Combining the logical and the probabilistic in program analysis

Zhang, Xin; Si, Xujie; Naik, Mayur

doi:10.1145/3088525.3088563

Cited by 8 publications

(5 citation statements)

References 53 publications

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“…Thus, it would be interesting to study how ideas and techniques from the program analysis literature can be carry over to our framework, and vice versa. For example, Zhang et al [73] consider statistical properties of program behavior to advise abstractions, Sharma et al [65] relate verification to the learnability of concepts, Holtzen et al [36] study abstract predicates for loop-free probabilistic programs, and Monniaux [51] defines abstract representations for probabilistic program path analysis. On the semantical front, Cousot and Monerau [15] present a detailed and careful analysis for reasoning about (probabilistic) nondeterminism in programs, but as argued by Holtzen et al [36], they do consider the abstractions themselves to be probabilistic structures.…”

Section: Related Work and Discussionmentioning

confidence: 99%

Abstracting probabilistic models: Relations, constraints and beyond

Belle

2020

Knowledge-Based Systems

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ion is a powerful idea widely used in science, to model, reason and explain the behavior of systems in a more tractable search space, by omitting irrelevant details. While notions of abstraction have matured for deterministic systems, the case for abstracting probabilistic models is not yet fully understood.In this paper, we provide a semantical framework for analyzing such abstractions from first principles. We develop the framework in a general way, allowing for expressive languages, including logic-based ones that admit relational, deterministic and hierarchical constructs with stochastic primitives. We motivate a definition of consistency between a high-level model and its low-level counterpart, but also treat the case when the high-level model is missing critical information present in the low-level model. We go on to prove properties of abstractions, both at the level of the parameter as well as the structure of the models. We conclude with some observations about how abstractions can be derived automatically.

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Section: Related Work and Discussionmentioning

confidence: 99%

Abstracting probabilistic models: Relations, constraints and beyond

Belle

2020

Knowledge-Based Systems

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“…Thus, it would be interesting to study how ideas and techniques from the program analysis literature can be carry over to our framework, and vice versa. For example, [63] consider statistical properties of program behavior to advise abstractions, [55] relate verification to the learnability of concepts, [32] study abstract predicates for loop-free probabilistic programs, and [43] defines abstract representations for probabilistic program path analysis. On the semantical front, [11] present a detailed and careful analysis for reasoning about (probabilistic) nondeterminism in programs, but as argued in [32], they do consider the abstractions themselves to be probabilistic structures.…”

Section: Related Work and Discussionmentioning

confidence: 99%

Abstracting Probabilistic Models: A Logical Perspective

Belle

2018

Preprint

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ion is a powerful idea widely used in science, to model, reason and explain the behavior of systems in a more tractable search space, by omitting irrelevant details. While notions of abstraction have matured for deterministic systems, the case for abstracting probabilistic models is not yet fully understood.In this paper, we provide a semantical framework for analyzing such abstractions from first principles. We develop the framework in a general way, allowing for expressive languages, including logic-based ones that admit relational and hierarchical constructs with stochastic primitives. We motivate a definition of consistency between a high-level model and its low-level counterpart, but also treat the case when the high-level model is missing critical information present in the low-level model. We prove properties of abstractions, both at the level of the parameter as well as the structure of the models. We conclude with some observations about how abstractions can be derived automatically.

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“…Static analysis is known to produce a lot of false positives. To suppress them, several machine learning based approaches [25], [26], [27], [28], [10], [29], [30], [31], [49], [32], [33] have been proposed. Because they either target different languages or different static analyzers, they are not directly applicable.…”

Section: Table V Aumentioning

confidence: 99%

“…To suppress them, various methods have been proposed (as summarized in [24]). Among them, machine learning based approaches [25], [26], [27], [28], [10], [29], [30], [31], [32], [33] focus on learning the patterns of false positives from examples. However, training such models requires good labeled datasets.…”

Section: Introductionmentioning

confidence: 99%

D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis

Zheng¹,

Pujar²,

Lewis³

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP)

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Static analysis tools are widely used for vulnerability detection as they understand programs with complex behavior and millions of lines of code. Despite their popularity, static analysis tools are known to generate an excess of false positives. The recent ability of Machine Learning models to understand programming languages opens new possibilities when applied to static analysis. However, existing datasets to train models for vulnerability identification suffer from multiple limitations such as limited bug context, limited size, and synthetic and unrealistic source code. We propose D2A, a differential analysis based approach to label issues reported by static analysis tools. The D2A dataset is built by analyzing version pairs from multiple open source projects. From each project, we select bug fixing commits and we run static analysis on the versions before and after such commits. If some issues detected in a before-commit version disappear in the corresponding after-commit version, they are very likely to be real bugs that got fixed by the commit. We use D2A to generate a large labeled dataset to train models for vulnerability identification. We show that the dataset can be used to build a classifier to identify possible false alarms among the issues reported by static analysis, hence helping developers prioritize and investigate potential true positives first.

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Combining the logical and the probabilistic in program analysis

Cited by 8 publications

References 53 publications

Abstracting probabilistic models: Relations, constraints and beyond

Abstracting probabilistic models: Relations, constraints and beyond

Abstracting Probabilistic Models: A Logical Perspective

D2A: A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis

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