Thodoris Sotiropoulos, Stefanos Chaliasos, Zhendong Su
We introduce a novel approach for testing static typing implementations based on the concept of API-driven program synthesis. The idea is to synthesize type-intensive but small and well-typed programs by leveraging and combining application programming interfaces (APIs) derived from existing software libraries. Our primary insight is backed up by real-world evidence: a significant number of compiler typing bugs are caused by small test cases that employ APIs from the standard library of the language under test. This is attributed to the inherent complexity of the majority of these APIs, which often exercise a wide range of sophisticated type-related features. The main contribution of our approach is the ability to produce small client programs with increased feature coverage, without bearing the burden of generating the corresponding well-formed API definitions from scratch. To validate diverse aspects of static typing procedures (i.e., soundness, precision of type inference), we also enrich our API-driven approach with fault-injection and semantics-preserving modes, along with their corresponding test oracles. We evaluate our implemented tool, Thalia on testing the static typing implementations of the compilers for three popular languages, namely, Scala, Kotlin, and Groovy. Thalia has uncovered 84 typing bugs (77 confirmed and 22 fixed), most of which are triggered by test cases featuring APIs that rely on parametric polymorphism, overloading, and higher-order functions. Our comparison with state-of-the-art shows that Thalia yields test programs with distinct characteristics, offering additional and complementary benefits.
我们基于 API 驱动的程序综合概念，介绍了一种测试静态类型实现的新方法。我们的想法是，通过利用和组合从现有软件库中衍生出来的应用编程接口（API），合成类型密集型的小型良好类型程序。我们的主要见解得到了现实世界证据的支持：大量编译器类型错误是由采用被测语言标准库中的应用程序接口的小型测试用例引起的。这归因于大多数这些应用程序接口的固有复杂性，它们通常会使用各种复杂的类型相关功能。我们的方法的主要贡献在于能够生成具有更多特性覆盖范围的小型客户端程序，而无需承担从头开始生成相应的格式良好的 API 定义的负担。为了验证静态类型化程序的各个方面（即健全性、类型推断的精确性），我们还通过故障注入和语义保留模式以及相应的测试谕令来丰富我们的 API 驱动方法。我们在测试三种流行语言（Scala、Kotlin 和 Groovy）编译器的静态类型实现时评估了我们的工具 Thalia。Thalia 发现了 84 个类型错误（77 个已确认，22 个已修复），其中大部分是由依赖参数多态性、重载和高阶函数的 API 测试用例触发的。我们与最新技术的比较表明，Thalia 生成的测试程序具有鲜明的特点，提供了额外的互补优势。
{"title":"API-Driven Program Synthesis for Testing Static Typing Implementations","authors":"Thodoris Sotiropoulos, Stefanos Chaliasos, Zhendong Su","doi":"10.1145/3632904","DOIUrl":"https://doi.org/10.1145/3632904","url":null,"abstract":"We introduce a novel approach for testing static typing implementations based on the concept of API-driven program synthesis. The idea is to synthesize type-intensive but small and well-typed programs by leveraging and combining application programming interfaces (APIs) derived from existing software libraries. Our primary insight is backed up by real-world evidence: a significant number of compiler typing bugs are caused by small test cases that employ APIs from the standard library of the language under test. This is attributed to the inherent complexity of the majority of these APIs, which often exercise a wide range of sophisticated type-related features. The main contribution of our approach is the ability to produce small client programs with increased feature coverage, without bearing the burden of generating the corresponding well-formed API definitions from scratch. To validate diverse aspects of static typing procedures (i.e., soundness, precision of type inference), we also enrich our API-driven approach with fault-injection and semantics-preserving modes, along with their corresponding test oracles. We evaluate our implemented tool, Thalia on testing the static typing implementations of the compilers for three popular languages, namely, Scala, Kotlin, and Groovy. Thalia has uncovered 84 typing bugs (77 confirmed and 22 fixed), most of which are triggered by test cases featuring APIs that rely on parametric polymorphism, overloading, and higher-order functions. Our comparison with state-of-the-art shows that Thalia yields test programs with distinct characteristics, offering additional and complementary benefits.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139384150","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}
