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Software meta-language engineering and CBS

JOURNAL OF COMPUTER languageS 2019年 50卷 39-48页

作者： Mosses, Peter D. Swansea Univ Dept Comp Sci Computat Foundry Bay Campus Swansea SA1 8EN W Glam Wales Delft Univ Technol Programming Languages EEMCS POB 5031 NL-2600 GA Delft Netherlands

The SLE conference series is devoted to the engineering principles of software languages: their design, their implementation, and their evolution. This paper is about the role of language specification in SLE. A precise specification of a software language needs to be written in a formal meta-language, and it needs to co-evolve with the specified language. Moreover, different software languages often have features in common, which should provide opportunities for reuse of parts of language specifications. Support for co-evolution and reuse in a meta-language requires careful engineering of its design. The author has been involved in the development of several meta-languages for semantic specification, including action semantics and modular variants of structural operational semantics (MSOS, I-MSOS). This led to the PLanCompS project, and to the design of its meta-language, CBS, for component-based semantics. CBS comes together with an extensible library of reusable components called 'funcons', corresponding to fundamental programming constructs. The main aim of CBS is to optimise co-evolution and reuse of specifications during language development, and to make specification of language semantics almost as straightforward as context-free syntax specification. The paper discusses the engineering of a selection of previous meta-languages, assessing how well they support co-evolution and reuse. It then gives an introduction to CBS, and illustrates significant features. It also considers whether other current meta-languages might also be used to define an extensible library of funcons for use in component-based semantics.

关键词： Semantics of programming languages Meta-languages Modularity

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FaCT: A DSL for Timing-Sensitive Computation 2019

FaCT: A DSL for Timing-Sensitive Computation

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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： Cauligi, Sunjay Soeller, Gary Johannesmeyer, Brian Brown, Fraser Wahby, Riad S. Renner, John Gregoire, Benjamin Barthe, Gilles Jhala, Ranjit Stefan, Deian Univ Calif San Diego La Jolla CA 92093 USA INRIA Sophia Antipolis Biot France MPI Secur & Privacy Saarbrucken Germany IMDEA Software Inst Madrid Spain

ISBN: (纸本)9781450367127

Real-world cryptographic code is often written in a subset of C intended to execute in constant-time, thereby avoiding timing side channel vulnerabilities. This C subset eschews structured programming as we know it: if-statements, looping constructs, and procedural abstractions can leak timing information when handling sensitive data. The resulting obfuscation has led to subtle bugs, even in widely-used high-profile libraries like OpenSSL. To address the challenge of writing constant-time cryptographic code, we present FaCT, a crypto DSL that provides high-level but safe language constructs. The FaCT compiler uses a secrecy type system to automatically transform potentially timing-sensitive high-level code into low-level, constant-time LLVM bitcode. We develop the language and type system, formalize the constant-time transformation, and present an empirical evaluation that uses FaCT to implement core crypto routines from several open-source projects including OpenSSL, libsodium, and curve25519-donna. Our evaluation shows that FaCT's design makes it possible to write readable, high-level cryptographic code, with efficient, constant-time behavior.

关键词： domain-specific language program transformation cryptography

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DBPL 2019 - proceedings of the 17th acm sigplan International Symposium on Database programming languages, co-located with PLDI 2019

DBPL 2019 - Proceedings of the 17th ACM SIGPLAN Internationa...

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17th acm sigplan International Symposium on Database programming languages, DBPL 2019, co-located with PLDI 2019

ISBN: (纸本)9781450367189

The proceedings contain 8 papers. The topics discussed include: fluid data structures;detecting unsatisfiable CSS rules in the presence of DTDs;towards compiling graph queries in relational engines;streaming saturation for large RDF graphs with dynamic schema information;arc: an IR for batch and stream programming;on the semantics of cypher’s implicit group-by;mixing set and bag semantics;and language-integrated provenance by trace analysis.

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Compiling KB-Sized Machine Learning Models to Tiny IoT Devices 2019

Compiling KB-Sized Machine Learning Models to Tiny IoT Devic...

