Improving Thread-Modular Abstract Interpretation

Schwarz, Michael; Saan, Simmo; Seidl, Helmut; Apinis, Kalmer; Erhard, Julian; Vojdani, Vesal

doi:10.1007/978-3-030-88806-0_18

Cited by 16 publications

(22 citation statements)

References 20 publications

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“…2 illustrates the workflow of Concrat G . It uses Goblint [52], [55] to analyze C code. Goblint computes both datalock and flow-lock relations using abstract interpretation [26].…”

Section: A Implementationmentioning

confidence: 99%

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Concrat: An Automatic C-to-Rust Lock API Translator for Concurrent Programs

Hong¹,

Ryu²

2023

Preprint

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Concurrent programs suffer from data races. To prevent data races, programmers use locks. However, programs can eliminate data races only when they acquire and release correct locks at correct timing. The lock API of C, in which people have developed a large portion of legacy system programs, does not validate the correct use of locks. On the other hand, Rust, a recently developed system programming language, provides a lock API that guarantees the correct use of locks via type checking. This makes rewriting legacy system programs in Rust a promising way to retrofit safety into them. Unfortunately, manual C-to-Rust translation is extremely laborious due to the discrepancies between their lock APIs. Even the state-of-the-art automatic C-to-Rust translator retains the C lock API, expecting developers to replace them with the Rust lock API. In this work, we propose an automatic tool to replace the C lock API with the Rust lock API. It facilitates C-to-Rust translation of concurrent programs with less human effort than the current practice. Our tool consists of a Rust code transformer that takes a lock summary as an input and a static analyzer that efficiently generates precise lock summaries. We show that the transformer is scalable and widely applicable while preserving the semantics; it transforms 66 KLOC in 2.6 seconds and successfully handles 74% of real-world programs. We also show that the analyzer is scalable and precise; it analyzes 66 KLOC in 4.3 seconds. of the Linux kernel will support Rust code [23]. Android and Fuchsia implementations also use RustReimplementing concurrent programs in Rust is, however, labor-intensive if done manually. The discrepancies between the lock APIs of C and Rust hinder programmers from mechanical rewriting. They have to understand the use of the C lock API in original programs and restructure the programs to express the intended logic with the Rust lock API. It poses the necessity for a tool for automatic C-to-Rust translation.The state-of-the-art C-to-Rust translator, C2Rust [58], is still far from alleviating the burden on programmers. The translation of C2Rust is completely syntactic and generates Rust code using the C lock API. Programmers are expected to replace the C lock API in C2Rust-generated code with the Rust lock API for thread safety. While being better than nothing,

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“…2 illustrates the workflow of Concrat G . It uses Goblint [52], [55] to analyze C code. Goblint computes both datalock and flow-lock relations using abstract interpretation [26].…”

Section: A Implementationmentioning

confidence: 99%

“…However, other analyzers perform context-or path-sensitive analyses to reduce false alarms. Goblint [52], [55] and others [45]- [47] are based on abstract interpretation [26]. RELAY [56] and SDRacer [57] utilize symbolic execution [20].…”

Section: Related Workmentioning

confidence: 99%

Concrat: An Automatic C-to-Rust Lock API Translator for Concurrent Programs

Hong¹,

Ryu²

2023

Preprint

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“…We first detail the handling of local variables and guards for the local trace semantics that are omitted in the main text. Then, we give an example formalism for local traces that extends the one proposed in [48] for handling thread joins and returns.…”

Section: A Details On the Local Trace Semanticsmentioning

confidence: 99%

“…In particular, a binary operation e may only be defined for a pair of local traces t 0 and t 1 if certain parts of t 0 and t 1 represent the same computation. To make such restrictions explicit, we recall the concrete representation of local traces presented in [48] and enhance it for returning from and joining of threads.…”

Section: A2 Formalism For Local Tracesmentioning

confidence: 99%

“…For that, we choose A = (I ♯ × 2 P ) × (M → (N 0 × (I ♯ × 2 P )). For convenience, we here (intermediately) allow control-point Table 3: Results of executing our analyzer (with presented analyses), NR-Goblint [48] (with intervals) and Duet [19] on the benchmark set by Schwarz et al [48], for which we automatically generated invariants using the analysis from Section 8. Checkmark (✓) indicates all invariants proven and otherwise the number of proven invariants is given.…”

Section: G Soundness Proof Of Analysis With Joinsmentioning

confidence: 99%

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Clustered Relational Thread-Modular Abstract Interpretation with Local Traces

Schwarz¹,

Saan²,

Seidl³

et al. 2023

Preprint

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We construct novel thread-modular analyses that track relational information for potentially overlapping clusters of global variables -given that they are protected by common mutexes. We provide a framework to systematically increase the precision of clustered relational analyses by splitting control locations based on abstractions of local traces. As one instance, we obtain an analysis of dynamic thread creation and joining. Interestingly, tracking less relational information for globals may result in higher precision. We consider the class of 2-decomposable domains that encompasses many weakly relational domains (e.g., Octagons). For these domains, we prove that maximal precision is attained already for clusters of globals of sizes at most 2.

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Clustered Relational Thread-Modular Abstract Interpretation with Local Traces

Schwarz

Saan

Seidl

et al. 2023

Lecture Notes in Computer Science

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We construct novel thread-modular analyses that track relational information for potentially overlapping clusters of global variables – given that they are protected by common mutexes. We provide a framework to systematically increase the precision of clustered relational analyses by splitting control locations based on abstractions of local traces. As one instance, we obtain an analysis of dynamic thread creation and joining. Interestingly, tracking less relational information for globals may result in higher precision. We consider the class of 2-decomposable domains that encompasses many weakly relational domains (e.g., Octagons). For these domains, we prove that maximal precision is attained already for clusters of globals of sizes at most 2.

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Improving Thread-Modular Abstract Interpretation

Cited by 16 publications

References 20 publications

Concrat: An Automatic C-to-Rust Lock API Translator for Concurrent Programs

Concrat: An Automatic C-to-Rust Lock API Translator for Concurrent Programs

Clustered Relational Thread-Modular Abstract Interpretation with Local Traces

Clustered Relational Thread-Modular Abstract Interpretation with Local Traces

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