Giovanna Kobus Conrado, Amir Kafshdar Goharshady, Chun Kit Lam
Pathwidth and treewidth are standard and well-studied graph sparsity parameters which intuitively model the degree to which a given graph resembles a path or a tree, respectively. It is well-known that the control-flow graphs of structured goto-free programs have a tree-like shape and bounded treewidth. This fact has been exploited to design considerably more efficient algorithms for a wide variety of static analysis and compiler optimization problems, such as register allocation, µ-calculus model-checking and parity games, data-flow analysis, cache management, and liftetime-optimal redundancy elimination. However, there is no bound in the literature for the pathwidth of programs, except the general inequality that the pathwidth of a graph is at most O (lg n ) times its treewidth, where n is the number of vertices of the graph. In this work, we prove that control-flow graphs of structured programs have bounded pathwidth and provide a linear-time algorithm to obtain a path decomposition of small width. Specifically, we establish a bound of 2 · d on the pathwidth of programs with nesting depth d . Since real-world programs have small nesting depth, they also have bounded pathwidth. This is significant for a number of reasons: (i) pathwidth is a strictly stronger parameter than treewidth, i.e. any graph family with bounded pathwidth has bounded treewidth, but the converse does not hold; (ii) any algorithm that is designed with treewidth in mind can be applied to bounded-pathwidth graphs with no change; (iii) there are problems that are fixed-parameter tractable with respect to pathwidth but not treewidth; (iv) verification algorithms that are designed based on treewidth would become significantly faster when using pathwidth as the parameter; and (v) it is easier to design algorithms based on bounded pathwidth since one does not have to consider the often-challenging case of merge nodes in treewidth-based dynamic programming. Thus, we invite the static analysis and compiler optimization communities to adopt pathwidth as their parameter of choice instead of, or in addition to, treewidth. Intuitively, control-flow graphs are not only tree-like, but also path-like and one can obtain simpler and more scalable algorithms by relying on path-likeness instead of tree-likeness. As a motivating example, we provide a simpler and more efficient algorithm for spill-free register allocation using bounded pathwidth instead of treewidth. Our algorithm reduces the runtime from O ( n · r 2 · tw · r + 2 · r ) to O ( n · pw · r pw · r + r + 1 ), where n is the number of lines of code, r is the number of registers, pw is the pathwidth of the control-flow graph and tw is its treewidth. We provide extensive experimental results showing that our approach is applicable to a wide variety of real-world embedded benchmarks from SDCC and obtains runtime improvements of 2-3 orders of magnitude. This is because the pathwidth is equal to the treewidth, or one more, in the ove
{"title":"The Bounded Pathwidth of Control-Flow Graphs","authors":"Giovanna Kobus Conrado, Amir Kafshdar Goharshady, Chun Kit Lam","doi":"10.1145/3622807","DOIUrl":"https://doi.org/10.1145/3622807","url":null,"abstract":"Pathwidth and treewidth are standard and well-studied graph sparsity parameters which intuitively model the degree to which a given graph resembles a path or a tree, respectively. It is well-known that the control-flow graphs of structured goto-free programs have a tree-like shape and bounded treewidth. This fact has been exploited to design considerably more efficient algorithms for a wide variety of static analysis and compiler optimization problems, such as register allocation, µ-calculus model-checking and parity games, data-flow analysis, cache management, and liftetime-optimal redundancy elimination. However, there is no bound in the literature for the pathwidth of programs, except the general inequality that the pathwidth of a graph is at most O (lg n ) times its treewidth, where n is the number of vertices of the graph. In this work, we prove that control-flow graphs of structured programs have bounded pathwidth and