Learning Boolean Functions Incrementally

Chen, Yu‐Fang; Wang, Bow-Yaw

doi:10.1007/978-3-642-31424-7_10

Cited by 12 publications

(8 citation statements)

References 22 publications

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“…Since a property often depends on a subset of context variables, it suffices to find contextual assumptions over such variables. In [14], an algorithm inferring Boolean functions over relevant variables is proposed. A similar learning algorithm for BDD's may further improve the performance of symbolic assume-guarantee reasoning.…”

Section: Resultsmentioning

confidence: 99%

Symbolic assume-guarantee reasoning through BDD learning

Wang

Yin

et al. 2014

Proceedings of the 36th International Conference on Software Engineering

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Both symbolic model checking and assume-guarantee reasoning aim to circumvent the state explosion problem. Symbolic model checking explores many states simultaneously and reports numerous erroneous traces. Automated assumeguarantee reasoning, on the other hand, infers contextual assumptions by inspecting spurious erroneous traces. One would expect that their integration could further improve the capacity of model checking. Yet examining numerous erroneous traces to deduce contextual assumptions can be very time-consuming. The integration of symbolic model checking and assume-guarantee reasoning is thus far from clear. In this paper, we present a progressive witness analysis algorithm for automated assume-guarantee reasoning to exploit a multitude of traces from BDD-based symbolic model checkers. Our technique successfully integrates symbolic model checking with automated assume-guarantee reasoning by directly inferring BDD's as implicit assumptions. It outperforms monolithic symbolic model checking in four benchmark problems and an industrial case study in experiments.

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Section: Resultsmentioning

confidence: 99%

Symbolic assume-guarantee reasoning through BDD learning

Wang

Yin

et al. 2014

Proceedings of the 36th International Conference on Software Engineering

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“…A more powerful well-foundedness checker should make the framework even more effective. An incremental learning algorithm for Boolean functions [9] should improve the efficiency of our technique as well.…”

Section: Discussionmentioning

confidence: 99%

Termination Analysis with Algorithmic Learning

Lee

Wang

2012

Computer Aided Verification

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An algorithmic-learning-based termination analysis technique is presented. The new technique combines transition predicate abstraction, algorithmic learning, and decision procedures to compute transition invariants as proofs of program termination. Compared to the previous approaches that mostly aim to find a particular form of transition invariants, our technique does not commit to any particular one. For the examples that the previous approaches simply give up and report failure our technique can still prove the termination. We compare our technique with others on several benchmarks from literature including PolyRank examples, SNU realtime benchmark, and Windows device driver examples. The result shows that our technique outperforms others both in efficiency and effectiveness.

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“…Recently, learning has gained renewed interest in the context of program verification, particularly for synthesizing loop invariants [15,24,25,34,35,[49][50][51][52]. However, Garg et al [25] argue that merely learning from positive and negative examples for synthesizing invariants is inherently non-robust and introduce ICE-learning, which extends the classical learning setting with implications.…”

Section: Ice Learning Modelmentioning

confidence: 99%

Learning invariants using decision trees and implication counterexamples

et al. 2016

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Inductive invariants can be robustly synthesized using a learning model where the teacher is a program verifier who instructs the learner through concrete program configurations, classified as positive, negative, and implications. We propose the first learning algorithms in this model with implication counterexamples that are based on machine learning techniques. In particular, we extend classical decision-tree learning algorithms in machine learning to handle implication samples, building new scalable ways to construct small decision trees using statistical measures. We also develop a decision-tree learning algorithm in this model that is guaranteed to converge to the right concept (invariant) if one exists. We implement the learners and an appropriate teacher, and show that the resulting invariant synthesis is efficient and convergent for a large suite of programs.

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Learning Boolean Functions Incrementally

Cited by 12 publications

References 22 publications

Symbolic assume-guarantee reasoning through BDD learning

Symbolic assume-guarantee reasoning through BDD learning

Termination Analysis with Algorithmic Learning

Learning invariants using decision trees and implication counterexamples

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