Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation最新文献_第6页

A typed, algebraic approach to parsing 一种类型化的代数解析方法

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-06-08 DOI: 10.1145/3314221.3314625

N. Krishnaswami, J. Yallop

引用次数: 21

Modular divide-and-conquer parallelization of nested loops 嵌套循环的模块化分治并行化

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-06-08 DOI: 10.1145/3314221.3314612

Azadeh Farzan, Victor Nicolet

引用次数: 16

Scalable taint specification inference with big code 使用大代码进行可伸缩的污染规范推断

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-06-08 DOI: 10.1145/3314221.3314648

Victor Chibotaru, Benjamin Bichsel, Veselin Raychev, Martin T. Vechev

{"title":"Scalable taint specification inference with big code","authors":"Victor Chibotaru, Benjamin Bichsel, Veselin Raychev, Martin T. Vechev","doi":"10.1145/3314221.3314648","DOIUrl":"https://doi.org/10.1145/3314221.3314648","url":null,"abstract":"We present a new scalable, semi-supervised method for inferring taint analysis specifications by learning from a large dataset of programs. Taint specifications capture the role of library APIs (source, sink, sanitizer) and are a critical ingredient of any taint analyzer that aims to detect security violations based on information flow. The core idea of our method is to formulate the taint specification learning problem as a linear optimization task over a large set of information flow constraints. The resulting constraint system can then be efficiently solved with state-of-the-art solvers. Thanks to its scalability, our method can infer many new and interesting taint specifications by simultaneously learning from a large dataset of programs (e.g., as found on GitHub), while requiring few manual annotations. We implemented our method in an end-to-end system, called Seldon, targeting Python, a language where static specification inference is particularly hard due to lack of typing information. We show that Seldon is practically effective: it learned almost 7,000 API roles from over 210,000 candidate APIs with very little supervision (less than 300 annotations) and with high estimated precision (67%). Further, using the learned specifications, our taint analyzer flagged more than 20,000 violations in open source projects, 97% of which were undetectable without the inferred specifications.","PeriodicalId":441774,"journal":{"name":"Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"33 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114013853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Generating piecewise-regular code from irregular structures 从不规则结构生成分段规则代码

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-06-08 DOI: 10.1145/3314221.3314615

T. Augustine, Janarthanan Sarma, L. Pouchet, Gabriel Rodríguez

引用次数: 28

Gen: a general-purpose probabilistic programming system with programmable inference Gen:具有可编程推理的通用概率规划系统

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-06-08 DOI: 10.1145/3314221.3314642

Marco F. Cusumano-Towner, Feras A. Saad, Alexander K. Lew, Vikash K. Mansinghka

{"title":"Gen: a general-purpose probabilistic programming system with programmable inference","authors":"Marco F. Cusumano-Towner, Feras A. Saad, Alexander K. Lew, Vikash K. Mansinghka","doi":"10.1145/3314221.3314642","DOIUrl":"https://doi.org/10.1145/3314221.3314642","url":null,"abstract":"Although probabilistic programming is widely used for some restricted classes of statistical models, existing systems lack the flexibility and efficiency needed for practical use with more challenging models arising in fields like computer vision and robotics. This paper introduces Gen, a general-purpose probabilistic programming system that achieves modeling flexibility and inference efficiency via several novel language constructs: (i) the generative function interface for encapsulating probabilistic models; (ii) interoperable modeling languages that strike different flexibility/efficiency trade-offs; (iii) combinators that exploit common patterns of conditional independence; and (iv) an inference library that empowers users to implement efficient inference algorithms at a high level of abstraction. We show that Gen outperforms state-of-the-art probabilistic programming systems, sometimes by multiple orders of magnitude, on diverse problems including object tracking, estimating 3D body pose from a depth image, and inferring the structure of a time series.","PeriodicalId":441774,"journal":{"name":"Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131362214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 135

Toward efficient gradual typing for structural types via coercions 通过矫顽力实现结构类型的高效渐进式分型

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-06-08 DOI: 10.1145/3314221.3314627

Andre Kuhlenschmidt, Deyaaeldeen Almahallawi, Jeremy G. Siek

{"title":"Toward efficient gradual typing for structural types via coercions","authors":"Andre Kuhlenschmidt, Deyaaeldeen Almahallawi, Jeremy G. Siek","doi":"10.1145/3314221.3314627","DOIUrl":"https://doi.org/10.1145/3314221.3314627","url":null,"abstract":"Gradual typing combines static and dynamic typing in the same program. Siek et al. (2015) describe five criteria for gradually typed languages, including type soundness and the gradual guarantee. A significant number of languages have been developed in academia and industry that support some of these criteria (TypeScript, Typed Racket, Safe TypeScript, Transient Reticulated Python, Thorn, etc.) but relatively few support all the criteria (Nom, Gradualtalk, Guarded Reticulated Python). Of those that do, only Nom does so efficiently. The Nom experiment shows that one can achieve efficient gradual typing in languages with only nominal types, but many languages have structural types: function types, tuples, record and object types, generics, etc. In this paper we present a compiler, named Grift, that addresses the difficult challenge of efficient gradual typing for structural types. The input language includes a selection of difficult features: first-class functions, mutable arrays, and recursive types. We show that a close-to-the-metal implementation of run-time casts inspired by Henglein's coercions eliminates all of the catastrophic slowdowns without introducing significant average-case overhead. As a result, Grift exhibits lower overheads than those of Typed Racket.","PeriodicalId":441774,"journal":{"name":"Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127537332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