provide a linear-time algorithm to obtain a path decomposition of small width. Specifically, we establish a bound of 2 · d on the pathwidth of programs with nesting depth d . Since real-world programs have small nesting depth, they also have bounded pathwidth. This is significant for a number of reasons: (i) pathwidth is a strictly stronger parameter than treewidth, i.e. any graph family with bounded pathwidth has bounded treewidth, but the converse does not hold; (ii) any algorithm that is designed with treewidth in mind can be applied to bounded-pathwidth graphs with no change; (iii) there are problems that are fixed-parameter tractable with respect to pathwidth but not treewidth; (iv) verification algorithms that are designed based on treewidth would become significantly faster when using pathwidth as the parameter; and (v) it is easier to design algorithms based on bounded pathwidth since one does not have to consider the often-challenging case of merge nodes in treewidth-based dynamic programming. Thus, we invite the static analysis and compiler optimization communities to adopt pathwidth as their parameter of choice instead of, or in addition to, treewidth. Intuitively, control-flow graphs are not only tree-like, but also path-like and one can obtain simpler and more scalable algorithms by relying on path-likeness instead of tree-likeness. As a motivating example, we provide a simpler and more efficient algorithm for spill-free register allocation using bounded pathwidth instead of treewidth. Our algorithm reduces the runtime from O ( n · r 2 · tw · r + 2 · r ) to O ( n · pw · r pw · r + r + 1 ), where n is the number of lines of code, r is the number of registers, pw is the pathwidth of the control-flow graph and tw is its treewidth. We provide extensive experimental results showing that our approach is applicable to a wide variety of real-world embedded benchmarks from SDCC and obtains runtime improvements of 2-3 orders of magnitude. This is because the pathwidth is equal to the treewidth, or one more, in the ove","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136112664","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}
In this paper, we introduce DebloaterX, a new approach for automatically identifying context-independent objects to debloat contexts in object-sensitive pointer analysis ( k obj). Object sensitivity achieves high precision, but its context construction mechanism combines objects with their contexts indiscriminately. This leads to a combinatorial explosion of contexts in large programs, resulting in inefficiency. Previous research has proposed a context-debloating approach that inhibits a pre-selected set of context-independent objects from forming new contexts, improving the efficiency of k obj. However, this earlier context-debloating approach under-approximates the set of context-independent objects identified, limiting performance speedups. We introduce a novel context-debloating pre-analysis approach that identifies objects as context-dependent only when they are potentially precision-critical to k obj based on three general container-usage patterns. Our research finds that objects containing no fields of ”abstract” (i.e., open) types can be analyzed context-insensitively with negligible precision loss in real-world applications. We provide clear rules and efficient algorithms to recognize these patterns, selecting more context-independent objects for better debloating. We have implemented DebloaterX in the Qilin framework and will release it as an open-source tool. Our experimental results on 12 standard Java benchmarks and real-world programs show that DebloaterX selects 92.4% of objects to be context-independent on average, enabling k obj to run significantly faster (an average of 19.3x when k = 2 and 150.2x when k = 3) and scale up to 8 more programs when k = 3, with only a negligible loss of precision (less than 0.2%). Compared to state-of-the-art alternative pre-analyses in accelerating k obj, DebloaterX outperforms Zipper significantly in both precision and efficiency and outperforms Conch (the earlier context-debloating approach) in efficiency substantially while achieving nearly the same precision.
