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检索条件"任意字段=Proceedings of the ACM SIGPLAN 1993 conference on Programming language design and implementation"

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TRANSIT: Specifying Protocols with Concolic Snippets

TRANSIT: Specifying Protocols with Concolic Snippets

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34th acm sigplan conference on programming language design and implementation (PLDI)

作者： Udupa, Abhishek Raghavan, Arun Deshmukh, Jyotirmoy V. Mador-Haim, Sela Martin, Milo M. K. Alur, Rajeev Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9781450320146

With the maturing of technology for model checking and constraint solving, there is an emerging opportunity to develop programming tools that can transform the way systems are specified. In this paper, we propose a new way to program distributed protocols using concolic snippets. Concolic snippets are sample execution fragments that contain both concrete and symbolic values. The proposed approach allows the programmer to describe the desired system partially using the traditional model of communicating extended finite-state-machines (EFSM), along with high-level invariants and concrete execution fragments. Our synthesis engine completes an EFSM skeleton by inferring guards and updates from the given fragments which is then automatically analyzed using a model checker with respect to the desired invariants. The counterexamples produced by the model checker can then be used by the programmer to add new concrete execution fragments that describe the correct behavior in the specific scenario corresponding to the counterexample. We describe TRANSIT, a language and prototype implementation of the proposed specification methodology for distributed protocols. Experimental evaluations of TRANSIT to specify cache coherence protocols show that (1) the algorithm for expression inference from concolic snippets can synthesize expressions of size 15 involving typical operators over commonly occurring types, (2) for a classical directory-based protocol, TRANSIT automatically generates, in a few seconds, a complete implementation from a specification consisting of the EFSM structure and a few concrete examples for every transition, and (3) a published partial description of the SGI Origin cache coherence protocol maps directly to symbolic examples and leads to a complete implementation in a few iterations, with the programmer correcting counterexamples resulting from underspecified transitions by adding concrete examples in each iteration.

关键词： Program Synthesis Distributed Protocol Synthesis Cache Coherence Protocols programming by Example

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Test-Driven Synthesis 14

Test-Driven Synthesis

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35th acm sigplan conference on programming language design and implementation (PLDI)

作者： Perelman, Daniel Gulwani, Sumit Grossman, Dan Provost, Peter Univ Washington Seattle WA 98195 USA Microsoft Corp Mountain View CA USA

ISBN: (纸本)9781450327848

programming-by-example technologies empower end-users to create simple programs merely by providing input/output examples. Existing systems are designed around solvers specialized for a specific set of data types or domain-specific language (DSL). We present a program synthesizer which can be parameterized by an arbitrary DSL that may contain conditionals and loops and therefore is able to synthesize programs in any domain. In order to use our synthesizer, the user provides a sequence of increasingly sophisticated input/output examples along with an expert-written DSL definition. These two inputs correspond to the two key ideas that allow our synthesizer to work in arbitrary domains. First, we developed a novel iterative synthesis technique inspired by test-driven development-which also gives our technique the name of test-driven synthesis-where the input/output examples are consumed one at a time as the program is refined. Second, the DSL allows our system to take an efficient component-based approach to enumerating possible programs. We present applications of our synthesis methodology to end-user programming for transformations over strings, XML, and table layouts. We compare our synthesizer on these applications to state-of-the-art DSL-specific synthesizers as well to the general purpose synthesizer Sketch.

关键词： languages Experimentation program synthesis end-user programming test driven development

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Gillian, Part I: A Multi-language Platform for Symbolic Execution 2020

Gillian, Part I: A Multi-language Platform for Symbolic Exec...

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41st acm sigplan conference on programming language design and implementation (PLDI)

作者： Santos, Jose Fragoso Maksimovic, Petar Ayoun, Sacha-Elie Gardner, Philippa Univ Lisbon INESC ID Inst Super Tecn Lisbon Portugal Imperial Coll London London England

ISBN: (纸本)9781450376136

We introduce Gillian, a platform for developing symbolic analysis tools for programming languages. Here, we focus on the symbolic execution engine at the heart of Gillian, which is parametric on the memory model of the target language. We give a formal description of the symbolic analysis and a modular implementation that closely follows this description. We prove a parametric soundness result, introducing restriction on abstract states, which generalises path conditions used in classical symbolic execution. We instantiate Gillian to obtain trusted symbolic testing tools for JavaScript and C, and use these tools to find bugs in real-world code, thus demonstrating the viability of our parametric approach.

关键词： Symbolic execution parametric semantics bug-finding bounded verification JavaScript C

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SCENIC: A language for Scenario Specification and Scene Generation 2019

SCENIC: A Language for Scenario Specification and Scene Gene...

