Approximate counting in SMT and value estimation for probabilistic programs

Chistikov, Dmitry; Dimitrova, Rayna; Majumdar, Rupak

doi:10.1007/s00236-017-0297-2

Cited by 32 publications

(31 citation statements)

References 51 publications

(100 reference statements)

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“…To deal with program variables, we also extend XOR-Sample' from binary variables to bit-vector representations, similarly to [18]. For floating point arithmetic, we use a mixed abstraction to transform the floating point representation into bit-vectors [19].…”

Section: Adapting Xorsample' For Output Diversitymentioning

confidence: 99%

Output Sampling for Output Diversity in Automatic Unit Test Generation

Menéndez

Boreale

Gorla

et al. 2022

IIEEE Trans. Software Eng.

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Diverse test sets are able to expose bugs that test sets generated with structural coverage techniques cannot discover. Input-diverse test set generators have been shown to be effective for this, but also have limitations: e.g., they need to be complemented with semantic information derived from the Software Under Test. We demonstrate how to drive the test set generation process with semantic information in the form of output diversity. We present the first totally automatic output sampling for output diversity unit test set generation tool, called OutGen. OutGen transforms a program into an SMT formula in bit-vector arithmetic. It then applies universal hashing in order to generate an output-based diverse set of inputs. The result offers significant diversity improvements when measured as a high output uniqueness count. It achieves this by ensuring that the test set's output probability distribution is uniform, i.e. highly diverse. The use of output sampling, as opposed to any of input sampling, CBMC, CAVM, behaviour diversity or random testing improves mutation score and bug detection by up to 4150% and 963% respectively on programs drawn from three different corpora: the R-project, SIR and CodeFlaws. OutGen test sets achieve an average mutation score of up to 92%, and 70% of the test sets detect the defect. Moreover, OutGen is the only automatic unit test generation tool that is able to detect bugs on the real number C functions from the R-project.

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Section: Adapting Xorsample' For Output Diversitymentioning

confidence: 99%

Output Sampling for Output Diversity in Automatic Unit Test Generation

Menéndez

Boreale

Gorla

et al. 2022

IIEEE Trans. Software Eng.

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“…More general-though usually more expensive-SAT and SMT solvers also exist for model counting over mixed theories (e.g., [62], [63], [64]). The growing research interest in model counting for program analysis and artificial intelligence is driving a substantial research effort discovering new fragments of theories where efficient solutions are possible (e.g., [65], [66]) and are expected to directly benefit quantitative program analysis in the coming years.…”

Section: Instantiation Of Estimatementioning

confidence: 99%

Conditional Quantitative Program Analysis

Gerrard

Borges

Dwyer

et al. 2022

IIEEE Trans. Software Eng.

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Standards for certifying safety-critical systems have evolved to permit the inclusion of evidence generated by program analysis and verification techniques. The past decade has witnessed the development of several program analyses that are capable of computing guarantees on bounds for the probability of failure. This paper develops a novel program analysis framework, CQA, that combines evidence from different underlying analyses to compute bounds on failure probability. It reports on an evaluation of different CQA-enabled analyses and implementations of state-of-the-art quantitative analyses to evaluate their relative strengths and weaknesses. To conduct this evaluation, we filter an existing verification benchmark to reflect certification evidence generation challenges. Our evaluation across the resulting set of 136 C programs, totaling more than 385k SLOC, each with a probability of failure below 10 −4 , demonstrates how CQA extends the state-of-the-art. The CQA infrastructure, including tools, subjects, and generated data is publicly available at bitbucket.org/mgerrard/cqa.

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“…Due to the propositional setting, however, WMC is limited to finite domain discrete random variables, which have spurred considerable interest in generalizing WMC to continuous and hybrid distributions [16,18,43]. Weighted model integration (WMI) is a computational abstraction for computing probabilities with continuous and mixed discrete-continuous distributions [16].…”

Section: Weighted Model Integrationmentioning

confidence: 99%

Probabilistic Tractable Models in Mixed Discrete-Continuous Domains

2021

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We study the problem of the unsupervised learning of graphical models in mixed discrete-continuous domains. The problem of unsupervised learning of such models in discrete domains alone is notoriously challenging, compounded by the fact that inference is computationally demanding. The situation is generally believed to be significantly worse in discrete-continuous domains: estimating the unknown probability distribution of given samples is often limited in practice to a handful of parametric forms, and in addition to that, computing conditional queries needs to carefully handle low-probability regions in safety-critical applications. In recent years, the regime of tractable learning has emerged, which attempts to learn a graphical model that permits efficient inference. Most of the results in this regime are based on arithmetic circuits, for which inference is linear in the size of the obtained circuit. In this work, we show how, with minimal modifications, such regimes can be generalized by leveraging efficient density estimation schemes based on piecewise polynomial approximations. Our framework is realized on a recent computational abstraction that permits efficient inference for a range of queries in the underlying language. Our empirical results show that our approach is effective, and allows a study of the trade-off between the granularity of the learned model and its predictive power.

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Approximate counting in SMT and value estimation for probabilistic programs

Cited by 32 publications

References 51 publications

Output Sampling for Output Diversity in Automatic Unit Test Generation

Output Sampling for Output Diversity in Automatic Unit Test Generation

Conditional Quantitative Program Analysis

Probabilistic Tractable Models in Mixed Discrete-Continuous Domains

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