{"title":"A Container-Usage-Pattern-Based Context Debloating Approach for Object-Sensitive Pointer Analysis","authors":"Dongjie He, Yujiang Gui, Wei Li, Yonggang Tao, Changwei Zou, Yulei Sui, Jingling Xue","doi":"10.1145/3622832","DOIUrl":"https://doi.org/10.1145/3622832","url":null,"abstract":"In this paper, we introduce DebloaterX, a new approach for automatically identifying context-independent objects to debloat contexts in object-sensitive pointer analysis ( k obj). Object sensitivity achieves high precision, but its context construction mechanism combines objects with their contexts indiscriminately. This leads to a combinatorial explosion of contexts in large programs, resulting in inefficiency. Previous research has proposed a context-debloating approach that inhibits a pre-selected set of context-independent objects from forming new contexts, improving the efficiency of k obj. However, this earlier context-debloating approach under-approximates the set of context-independent objects identified, limiting performance speedups. We introduce a novel context-debloating pre-analysis approach that identifies objects as context-dependent only when they are potentially precision-critical to k obj based on three general container-usage patterns. Our research finds that objects containing no fields of ”abstract” (i.e., open) types can be analyzed context-insensitively with negligible precision loss in real-world applications. We provide clear rules and efficient algorithms to recognize these patterns, selecting more context-independent objects for better debloating. We have implemented DebloaterX in the Qilin framework and will release it as an open-source tool. Our experimental results on 12 standard Java benchmarks and real-world programs show that DebloaterX selects 92.4% of objects to be context-independent on average, enabling k obj to run significantly faster (an average of 19.3x when k = 2 and 150.2x when k = 3) and scale up to 8 more programs when k = 3, with only a negligible loss of precision (less than 0.2%). Compared to state-of-the-art alternative pre-analyses in accelerating k obj, DebloaterX outperforms Zipper significantly in both precision and efficiency and outperforms Conch (the earlier context-debloating approach) in efficiency substantially while achieving nearly the same precision.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136112813","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}
Programming tools are increasingly integral to research and analysis in myriad domains, including specialized areas with no formal relation to computer science. Embedded domain-specific languages (eDSLs) have the potential to serve these programmers while placing relatively light implementation burdens on language designers. However, barriers to eDSL use reduce their practical value and adoption. In this paper, we aim to deepen our understanding of how programmers use eDSLs and identify user needs to inform future eDSL designs. We performed a contextual inquiry (9 participants) with domain experts using Mimi, an eDSL for climate change economics modeling. A thematic analysis identified five key themes, including: the interaction between the eDSL and the host language has significant and sometimes unexpected impacts on eDSL user experience, and users preferentially engage with domain-specific communities and code templates rather than host language resources. The needs uncovered in our study offer design considerations for future eDSLs and suggest directions for future DSL usability research.
{"title":"How Domain Experts Use an Embedded DSL","authors":"Lisa Rennels, Sarah E. Chasins","doi":"10.1145/3622851","DOIUrl":"https://doi.org/10.1145/3622851","url":null,"abstract":"Programming tools are increasingly integral to research and analysis in myriad domains, including specialized areas with no formal relation to computer science. Embedded domain-specific languages (eDSLs) have the potential to serve these programmers while placing relatively light implementation burdens on language designers. However, barriers to eDSL use reduce their practical value and adoption. In this paper, we aim to deepen our understanding of how programmers use eDSLs and identify user needs to inform future eDSL designs. We performed a contextual inquiry (9 participants) with domain experts using Mimi, an eDSL for climate change economics modeling. A thematic analysis identified five key themes, including: the interaction between the eDSL and the host language has significant and sometimes unexpected impacts on eDSL user experience, and users preferentially engage with domain-specific communities and code templates rather than host language resources. The needs uncovered in our study offer design considerations for future eDSLs and suggest directions for future DSL usability research.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136115975","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}
Interprocedural alias analyses often sacrifice precision for scalability. Thus, modern compilers such as GCC and LLVM implement more scalable but less precise intraprocedural alias analyses. This compromise makes the compilers miss out on potential optimization opportunities, affecting the performance of the application. Modern compilers implement loop-versioning with dynamic checks for pointer disambiguation to enable the missed optimizations. Polyhedral access range analysis and symbolic range analysis enable 𝑂 (1) range checks for non-overlapping of memory accesses inside loops. However, these approaches work only for the loops in which the loop bounds are loop invariants. To address this limitation, researchers proposed a technique that requires 𝑂 (𝑙𝑜𝑔 𝑛) memory accesses for pointer disambiguation. Others improved the performance of dynamic checks to single memory access by constraining the object size and alignment. However, the former approach incurs noticeable overhead due to its dynamic checks, whereas the latter has a noticeable allocator overhead. Thus, scalability remains a challenge. In this work, we present a tool, Rapid, that further reduces the overheads of the allocator and dynamic checks proposed in the existing approaches. The key idea is to identify objects that need disambiguation checks using a profiler and allocate them in different regions, which are disjoint memory areas. The disambiguation checks simply compare the regions corresponding to the objects. The regions are aligned such that the top 32 bits in the addresses of any two objects allocated in different regions are always different. As a consequence, the dynamic checks do not require any memory access to ensure that the objects belong to different regions, making them efficient. Rapid achieved a maximum performance benefit of around 52.94% for Polybench and 1.88% for CPU SPEC 2017 benchmarks. The maximum CPU overhead of our allocator is 0.57% with a geometric mean of -0.2% for CPU SPEC 2017 benchmarks. Due to the low overhead of the allocator and dynamic checks, Rapid could improve the performance of 12 out of 16 CPU SPEC 2017 benchmarks. In contrast, a state-of-the-art approach used in the comparison could improve only five CPU SPEC 2017 benchmarks.