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40th acm sigplan conference on programming language design and implementation (PLDI) part of acm's Federated Computing Research conference (FCRC)

作者： Fremont, Daniel J. Dreossi, Tommaso Ghosh, Shromona Yue, Xiangyu Sangiovanni-Vincentelli, Alberto L. Seshia, Sanjit A. Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781450367127

We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to handle rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs and sampling these to generate specialized training and test sets. More generally, such languages can be used for cyber-physical systems and robotics to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems like autonomous cars and robots, whose environment is a scene, a configuration of physical objects and agents. We design a domain-specific language, Scenic, for describing scenarios that are distributions over scenes. As a probabilistic programming language, Scenic allows assigning distributions to features of the scene, as well as declaratively imposing hard and soft constraints over the scene. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided by Scenic's domain-specific syntax. Finally, we apply Scenic in a case study on a convolutional neural network designed to detect cars in road images, improving its performance beyond that achieved by state-of-the-art synthetic data generation methods.

关键词： scenario description language synthetic data deep learning probabilistic programming automatic test generation fuzz testing

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A General Path-Based Representation for Predicting Program Properties 2018

A General Path-Based Representation for Predicting Program P...

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39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Alon, Uri Zilberstein, Meital Levy, Omer Yahav, Eran Technion Haifa Israel Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781450356985

Predicting program properties such as names or expression types has a wide range of applications. It can ease the task of programming, and increase programmer productivity. A major challenge when learning from programs is how to represent programs in a way that facilitates effective learning. We present a general path-based representation for learning from programs. Our representation is purely syntactic and extracted automatically. The main idea is to represent a program using paths in its abstract syntax tree (AST). This allows a learning model to leverage the structured nature of code rather than treating it as a flat sequence of tokens. We showthat this representation is general and can: (i) cover different prediction tasks, (ii) drive different learning algorithms (for both generative and discriminative models), and (iii) work across different programming languages. We evaluate our approach on the tasks of predicting variable names, method names, and full types. We use our representation to drive both CRF-based and word2vec-based learning, for programs of four languages: JavaScript, Java, Python and C#. Our evaluation shows that our approach obtains better results than task-specific handcrafted representations across different tasks and programming languages.

关键词： programming languages Big Code Machine Learning Learning Representations

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Green: A Framework for Supporting Energy-Conscious programming using Controlled Approximation 10

Green: A Framework for Supporting Energy-Conscious Programmi...

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acm sigplan conference on programming language design and implementation

作者： Baek, Woongki Chilimbi, Trishul M. Stanford Univ Comp Syst Lab Stanford CA 94305 USA Microsoft Res Redmond WA 98052 USA

ISBN: (纸本)9781450300193

Energy-efficient computing is important in several systems ranging from embedded devices to large scale data centers. Several application domains offer the opportunity to tradeoff quality of service/solution (QoS) for improvements in performance and reduction in energy consumption. Programmers sometimes take advantage of such opportunities, albeit in an ad-hoc manner and often without providing any QoS guarantees. We propose a system called Green that provides a simple and flexible framework that allows programmers to take advantage of such approximation opportunities in a systematic manner while providing statistical QoS guarantees. Green enables programmers to approximate expensive functions and loops and operates in two phases. In the calibration phase, it builds a model of the QoS loss produced by the approximation. This model is used in the operational phase to make approximation decisions based on the QoS constraints specified by the programmer. The operational phase also includes an adaptation function that occasionally monitors the runtime behavior and changes the approximation decisions and QoS model to provide strong statistical QoS guarantees. To evaluate the effectiveness of Green, we implemented our system and language extensions using the Phoenix compiler framework. Our experiments using benchmarks from domains such as graphics, machine learning, signal processing, and finance, and an in-production, real-world web search engine, indicate that Green can produce significant improvements in performance and energy consumption with small and controlled QoS degradation.

关键词： Performance Measurement languages designs Energy-Conscious programming Controlled Approximation

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Module-sensitive program specialization

Module-sensitive program specialization

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proceedings of the 1997 acm SIGPLAM conference on programming language design and implementation, PLDI

作者： Dussart, Dirk Heldal, Rogardt Hughes, John

We present an approach for specializing large programs, such as programs consisting of several modules, or libraries. This approach is based on the idea of using a compiler generator (cogen) for creating generating extensions. Generating extensions are specializers specialized with respect to some input program. When run on some input data the generating extension produces a specialized version of the input program. Here we use the cogen to tailor modules for specialization. This happens once and for all, independently of all other modules. The resulting module can then be used as a building block for generating extensions for complete programs, in much the same way as the original modules can be put together into complete programs. The result of running the final generating extension is a collection of residual modules, with a module structure derived from the original program.

