Reim &amp; ReImInfer

Huang, Wei; Milanova, Ana; Dietl, Werner; Ernst, M.

doi:10.1145/2384616.2384680

Cited by 36 publications

(6 citation statements)

References 31 publications

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“…for each pointer it infers whether it is owning/non-owning at particular program locations. -Mutability analysis: infers which pointers are used to modify the object they point to (inspired by [18,20]). -Fatness analysis: distinguishes array pointers from non-array pointers (inspired by [27]).…”

Section: Architecturementioning

confidence: 99%

“…Type qualifiers are a lightweight, practical mechanism for specifying and checking properties not captured by traditional type systems. A general flow-insensitive type qualifier framework has been proposed [17], with subsequent applications analysing Java reference mutability [18,20] and C array bounds [27]. Our mutability and fatness analyses of Crown are greatly influenced by these work.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Ownership guided C to Rust translation

Zhang¹,

David²,

Yu³

et al. 2023

Preprint

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Dubbed a safer C, Rust is a modern programming language that combines memory safety and low-level control. This interesting combination has made Rust very popular among developers and there is a growing trend of migrating legacy codebases (very often in C) to Rust. In this paper, we present a C to Rust translation approach centred around static ownership analysis. We design a suite of analyses that infer ownership models of C pointers and automatically translate the pointers into safe Rust equivalents. The resulting tool, Crown, scales to realworld codebases (half a million lines of code in less than 10 seconds) and achieves a high conversion rate.

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Section: Architecturementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Ownership guided C to Rust translation

Zhang¹,

David²,

Yu³

et al. 2023

Preprint

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“…They enforce the standard rule that the right-hand side of an implicit or explicit assignment is a subtype of the left-hand side, which disallows flow paths from a readonly variables to an update. The details of the analysis are described by Huang et al 14 and Milanova. 16 Standard reference immutability provides type inference as well.…”

Section: Standard Reference Immutability (Reim)mentioning

confidence: 99%

“…At the core of

\mathit{\operatorname{mod}}

is the known reference immutability analysis, however, adaptation to Python requires a significant extension of this work. In this section, we outline the ReIm 14 reference immutability system; just as other reference immutability systems such as Javari, 15 ReIm is designed for Java. In the following sections, we describe the extensions needed to adapt the analysis to Python and the notion of soundness we use in this work.…”

Section: ‐Address Code Reference Immutability and Soundness Analysismentioning

confidence: 99%

Principled and practical static analysis for Python: Weakest precondition inference of hyperparameter constraints

Rak‐amnouykit,

Milanova,

Baudart

et al. 2023

Softw Pract Exp

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Application programming interfaces often have correctness constraints that cut across multiple arguments. Violating these constraints causes the underlying code to raise runtime exceptions, but at the interface level, these are usually documented at most informally. This article presents novel principled static analysis and the first interprocedural weakest‐precondition analysis for Python to extract inter‐argument constraints. The analysis is mostly static, but to make it tractable for typical Python idioms, it selectively switches to the concrete domain for some cases. This article focuses on the important case where the interfaces are machine‐learning operators and their arguments are hyperparameters, rife with constraints. We extracted hyperparameter constraints for 429 functions and operators from 11 libraries and found real bugs. We used a methodology to obtain ground truth for 181 operators from 8 machine‐learning libraries; the analysis achieved high precision and recall for them. Our technique advances static analysis for Python and is a step towards safer and more robust machine learning.

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“…As mentioned in §3, checking of proper initialization has been the subject of a variety of previous work [24,25,35,37], as incorrect initialization jeopardizes reasoning about many other object properties (e.g., immutability [28,38]). NullAway is distinguished by initialization checking that is unsound, yet has prevented nearly all initialization errors in a multi-million line code base over many months, requiring fewer annotations than the sound approaches.…”

Section: Related Workmentioning

confidence: 99%

NullAway: practical type-based null safety for Java

Banerjee

Clapp

Sridharan

2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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NullPointerExceptions (NPEs) are a key source of crashes in modern Java programs. Previous work has shown how such errors can be prevented at compile time via code annotations and pluggable type checking. However, such systems have been difficult to deploy on large-scale software projects, due to significant build-time overhead and / or a high annotation burden. This paper presents NullAway, a new type-based null safety checker for Java that overcomes these issues. NullAway has been carefully engineered for low overhead, so it can run as part of every build. Further, NullAway reduces annotation burden through targeted unsound assumptions, aiming for no false negatives in practice on checked code. Our evaluation shows that NullAway has significantly lower build-time overhead (1.15×) than comparable tools (2.8-5.1×). Further, on a corpus of production crash data for widely-used Android apps built with NullAway, remaining NPEs were due to unchecked third-party libraries (64%), deliberate error suppressions (17%), or reflection and other forms of post-checking code modification (17%), never due to NullAway's unsound assumptions for checked code. CCS CONCEPTS• Software and its engineering → Extensible languages; Compilers; Formal software verification.

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Reim & ReImInfer

Cited by 36 publications

References 31 publications

Ownership guided C to Rust translation

Ownership guided C to Rust translation

Principled and practical static analysis for Python: Weakest precondition inference of hyperparameter constraints

NullAway: practical type-based null safety for Java

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