{"title":"Rapid: Region-Based Pointer Disambiguation","authors":"Khushboo Chitre, Piyus Kedia, Rahul Purandare","doi":"10.1145/3622859","DOIUrl":"https://doi.org/10.1145/3622859","url":null,"abstract":"Interprocedural alias analyses often sacrifice precision for scalability. Thus, modern compilers such as GCC and LLVM implement more scalable but less precise intraprocedural alias analyses. This compromise makes the compilers miss out on potential optimization opportunities, affecting the performance of the application. Modern compilers implement loop-versioning with dynamic checks for pointer disambiguation to enable the missed optimizations. Polyhedral access range analysis and symbolic range analysis enable 𝑂 (1) range checks for non-overlapping of memory accesses inside loops. However, these approaches work only for the loops in which the loop bounds are loop invariants. To address this limitation, researchers proposed a technique that requires 𝑂 (𝑙𝑜𝑔 𝑛) memory accesses for pointer disambiguation. Others improved the performance of dynamic checks to single memory access by constraining the object size and alignment. However, the former approach incurs noticeable overhead due to its dynamic checks, whereas the latter has a noticeable allocator overhead. Thus, scalability remains a challenge. In this work, we present a tool, Rapid, that further reduces the overheads of the allocator and dynamic checks proposed in the existing approaches. The key idea is to identify objects that need disambiguation checks using a profiler and allocate them in different regions, which are disjoint memory areas. The disambiguation checks simply compare the regions corresponding to the objects. The regions are aligned such that the top 32 bits in the addresses of any two objects allocated in different regions are always different. As a consequence, the dynamic checks do not require any memory access to ensure that the objects belong to different regions, making them efficient. Rapid achieved a maximum performance benefit of around 52.94% for Polybench and 1.88% for CPU SPEC 2017 benchmarks. The maximum CPU overhead of our allocator is 0.57% with a geometric mean of -0.2% for CPU SPEC 2017 benchmarks. Due to the low overhead of the allocator and dynamic checks, Rapid could improve the performance of 12 out of 16 CPU SPEC 2017 benchmarks. In contrast, a state-of-the-art approach used in the comparison could improve only five CPU SPEC 2017 benchmarks.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136116388","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}
Programmers and researchers are increasingly developing surrogates of programs, models of a subset of the observable behavior of a given program, to solve a variety of software development challenges. Programmers train surrogates from measurements of the behavior of a program on a dataset of input examples. A key challenge of surrogate construction is determining what training data to use to train a surrogate of a given program. We present a methodology for sampling datasets to train neural-network-based surrogates of programs. We first characterize the proportion of data to sample from each region of a program's input space (corresponding to different execution paths of the program) based on the complexity of learning a surrogate of the corresponding execution path. We next provide a program analysis to determine the complexity of different paths in a program. We evaluate these results on a range of real-world programs, demonstrating that complexity-guided sampling results in empirical improvements in accuracy.