关键词： Computer programming

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Lightweight, Flexible Object-Oriented Generics 15

Lightweight, Flexible Object-Oriented Generics

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36th acm sigplan conference on programming language design and implementation

作者： Zhang, Yizhou Loring, Matthew C. Salvaneschi, Guido Liskov, Barbara Myers, Andrew C. Cornell Univ Ithaca NY 14853 USA Tech Univ Darmstadt Darmstadt Germany MIT Cambridge MA 02139 USA

ISBN: (纸本)9781450334686

The support for generic programming in modern object-oriented programming languages is awkward and lacks desirable expressive power. We introduce an expressive genericity mechanism that adds expressive power and strengthens static checking, while remaining lightweight and simple in common use cases. Like type classes and concepts, the mechanism allows existing types to model type constraints retroactively. For expressive power, we expose models as named constructs that can be defined and selected explicitly to witness constraints;in common uses of genericity, however, types implicitly witness constraints without additional programmer effort. Models are integrated into the object-oriented style, with features like model generics, model-dependent types, model enrichment, model multimethods, constraint entailment, model inheritance, and existential quantification further extending expressive power in an object-oriented setting. We introduce the new genericity features and show that common generic programming idioms, including current generic libraries, can be expressed more precisely and concisely. The static semantics of the mechanism and a proof of a key decidability property can be found in an associated technical report.

关键词： Genus generic programming constraints models

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Latte: A language, Compiler, and Runtime for Elegant and Efficient Deep Neural Networks 16

Latte: A Language, Compiler, and Runtime for Elegant and Eff...

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37th acm sigplan conference on programming language design and implementation

作者： Truong, Leonard Barik, Rajkishore Totoni, Ehsan Liu, Hai Markley, Chick Fox, Armando Shpeisman, Tatiana Univ Calif Berkeley Intel Labs Berkeley CA USA Intel Labs Berkeley CA USA Univ Calif Berkeley Berkeley CA USA

ISBN: (纸本)9781450342612

Deep neural networks (DNNs) have undergone a surge in popularity with consistent advances in the state of the art for tasks including image recognition, natural language processing, and speech recognition. The computationally expensive nature of these networks has led to the proliferation of implementations that sacrifice abstraction for high performance. In this paper, we present Latte, a domain-specific language for DNNs that provides a natural abstraction for specifying new layers without sacrificing performance. Users of Latte express DNNs as ensembles of neurons with connections between them. The Latte compiler synthesizes a program based on the user specification, applies a suite of domain-specific and general optimizations, and emits efficient machine code for heterogeneous architectures. Latte also includes a communication runtime for distributed memory data-parallelism. Using networks described using Latte, we demonstrate 3-6x speedup over Caffe (C++/MKL) on the three state-of-the-art ImageNet models executing on an Intel Xeon E5-2699 v3 x86 CPU.

关键词： Deep Learning Neural Networks Domain Specific language Compiler Optimization

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To-Many or To-One? All-in-One! Efficient Purely Functional Multi-maps with Type-Heterogeneous Hash-Tries 2018

To-Many or To-One? All-in-One! Efficient Purely Functional M...

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39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Steindorfer, Michael J. Vinju, Jurgen J. Delft Univ Technol Delft Netherlands Ctr Wiskunde & Informat Amsterdam Netherlands TU Eindhoven Eindhoven Netherlands

ISBN: (纸本)9781450356985

An immutable multi-map is a many-to-many map data structure with expected fast insert and lookup operations. This data structure is used for applications processing graphs or many-to-many relations as applied in compilers, runtimes of programming languages, or in static analysis of object-oriented systems. Collection data structures are assumed to carefully balance execution time of operations with memory consumption characteristics and need to scale gracefully from a few elements to multiple gigabytes at least. When processing larger in-memory data sets the overhead of the data structure encoding itself becomes a memory usage bottleneck, dominating the overall performance. In this paper we propose AXIOM, a novel hash-trie data structure that allows for a highly efficient and type-safe multi-map encoding by distinguishing inlined values of singleton sets from nested sets of multi-mappings. AXIOM strictly generalizes over previous hash-trie data structures by supporting the processing of fine-grained type-heterogeneous content on the implementation level (while API and language support for type-heterogeneity are not scope of this paper). We detail the design and optimizations of AXIOM and further compare it against state-of-the-art immutable maps and multi-maps in Java, Scala and Clojure. We isolate key differences using microbenchmarks and validate the resulting conclusions on a case study in static analysis. AXIOM reduces the key-value storage overhead by 1.87 x;with specializing and inlining across collection boundaries it improves by 5.1 x.

关键词： Data structures persistent data structures functional programming hashtable multi-map many-to-many relation graph optimization performance JVM

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