple-type interpretation of the specification. We also prove the parsers are "error-correct," accurately reporting the number of physical and semantic errors that occur in the returned data. We use the calculus to describe the features of various data description languages, and we discuss how we have used the calculus to improve PADS.

Enormous amounts of data exist in "well-behaved" formats such as relational tables and XML, which come equipped with extensive tool support. However, vast amounts of data also exist in non-standard or ad hoc data formats, which often lack standard or extensible tools. This deficiency forces data analysts to implement their own tools for parsing, querying, and analyzing their ad hoc data. The resulting tools typically interleave parsing, querying, and analysis, obscuring the semantics of the data format and making it nearly impossible for others to resuse the tools. This proposal describes PADS, an end-to-end system for processing ad hoc data sources. The core of PADS is a declarative language for describing ad hoc data sources and a data-description compiler that produces customizable libraries for parsing the ad hoc data. A suite of tools built around this core includes statistical data-profiling tools, a query engine that permits viewing ad hoc sources as XML and for querying them with XQuery, and an interactive front-end that helps users produce PADS descriptions quickly.

The Ninth ACM SIGPLAN International Conference on Functional Programming (ICFP) took place on September 19–21 2004 in Snowbird, Utah. The scope of ICFP includes all languages that encourage programming with functions, with topics ranging from principles to practice, foundations to features, and abstractions to applications. The program committee, which I chaired, selected 21 papers from 80 submissions for presentation at the conference. Afterwards, the program committee invited the authors of nine papers to submit extended versions for this special issue of JFP. The seven papers that appear in this volume were reviewed, revised, and accepted following standard Journal of Functional Programming procedures. Reflecting the scope of the conference, the papers cover the theoretical foundations, implementation, design, programming techniques, and applications of functional languages.

Current languages allow a programmer to describe an interface only by enumerating its parts, possibly including other interfaces wholesale. Such languages cannot express relationships between interfaces, yet when independently developed software components are combined into a larger system, significant relationships arise.To address this shortcoming, we define, as a conservative extension of ML, a language for manipulating interfaces. Our language includes operations for adding, renaming, and removing components; for changing the type associated with a value; for making manifest types abstract and vice versa; and for combining interfaces. These operations can express useful relationships among interfaces. We have defined a formal semantics in which an interface denotes a group of four sets; we show how these sets determine a subtyping relation, and we sketch the elaboration of an interface into its denotation.

PADS is a declarative data description language that allows data analysts to describe both the physical layout of ad hoc data sources and semantic properties of that data. From such descriptions, the PADS compiler generates libraries and tools for manipulating the data, including parsing routines, statistical profiling tools, translation programs to produce well-behaved formats such as Xml or those required for loading relational databases, and tools for running XQueries over raw PADS data sources. The descriptions are concise enough to serve as "living" documentation while flexible enough to describe most of the ASCII, binary, and Cobol formats that we have seen in practice. The generated parsing library provides for robust, application-specific error handling.

Massive transaction streams present a number of opportunities for data mining techniques. The transactions in such streams might represent calls on a telephone network, commercial credit card purchases, stock market trades, or HTTP requests to a web server. While historically such data have been collected for billing or security purposes, they are now being used to discover how the transactors, for example, credit-card numbers or IP addresses, use the associated services.Over the past 5 years, we have computed evolving profiles (called signatures ) of transactors in several very large data streams. The signature for each transactor captures the salient features of his or her behavior through time. Programs for processing signatures must be highly optimized because of the size of the data stream (several gigabytes per day) and the number of signatures to maintain (hundreds of millions). Originally, we wrote such programs directly in C, but because these programs often sacrificed readability for performance, they were difficult to verify and maintain.Hancock is a domain-specific language we created to express computationally efficient signature programs cleanly. In this paper, we describe the obstacles to computing signatures from massive streams and explain how Hancock addresses these problems. For expository purposes, we present Hancock using a running example from the telecommunications industry; however, the language itself is general and applies equally well to other data sources.