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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： Gopinath, Sridhar Ghanathe, Nikhil Seshadri, Vivek Sharma, Rahul Microsoft Res Bengaluru India

ISBN: (纸本)9781450367127

Recent advances in machine learning (ML) have produced KiloByte-size models that can directly run on constrained IoT devices. This approach avoids expensive communication between IoT devices and the cloud, thereby enabling energy-efficient real-time analytics. However, ML models are expressed typically in floating-point, and IoT hardware typically does not support floating-point. Therefore, running these models on IoT devices requires simulating IEEE-754 floating-point using software, which is very inefficient. We present SeeDot, a domain-specific language to express ML inference algorithms and a compiler that compiles SeeDot programs to fixed-point code that can efficiently run on constrained IoT devices. We propose 1) a novel compilation strategy that reduces the search space for some key parameters used in the fixed-point code, and 2) new efficient implementations of expensive operations. SeeDot compiles state-of-the-art KB-sized models to various microcontrollers and low-end FPGAs. We show that SeeDot outperforms 1) software emulation of floating-point (Arduino), 2) high-bitwidth fixed-point (MATLAB), 3) post-training quantization (TensorFlow-Lite), and 4) floating- and fixed-point FPGA implementations generated using high-level synthesis tools.

关键词： Machine Learning IoT device programming language Compiler Fixed-point Microcontroller FPGA

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Lightweight Multi-language Syntax Transformation with Parser Parser Combinators 2019

Lightweight Multi-Language Syntax Transformation with Parser...

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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： van Tonder, Rijnard Le Goues, Claire Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA

ISBN: (纸本)9781450367127

Automatically transforming programs is hard, yet critical for automated program refactoring, rewriting, and repair. Multi-language syntax transformation is especially hard due to heterogeneous representations in syntax, parse trees, and abstract syntax trees (ASTs). Our insight is that the problem can be decomposed such that (1) a common grammar expresses the central context-free language (CFL) properties shared by many contemporary languages and (2) open extension points in the grammar allow customizing syntax (e.g., for balanced delimiters) and hooks in smaller parsers to handle language-specific syntax (e.g., for comments). Our key contribution operationalizes this decomposition using a Parser Parser combinator (PPC), a mechanism that generates parsers for matching syntactic fragments in source code by parsing declarative user-supplied templates. This allows our approach to detach from translating input programs to any particular abstract syntax tree representation, and lifts syntax rewriting to a modularly-defined parsing problem. A notable effect is that we skirt the complexity and burden of defining additional translation layers between concrete user input templates and an underlying abstract syntax representation. We demonstrate that these ideas admit efficient and declarative rewrite templates across 12 languages, and validate effectiveness of our approach by producing correct and desirable lightweight transformations on popular real-world projects (over 50 syntactic changes produced by our approach have been merged into 40+). Our declarative rewrite patterns require an order of magnitude less code compared to analog implementations in existing, language-specific tools.

关键词： syntax transformation parsers rewriting

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SEMCLUSTER: Clustering of Imperative programming Assignments Based on Quantitative Semantic Features 2019

SEMCLUSTER: Clustering of Imperative Programming Assignments...

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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： Perry, David M. Kim, Dohyeong Samanta, Roopsha Zhang, Xiangyu Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781450367127

A fundamental challenge in automated reasoning about programming assignments at scale is clustering student submissions based on their underlying algorithms. State-of-the-art clustering techniques are sensitive to control structure variations, cannot cluster buggy solutions with similar correct solutions, and either require expensive pair-wise program analyses or training efforts. We propose a novel technique that can cluster small imperative programs based on their algorithmic essence: (A) how the input space is partitioned into equivalence classes and (B) how the problem is uniquely addressed within individual equivalence classes. We capture these algorithmic aspects as two quantitative semantic program features that are merged into a program's vector representation. Programs are then clustered using their vector representations. The computation of our first semantic feature leverages model counting to identify the number of inputs belonging to an input equivalence class. The computation of our second semantic feature abstracts the program's data flow by tracking the number of occurrences of a unique pair of consecutive values of a variable during its lifetime. The comprehensive evaluation of our tool SemCluster on benchmarks drawn from solutions to small programming assignments shows that SemCluster (1) generates far fewer clusters than other clustering techniques, (2) precisely identifies distinct solution strategies, and (3) boosts the performance of clustering-based program repair, all within a reasonable amount of time.

关键词： Program clustering Program analysis Quantitative reasoning

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ARRAY 2019 - proceedings of the 6th acm sigplan International Workshop on Libraries, languages and Compilers for Array programming, co-located with PLDI 2019

ARRAY 2019 - Proceedings of the 6th ACM SIGPLAN Internationa...