{"title":"Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs","authors":"Renda, Alex, Ding, Yi, Carbin, Michael","doi":"10.1145/3622856","DOIUrl":"https://doi.org/10.1145/3622856","url":null,"abstract":"Programmers and researchers are increasingly developing surrogates of programs, models of a subset of the observable behavior of a given program, to solve a variety of software development challenges. Programmers train surrogates from measurements of the behavior of a program on a dataset of input examples. A key challenge of surrogate construction is determining what training data to use to train a surrogate of a given program. We present a methodology for sampling datasets to train neural-network-based surrogates of programs. We first characterize the proportion of data to sample from each region of a program's input space (corresponding to different execution paths of the program) based on the complexity of learning a surrogate of the corresponding execution path. We next provide a program analysis to determine the complexity of different paths in a program. We evaluate these results on a range of real-world programs, demonstrating that complexity-guided sampling results in empirical improvements in accuracy.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136078393","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}
Angello Astorga, Chiao Hsieh, P. Madhusudan, Sayan Mitra
We introduce a novel notion of perception contracts to reason about the safety of controllers that interact with an environment using neural perception. Perception contracts capture errors in ground-truth estimations that preserve invariants when systems act upon them. We develop a theory of perception contracts and design symbolic learning algorithms for synthesizing them from a finite set of images. We implement our algorithms and evaluate synthesized perception contracts for two realistic vision-based control systems, a lane tracking system for an electric vehicle and an agricultural robot that follows crop rows. Our evaluation shows that our approach is effective in synthesizing perception contracts and generalizes well when evaluated over test images obtained during runtime monitoring of the systems.
{"title":"Perception Contracts for Safety of ML-Enabled Systems","authors":"Angello Astorga, Chiao Hsieh, P. Madhusudan, Sayan Mitra","doi":"10.1145/3622875","DOIUrl":"https://doi.org/10.1145/3622875","url":null,"abstract":"We introduce a novel notion of perception contracts to reason about the safety of controllers that interact with an environment using neural perception. Perception contracts capture errors in ground-truth estimations that preserve invariants when systems act upon them. We develop a theory of perception contracts and design symbolic learning algorithms for synthesizing them from a finite set of images. We implement our algorithms and evaluate synthesized perception contracts for two realistic vision-based control systems, a lane tracking system for an electric vehicle and an agricultural robot that follows crop rows. Our evaluation shows that our approach is effective in synthesizing perception contracts and generalizes well when evaluated over test images obtained during runtime monitoring of the systems.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136112663","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}
Adithya Murali, Lucas Peña, Ranjit Jhala, P. Madhusudan
Several practical tools for automatically verifying functional programs (e.g., Liquid Haskell and Leon for Scala programs) rely on a heuristic based on unrolling recursive function definitions followed by quantifier-free reasoning using SMT solvers. We uncover foundational theoretical properties of this heuristic, revealing that it can be generalized and formalized as a technique that is in fact complete for reasoning with combined First-Order theories of algebraic datatypes and background theories, where background theories support decidable quantifier-free reasoning. The theory developed in this paper explains the efficacy of these heuristics when they succeed, explain why they fail when they fail, and the precise role that user help plays in making proofs succeed.
