Data-driven precondition inference with learned features
We extend the data-driven approach to inferring preconditions for code from a set of test
executions. Prior work requires a fixed set of features, atomic predicates that define the …
executions. Prior work requires a fixed set of features, atomic predicates that define the …
From invariant checking to invariant inference using randomized search
We describe a general framework c2i for generating an invariant inference procedure from
an invariant checking procedure. Given a checker and a language of possible invariants, c2i …
an invariant checking procedure. Given a checker and a language of possible invariants, c2i …
Combining model checking and data-flow analysis
Until recently, model checking and data-flow analysis—two traditional approaches to
software verification—were used independently and in isolation for solving similar problems …
software verification—were used independently and in isolation for solving similar problems …
Automated assume-guarantee reasoning through implicit learning
We propose a purely implicit solution to the contextual assumption generation problem in
assume-guarantee reasoning. Instead of improving the L* algorithm—a learning algorithm …
assume-guarantee reasoning. Instead of improving the L* algorithm—a learning algorithm …
Inferring loop invariants by mutation, dynamic analysis, and static checking
Verifiers that can prove programs correct against their full functional specification require, for
programs with loops, additional annotations in the form of loop invariants-properties that …
programs with loops, additional annotations in the form of loop invariants-properties that …
Automatically inferring quantified loop invariants by algorithmic learning from simple templates
By combining algorithmic learning, decision procedures, predicate abstraction, and simple
templates, we present an automated technique for finding quantified loop invariants. Our …
templates, we present an automated technique for finding quantified loop invariants. Our …
Termination analysis with algorithmic learning
An algorithmic-learning-based termination analysis technique is presented. The new
technique combines transition predicate abstraction, algorithmic learning, and decision …
technique combines transition predicate abstraction, algorithmic learning, and decision …
Learning boolean functions incrementally
YF Chen, BY Wang - International Conference on Computer Aided …, 2012 - Springer
Classical learning algorithms for Boolean functions assume that unknown targets are
Boolean functions over fixed variables. The assumption precludes scenarios where …
Boolean functions over fixed variables. The assumption precludes scenarios where …
Predicate generation for learning-based quantifier-free loop invariant inference
We address the predicate generation problem in the context of loop invariant inference.
Motivated by the interpolation-based abstraction refinement technique, we apply the …
Motivated by the interpolation-based abstraction refinement technique, we apply the …
Comparing learning algorithms in automated assume-guarantee reasoning
We compare two learning algorithms for generating contextual assumptions in automated
assume-guarantee reasoning. The CDNF algorithm implicitly represents contextual …
assume-guarantee reasoning. The CDNF algorithm implicitly represents contextual …