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6th acm sigplan International Workshop on Libraries, languages and Compilers for Array programming, ARRAY 2019, co-located with PLDI 2019

ISBN: (纸本)9781450367172

The proceedings contain 9 papers. The topics discussed include: finite difference methods fengshui: alignment through a mathematics of arrays;data-parallel flattening by expansion;ALPyNA: acceleration of loops in python for novel architectures;high-level synthesis of functional patterns with lift;toward generalized tensor algebra for ab initio quantum chemistry methods;TeIL: a type-safe imperative tensor intermediate language;convolutional neural networks in APL;records with rank polymorphism;and linear algebraic depth-first search. © 2019 Association for Computing Machinery. All rights reserved.

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programming Support for Autonomizing Software 2019

Programming Support for Autonomizing Software

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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： Lee, Wen-Chuan Liu, Peng Liu, Yingqi Ma, Shiqing Zhang, Xiangyu Purdue Univ W Lafayette IN 47907 USA IBM Res San Jose CA USA

ISBN: (纸本)9781450367127

Most traditional software systems are not built with the artificial intelligence support (AI) in mind. Among them, some may require human interventions to operate, e.g., the manual specification of the parameters in the data processing programs, or otherwise, would behave poorly. We propose a novel framework called Autonomizer to autonomize these systems by installing the AI into the traditional programs. Autonomizer is general so it can be applied to many real-world applications. We provide the primitives and the runtime support, where the primitives abstract common tasks of autonomization and the runtime support realizes them transparently. With the support of Autonomizer, the users can gain the AI support with little engineering efforts. Like many other AI applications, the challenge lies in the feature selection, which we address by proposing multiple automated strategies based on the program analysis. Our experiment results on nine real-world applications show that the autonomization only requires adding a few lines to the source code. Besides, for the data-processing programs, Autonomizer improves the output quality by 161% on average over the default settings. For the interactive programs such as game/driving, Autonomizer achieves higher success rate with lower training time than existing autonomized programs.

关键词： AI Deep Learning Software Autonomization Dynamic Program Analysis

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Piranha: Reducing Feature Flag Debt at Uber 42

Piranha: Reducing Feature Flag Debt at Uber

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42nd acm/IEEE International conference on Software Engineering - Companion proceedings (ICSE-Companion) / 42nd acm/IEEE International conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP)

作者： Ramanathan, Murali Krishna Clapp, Lazaro Barik, Rajkishore Sridharan, Manu Uber Technol Inc New York NY 10001 USA Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9781450371230

Feature flags are commonly used in mobile app development and can introduce technical debt related to deleting their usage from the codebase. This can adversely affect the overall reliability of the apps and increase their maintenance complexity. Reducing this debt without imposing additional overheads on the developers necessitates the design of novel tools and automated workflows. In this paper, we describe the design and implementation of PIRANHA, an automated code refactoring tool which is used to automatically generate differential revisions (a.k.a diffs) to delete code corresponding to stale feature flags. PIRANHA takes as input the name of the flag, expected treatment behavior, and the name of the flag's author. It analyzes the ASTs of the program to generate appropriate refactorings which are packaged into a diff. The diff is assigned to the author of the flag for further processing, who can land it after performing any additional refactorings. We have implemented PIRANHA to delete code in Objective-C, Java, and Swift programs, and deployed it to handle stale flags in multiple Uber apps. We present our experiences with the deployment of PIRANHA from Dec 2017 to May 2019, including the following highlights: (a) generated code cleanup diffs for 1381 flags (17% of total flags), (b) 65% of the diffs landed without any changes, (c) over 85% of the generated diffs compile and pass tests successfully, (d) around 80% of the diffs affect more than one file, (e) developers process more than 88% of the generated diffs, (f) 75% of the generated diffs are processed within a week, and (g) PIRANHA diffs have been interacted with by similar to 200 developers across Uber. Piranha is available as open source at https://***/uber/piranha.

关键词： C (programming language)

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Demo: Light-weight programming language for blockchain 17

Demo: Light-weight programming language for blockchain

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17th acm International conference on Mobile Systems, Applications, and Services, MobiSys 2019

作者： Kim, Junhui Kim, Joongheon Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781450366618

This demo abstract introduces a new light-weight programming language koa which is suitable for blockchain system design and implementation. In this abstract, the basic features of koa are introduced including working system (with playground), architecture, and virtual machine operations. Rum-time execution of software implemented by koa will be presented during the session. © 2019 Copyright held by the owner/author(s).

关键词： Smart contract

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