{"title":"Complete First-Order Reasoning for Properties of Functional Programs","authors":"Adithya Murali, Lucas Peña, Ranjit Jhala, P. Madhusudan","doi":"10.1145/3622835","DOIUrl":"https://doi.org/10.1145/3622835","url":null,"abstract":"Several practical tools for automatically verifying functional programs (e.g., Liquid Haskell and Leon for Scala programs) rely on a heuristic based on unrolling recursive function definitions followed by quantifier-free reasoning using SMT solvers. We uncover foundational theoretical properties of this heuristic, revealing that it can be generalized and formalized as a technique that is in fact complete for reasoning with combined First-Order theories of algebraic datatypes and background theories, where background theories support decidable quantifier-free reasoning. The theory developed in this paper explains the efficacy of these heuristics when they succeed, explain why they fail when they fail, and the precise role that user help plays in making proofs succeed.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136112805","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}
George Zakhour, Pascal Weisenburger, Guido Salvaneschi
Several distributed programming language solutions have been proposed to reason about the placement of data, computations, and peers interaction. Such solutions include, among the others, multitier programming, choreographic programming and various approaches based on behavioral types. These methods statically ensure safety properties thanks to a complete knowledge about placement of data and computation at compile time. In distributed systems, however, dynamic placement of computation and data is crucial to enable performance optimizations, e.g., driven by data locality or in presence of a number of other constraints such as security and compliance regarding data storage location. Unfortunately, in existing programming languages, dynamic placement conflicts with static reasoning about distributed programs: the flexibility required by dynamic placement hinders statically tracking the location of data and computation. In this paper we present Dyno, a programming language that enables static reasoning about dynamic placement. Dyno features a type system where values are explicitly placed, but in contrast to existing approaches, placed values are also first class, ensuring that they can be passed around and referred to from other locations. Building on top of this mechanism, we provide a novel interpretation of dynamic placement as unions of placement types. We formalize type soundness, placement correctness (as part of type soundness) and architecture conformance. In case studies and benchmarks, our evaluation shows that Dyno enables static reasoning about programs even in presence of dynamic placement, ensuring type safety and placement correctness of programs at negligible performance cost. We reimplement an Android app with ∼ 7 K LOC in Dyno, find a bug in the existing implementation, and show that the app's approach is representative of a common way to implement dynamic placement found in over 100 apps in a large open-source app store.
已经提出了几种分布式编程语言解决方案来解释数据、计算和对等体交互的位置。这些解决方案包括多层编程、编排编程和基于行为类型的各种方法。由于在编译时完全了解数据和计算的位置，这些方法静态地确保了安全属性。然而，在分布式系统中，计算和数据的动态放置对于实现性能优化至关重要，例如，由数据位置驱动或存在许多其他约束，例如关于数据存储位置的安全性和遵从性。不幸的是，在现有的编程语言中，动态放置与分布式程序的静态推理相冲突:动态放置所需的灵活性阻碍了对数据和计算位置的静态跟踪。在本文中，我们介绍了Dyno，这是一种编程语言，可以对动态放置进行静态推理。Dyno具有显式放置值的类型系统，但与现有方法不同的是，放置的值也是第一类的，确保它们可以被传递并从其他位置引用。在此机制的基础上，我们将动态放置作为放置类型的联合提供了一种新的解释。我们将类型稳健性、位置正确性(作为类型稳健性的一部分)和体系结构一致性形式化。在案例研究和基准测试中，我们的评估表明，即使存在动态放置，Dyno也支持对程序的静态推理，以微不足道的性能成本确保程序的类型安全和放置正确性。我们在Dyno中重新实现了一个具有7 K LOC的Android应用程序，发现了现有实现中的一个bug，并表明该应用程序的方法代表了在大型开源应用程序商店中超过100个应用程序中实现动态放置的常见方法。
{"title":"Type-Safe Dynamic Placement with First-Class Placed Values","authors":"George Zakhour, Pascal Weisenburger, Guido Salvaneschi","doi":"10.1145/3622873","DOIUrl":"https://doi.org/10.1145/3622873","url":null,"abstract":"Several distributed programming language solutions have been proposed to reason about the placement of data, computations, and peers interaction. Such solutions include, among the others, multitier programming, choreographic programming and various approaches based on behavioral types. These methods statically ensure safety properties thanks to a complete knowledge about placement of data and computation at compile time. In distributed systems, however, dynamic placement of computation and data is crucial to enable performance optimizations, e.g., driven by data locality or in presence of a number of other constraints such as security and compliance regarding data storage location. Unfortunately, in existing programming languages, dynamic placement conflicts with static reasoning about distributed programs: the flexibility