The Eighth International Workshop on Foundations of Object-Oriented Languages (FOOL 8) was held on January 20, 2001, colocated with the ACM Symposium on Principles of Programming Languages in London. Kathleen Fisher chaired the program committee. Six contributed papers were presented. After the workshop, extended versions of three were solicited for this special issue of TOPLAS ; the two articles that were ultimately submitted were reviewed, revised, and accepted following standard TOPLAS procedures.The special issue opens with Type-Preserving Compilation of Featherweight Java by Christopher League, Zhong Shao, and Valery Trifonov. The goal of the authors is to establish a foundation for certifying compilation of Java-like class-based languages. To this end, the authors give an encoding of core Java features in a typed intermediate language suitable for use within a type-preserving compiler. Because of its intended use, the authors focus on developing an efficient encoding. They show that the type erasure of their implementation matches the standard vtable self-application semantics of message sending.The authors use Featherweight Java (FJ), which models the core features of Java, as their source language. Their translation targets a variant of F ω , already implemented as part of the SML/NJ compiler, using its row polymorphism and existential and recursive types to encode FJ. The authors show that the translation from FJ to their target preserves types.In the issue's second article, More Dynamic Object Reclassification: Fickle II , Sophia Drossopoulou, Ferruccio Damiani, Mariangiola Dezani-Ciancaglini, and Paola Giannini explore the issue of dynamic reclassification, by which an object changes its class membership at runtime while retaining its identity. This ability helps model real-world situations where an object has different roles over time---for example, a person who is first a student and then graduates to become a teacher.The authors focus their study on the design of a language, Fickle II , which extends an imperative, typed, class-based, object-oriented language with a reclassification operation. To specify the behavior of their language, the authors give an operational semantics. They then develop a type and effect system and show that the type system is sound, in the sense that a well-typed program cannot get stuck under the operational semantics.We would like to thank our colleagues who served on the FOOL 8 program committee and those who participated in the anonymous reviewing process for TOPLAS . Their efforts contributed greatly to the quality of this special issue.

Article Hancock: a language for extracting signatures from data streams Share on Authors: Corinna Cortes AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJ AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJView Profile , Kathleen Fisher AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJ AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJView Profile , Daryl Pregibon AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJ AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJView Profile , Anne Rogers AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJ AT&T Labs-Research, Shannon Laboratory, 180 Park Avenue, Florham Park, NJView Profile Authors Info & Claims KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data miningAugust 2000 Pages 9–17https://doi.org/10.1145/347090.347094Online:01 August 2000Publication History 102citation888DownloadsMetricsTotal Citations102Total Downloads888Last 12 Months4Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We present a set-based control flow analysis for an imperative, concurrent object calculus extending the Fisher-Honsell-Mitchell functional object-oriented calculus described in Fisher, Honsell and Mitchell, (1993). The analysis is shown to be sound with respect to a transition-system semantics.

Typical class-based languages, such as C++ and JAVA, provide complex class mechanisms but only weak module systems. In fact, classes in these languages incorporate many of the features found in richer module mechanisms. In this paper, we describe an alternative approach to designing a language that has both classes and modules. In our design, we rely on a rich ML-style module system to provide features such as visibility control and parameterization, while providing a minimal class mechanism that includes only those features needed to support inheritance. Programmers can then use the combination of modules and classes to implement the full range of class-based features and idioms. Our approach has the advantage that it provides a full-featured module system (useful in its own right), while keeping the class mechanism quite simple.We have incorporated this design in MOBY, which is an ML-style language that supports class-based object-oriented programming. In this paper, we describe our design via a series of simple examples, show how various class-based features and idioms are realized in MOBY, compare our design with others, and sketch its formal semantics.

An untyped lambda calculus, extended with object primitives that reflect the capabilities of so-called delegation-based object-oriented languages, is presented. A type inference system allows static detection of errors, such as message not understood, while at the same time allowing the type of an inherited method to be specialized to the type of the inheriting object. Type soundness, in the form of a subject-reduction theorem, is proved, and examples illustrating the expressiveness of the pure calculus are presented.>