LoCal: a language for programs operating on serialized data LoCal:一种用于对序列化数据进行操作的程序的语言

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-06-08 DOI: 10.1145/3314221.3314631

Michael Vollmer, Chaitanya Koparkar, Mike Rainey, Laith Sakka, Milind Kulkarni, Ryan Newton

{"title":"LoCal: a language for programs operating on serialized data","authors":"Michael Vollmer, Chaitanya Koparkar, Mike Rainey, Laith Sakka, Milind Kulkarni, Ryan Newton","doi":"10.1145/3314221.3314631","DOIUrl":"https://doi.org/10.1145/3314221.3314631","url":null,"abstract":"In a typical data-processing program, the representation of data in memory is distinct from its representation in a serialized form on disk. The former has pointers and arbitrary, sparse layout, facilitating easy manipulation by a program, while the latter is packed contiguously, facilitating easy I/O. We propose a language, LoCal, to unify in-memory and serialized formats. LoCal extends a region calculus into a location calculus, employing a type system that tracks the byte-addressed layout of all heap values. We formalize LoCal and prove type safety, and show how LoCal programs can be inferred from unannotated source terms. We transform the existing Gibbon compiler to use LoCal as an intermediate language, with the goal of achieving a balance between code speed and data compactness by introducing just enough indirection into heap layouts, preserving the asymptotic complexity of traditional representations, but working with mostly or completely serialized data. We show that our approach yields significant performance improvement over prior approaches to operating on packed data, without abandoning idiomatic programming with recursive functions.","PeriodicalId":441774,"journal":{"name":"Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124308040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Unsupervised learning of API aliasing specifications API混叠规范的无监督学习

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-06-08 DOI: 10.1145/3314221.3314640

Jan Eberhardt, Samuel Steffen, Veselin Raychev, Martin T. Vechev

{"title":"Unsupervised learning of API aliasing specifications","authors":"Jan Eberhardt, Samuel Steffen, Veselin Raychev, Martin T. Vechev","doi":"10.1145/3314221.3314640","DOIUrl":"https://doi.org/10.1145/3314221.3314640","url":null,"abstract":"Real world applications make heavy use of powerful libraries and frameworks, posing a significant challenge for static analysis as the library implementation may be very complex or unavailable. Thus, obtaining specifications that summarize the behaviors of the library is important as it enables static analyzers to precisely track the effects of APIs on the client program, without requiring the actual API implementation. In this work, we propose a novel method for discovering aliasing specifications of APIs by learning from a large dataset of programs. Unlike prior work, our method does not require manual annotation, access to the library's source code or ability to run its APIs. Instead, it learns specifications in a fully unsupervised manner, by statically observing usages of APIs in the dataset. The core idea is to learn a probabilistic model of interactions between API methods and aliasing objects, enabling identification of additional likely aliasing relations, and to then infer aliasing specifications of APIs that explain these relations. The learned specifications are then used to augment an API-aware points-to analysis. We implemented our approach in a tool called USpec and used it to automatically learn aliasing specifications from millions of source code files. USpec learned over 2000 specifications of various Java and Python APIs, in the process improving the results of the points-to analysis and its clients.","PeriodicalId":441774,"journal":{"name":"Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114981787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Abstract interpretation under speculative execution 投机性执行下的抽象解释

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-04-25 DOI: 10.1145/3314221.3314647

Meng Wu, Chao Wang

{"title":"Abstract interpretation under speculative execution","authors":"Meng Wu, Chao Wang","doi":"10.1145/3314221.3314647","DOIUrl":"https://doi.org/10.1145/3314221.3314647","url":null,"abstract":"Analyzing the behavior of a program running on a processor that supports speculative execution is crucial for applications such as execution time estimation and side channel detection. Unfortunately, existing static analysis techniques based on abstract interpretation do not model speculative execution since they focus on functional properties of a program while speculative execution does not change the functionality. To fill the gap, we propose a method to make abstract interpretation sound under speculative execution. There are two contributions. First, we introduce the notion of virtual control flow to augment instructions that may be speculatively executed and thus affect subsequent instructions. Second, to make the analysis efficient, we propose optimizations to handle merges and loops and to safely bound the speculative execution depth. We have implemented and evaluated the proposed method in a static cache analysis for execution time estimation and side channel detection. Our experiments show that the new method, while guaranteed to be sound under speculative execution, outperforms state-of-the-art abstract interpretation techniques that may be unsound.","PeriodicalId":441774,"journal":{"name":"Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131909426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 33

Optimization and abstraction: a synergistic approach for analyzing neural network robustness 优化与抽象:神经网络鲁棒性分析的协同方法

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation Pub Date : 2019-04-22 DOI: 10.1145/3314221.3314614

Greg Anderson, Shankara Pailoor, Işıl Dillig, Swarat Chaudhuri

引用次数: 86