An empirical study of optimizations in YOGI

Nori, Aditya V.; Rajamani, Sriram K.

doi:10.1145/1806799.1806852

Cited by 22 publications

(18 citation statements)

References 12 publications

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“…Second, YOGI has been highly optimized over several years of research and development [18], [11], [4], [10], thus, any performance improvement is considered significant. Third, it is a "third-party" tool; we were never a part of the design or implementation of YOGI.…”

Section: Discussionmentioning

confidence: 99%

A program transformation for faster goal-directed search

Lai¹,

Qadeer²

2014

2014 Formal Methods in Computer-Aided Design (FMCAD)

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A goal-directed search attempts to reveal only relevant information needed to establish reachability (or unreachability) of the goal from the initial state of the program. The further apart the goal is from the initial state, the harder it can get to establish what is relevant. This paper addresses this concern in the context of programs with assertions that may be nested deeply inside its call graph-thus, far away interprocedurally from main. We present a source-to-source transformation on programs that lifts all assertions in the input program to the entry procedure of the output program, thus, revealing more information about the assertions close to the entry of the program. The transformation is easy to implement and applies to sequential as well as concurrent programs. We empirically validate using multiple goal-directed verifiers that applying this transformation before invoking the verifier results in significant speedups, sometimes up to an order of magnitude.

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Section: Discussionmentioning

confidence: 99%

A program transformation for faster goal-directed search

Lai¹,

Qadeer²

2014

2014 Formal Methods in Computer-Aided Design (FMCAD)

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“…Picking the right predicates, either upfront or dynamically during analysis [5], is essential in this setting to ensure rapid convergence of a model checker, and is in practice achieved through a combination of "systematic" methods (for CEGAR, in particular through Craig interpolation) and heuristics. For instance, SLAM extracts refinement predicates from counterexamples using domain-specific heuristics [16]; YOGI uses machine learning to choose the default set of heuristics for picking predicates [19]; CPAchecker uses domain types to decide whether to represent variables explicitly or using BDDs [2], and to choose refinement predicates [4]; and Eldarica uses heuristics to guide the process of Craig interpolation [18]. Similar heuristics can be identified in tools based on abstract interpretation, among others.…”

Section: Introductionmentioning

confidence: 99%

Systematic Predicate Abstraction Using Variable Roles

Demyanova

Rümmer

Zuleger

2017

Lecture Notes in Computer Science

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Abstract. Heuristics for discovering predicates for abstraction are an essential part of software model checkers. Picking the right predicates affects the runtime of a model checker, or determines if a model checker is able to solve a verification task at all. In this paper we present a method to systematically specify heuristics for generating program-specific abstractions. The heuristics can be used to generate initial abstractions, and to guide abstraction refinement through templates provided for Craig interpolation. We describe the heuristics using variable roles, which allow us to pick domain-specific predicates according to the program under analysis. Variable roles identify typical variable usage patterns and can be computed using lightweight static analysis, for instance with the help of of-the-shelf logical programming engines. We implemented a prototype tool which extracts initial predicates and templates for C programs and passes them to the Eldarica model checker in the form of source code annotations. For evaluation, we defined a set of heuristics, motivated by Eldarica's previous built-in heuristics and typical verification benchmarks from the literature and SV-COMP. We evaluate our approach on a set of more than 500 programs, and observe an overall increase in the number of solved tasks by 11.2%, and significant speedup on certain benchmark families.

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“…Nevertheless, the existing tools work successfully in practice on most, if not all, problem instances; but their performance is difficult to analyze theoretically. Besides, a lot of engineering optimizations [37] go into the design of these tools whose utility can only be understood by the tool designers. We therefore take a novel approach, based on machine learning, to synthesize an algorithm selector [39] for software model checkers.…”

Section: Introductionmentioning

confidence: 99%

MUX: algorithm selection for software model checkers

Tulsian

Kanade

Kumar

et al. 2014

Proceedings of the 11th Working Conference on Mining Software Repositories

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With the growing complexity of modern day software, software model checking has become a critical technology for ensuring correctness of software. As is true with any promising technology, there are a number of tools for software model checking. However, their respective performance trade-offs are difficult to characterize accurately -making it difficult for practitioners to select a suitable tool for the task at hand. This paper proposes a technique called MUX that addresses the problem of selecting the most suitable software model checker for a given input instance. MUX performs machine learning on a repository of software verification instances. The algorithm selector, synthesized through machine learning, uses structural features from an input instance, comprising a program-property pair, at runtime and determines which tool to use.We have implemented MUX for Windows device drivers and evaluated it on a number of drivers and model checkers. Our results are promising in that the algorithm selector not only avoids a significant number of timeouts but also improves the total runtime by a large margin, compared to any individual model checker. It also outperforms a portfolio-based algorithm selector being used in Microsoft at present. Besides, MUX identifies structural features of programs that are key factors in determining performance of model checkers.

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An empirical study of optimizations in YOGI

Cited by 22 publications

References 12 publications

A program transformation for faster goal-directed search

A program transformation for faster goal-directed search

Systematic Predicate Abstraction Using Variable Roles

MUX: algorithm selection for software model checkers

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