required by dynamic placement hinders statically tracking the location of data and computation. In this paper we present Dyno, a programming language that enables static reasoning about dynamic placement. Dyno features a type system where values are explicitly placed, but in contrast to existing approaches, placed values are also first class, ensuring that they can be passed around and referred to from other locations. Building on top of this mechanism, we provide a novel interpretation of dynamic placement as unions of placement types. We formalize type soundness, placement correctness (as part of type soundness) and architecture conformance. In case studies and benchmarks, our evaluation shows that Dyno enables static reasoning about programs even in presence of dynamic placement, ensuring type safety and placement correctness of programs at negligible performance cost. We reimplement an Android app with ∼ 7 K LOC in Dyno, find a bug in the existing implementation, and show that the app's approach is representative of a common way to implement dynamic placement found in over 100 apps in a large open-source app store.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136112807","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}
Chijin Zhou, Quan Zhang, Lihua Guo, Mingzhe Wang, Yu Jiang, Qing Liao, Zhiyong Wu, Shanshan Li, Bin Gu
Web browsers exhibit rich semantics that enable a plethora of web-based functionalities. However, these intricate semantics present significant challenges for the implementation and testing of browsers. For example, fuzzing, a widely adopted testing technique, typically relies on handwritten context-free grammars (CFGs) for automatically generating inputs. However, these CFGs fall short in adequately modeling the complex semantics of browsers, resulting in generated inputs that cover only a portion of the semantics and are prone to semantic errors. In this paper, we present SaGe, an automated method that enhances browser fuzzing through the use of production-context sensitive grammars (PCSGs) incorporating semantic information. Our approach begins by extracting a rudimentary CFG from W3C standards and iteratively enhancing it to create a PCSG. The resulting PCSG enables our fuzzer to generate inputs that explore a broader range of browser semantics with a higher proportion of semantically-correct inputs. To evaluate the efficacy of SaGe, we conducted 24-hour fuzzing campaigns on mainstream browsers, including Chrome, Safari, and Firefox. Our approach demonstrated better performance compared to existing browser fuzzers, with a 6.03%-277.80% improvement in edge coverage, a 3.56%-161.71% boost in semantic correctness rate, twice the number of bugs discovered. Moreover, we identified 62 bugs across the three browsers, with 40 confirmed and 10 assigned CVEs.
{"title":"Towards Better Semantics Exploration for Browser Fuzzing","authors":"Chijin Zhou, Quan Zhang, Lihua Guo, Mingzhe Wang, Yu Jiang, Qing Liao, Zhiyong Wu, Shanshan Li, Bin Gu","doi":"10.1145/3622819","DOIUrl":"https://doi.org/10.1145/3622819","url":null,"abstract":"Web browsers exhibit rich semantics that enable a plethora of web-based functionalities. However, these intricate semantics present significant challenges for the implementation and testing of browsers. For example, fuzzing, a widely adopted testing technique, typically relies on handwritten context-free grammars (CFGs) for automatically generating inputs. However, these CFGs fall short in adequately modeling the complex semantics of browsers, resulting in generated inputs that cover only a portion of the semantics and are prone to semantic errors. In this paper, we present SaGe, an automated method that enhances browser fuzzing through the use of production-context sensitive grammars (PCSGs) incorporating semantic information. Our approach begins by extracting a rudimentary CFG from W3C standards and iteratively enhancing it to create a PCSG. The resulting PCSG enables our fuzzer to generate inputs that explore a broader range of browser semantics with a higher proportion of semantically-correct inputs. To evaluate the efficacy of SaGe, we conducted 24-hour fuzzing campaigns on mainstream browsers, including Chrome, Safari, and Firefox. Our approach demonstrated better performance compared to existing browser fuzzers, with a 6.03%-277.80% improvement in edge coverage, a 3.56%-161.71% boost in semantic correctness rate, twice the number of bugs discovered. Moreover, we identified 62 bugs across the three browsers, with 40 confirmed and 10 assigned CVEs.","PeriodicalId":20697,"journal":{"name":"Proceedings of the ACM on Programming Languages","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114709","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：