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Code2Inv: A Deep Learning Framework for Program Verification
[chapter]
2020
Lecture Notes in Computer Science
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We demonstrate the flexibility of Code2Inv by means of two small-scale yet expressive instances: a loop invariant synthesizer for C programs, and a Constrained Horn Clause (CHC) solver. ...
We propose a general end-to-end deep learning framework Code2Inv, which takes a verification task and a proof checker as input, and automatically learns a valid proof for the verification task by interacting ...
We thank the reviewers for insightful comments. We thank Elizabeth Dinella, Pardis Pashakhanloo, and Halley Young for feedback on improving the paper. ...
doi:10.1007/978-3-030-53291-8_9
fatcat:zwzve6xymvezrie4rdbv6uwj3u
Automatic loop-invariant generation anc refinement through selective sampling
2017
2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)
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In this work, we propose a technique for automatic loop-invariant generation through a combination of active learning and verification. ...
Automatic loop-invariant generation is important in program analysis and verification. ...
INTRODUCTION Automatic loop-invariant generation is fundamental for program analysis. A loop invariant can be useful for software verification, compiler optimization, program understanding, etc. ...
doi:10.1109/ase.2017.8115689
dblp:conf/kbse/LiSLLL17
fatcat:7e5e2aae6fatzmpwy7jojosn7m
On Scaling Data-Driven Loop Invariant Inference
[article]
2020
arXiv
pre-print
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Once all the invariants have been specified, software verification reduces to checking of verification conditions. ...
Although static analyses to infer invariants have been studied for over forty years, recent years have seen a flurry of data-driven invariant inference techniques which guess invariants from examples instead ...
Program Verification and loop invariants The first step in program verification is defining a specification for the desired property. ...
arXiv:1911.11728v2
fatcat:qtqbwjoln5f6fnetgf7u5gp24i
Active learning sheets for a beginner's course on reasoning about imperative programs
2007
Proceedinds of the 38th SIGCSE technical symposium on Computer science education - SIGCSE '07
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We decided to support active learning on a beginner's course on Reasoning about Imperative Programs because our students find the material challenging. ...
Because of the large class size and resource implications, we opted for a simple approach: the modified lecture format. We devised active learning sheets for use during lectures. ...
function for the loop is: 5 − i
Figure 4 : 4 Active learning sheet for loop invariants.
. ...
doi:10.1145/1227310.1227382
dblp:conf/sigcse/Lau07
fatcat:e4snsxspkngtjohdec5zipoewm
Active learning sheets for a beginner's course on reasoning about imperative programs
2007
ACM SIGCSE Bulletin
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We decided to support active learning on a beginner's course on Reasoning about Imperative Programs because our students find the material challenging. ...
Because of the large class size and resource implications, we opted for a simple approach: the modified lecture format. We devised active learning sheets for use during lectures. ...
function for the loop is: 5 − i
Figure 4 : 4 Active learning sheet for loop invariants.
. ...
doi:10.1145/1227504.1227382
fatcat:z27xmogsqrhknew77gqh7voj6q
Verification and refutation of C programs based on k-induction and invariant inference
2020
International Journal on Software Tools for Technology Transfer (STTT)
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loop invariants. ...
We apply two invariant generators to produce program invariants and feed these into a k-induction-based verification algorithm implemented in DepthK, which uses the efficient SMT-based context-bounded ...
[21] proposed combining refinement types with the machine-learning-based for invariant discovery in ICE framework [36] suitable for higher-order program verification. Champion et al. ...
doi:10.1007/s10009-020-00564-1
fatcat:let4uuegzbgvtllmdrswwzeqy4
Algebra-Based Synthesis of Loops and Their Invariants (Invited Paper)
[chapter]
2021
Lecture Notes in Computer Science
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2021 pre-print arXiv:2103.03599v1 fatcat:shwdhvngh5a7ph5eb6cnqy2hcm
By reverse engineering invariant synthesis, we then describe an automated method for synthesising program loops satisfying a given set of polynomial loop invariants. ...
In this paper we overview some of our results for both of these scenarios when analysing programs with loops. ...
We thank Maximillian Jaroschek (TU Wien) for joint work allowing to extend our invariant generation approaches to more complex loops and number sequences. ...
doi:10.1007/978-3-030-67067-2_2
fatcat:mzjf6jjlgndu3ehhiwv4v4w7oi
Inductive Verification of Hybrid Automata with Strongest Postcondition Calculus
[chapter]
2013
Lecture Notes in Computer Science
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Our proposed algorithm efficiently performs inductive reasoning by unrolling the execution for some steps and generating loop invariants from verification failures. ...
We propose an automated logical analytic method for verifying a class of hybrid automata. ...
Acknowledgments The authors are indebted to the anonymous referees for their helpful comments. This work was partially funded by JSPS (KAKENHI 23-3810). ...
doi:10.1007/978-3-642-38613-8_10
fatcat:zy4cq63vebcczhikod2mzlngiu
Simulation-Directed Invariant Mining for Software Verification
2008
2008 Design, Automation and Test in Europe
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When these learned invariants are added as constraints to the bounded model checking instances of the software, they help to significantly reduce the search space. ...
With the advance of SAT solvers, transforming a software program to a propositional formula has generated much interest for bounded model checking of software in recent years. ...
Two different types of learning were applied: 1) offline learning: invariants are computed statically and relatively independent from verification process, such as invariants in the form of ±x±y≤c in ...
doi:10.1109/date.2008.4484757
dblp:conf/date/ChengH08
fatcat:k6nevg6fn5dezbhmaieu2d6ffa
Simulation-directed invariant mining for software verification
2008
Proceedings of the conference on Design, automation and test in Europe - DATE '08
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When these learned invariants are added as constraints to the bounded model checking instances of the software, they help to significantly reduce the search space. ...
With the advance of SAT solvers, transforming a software program to a propositional formula has generated much interest for bounded model checking of software in recent years. ...
Two different types of learning were applied: 1) offline learning: invariants are computed statically and relatively independent from verification process, such as invariants in the form of ±x±y≤c in ...
doi:10.1145/1403375.1403541
fatcat:s2yn6ncgbbgttnaujcwjei5g3y
Verification as Learning Geometric Concepts
[chapter]
2013
Lecture Notes in Computer Science
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We formalize the problem of program verification as a learning problem, showing that invariants in program verification can be regarded as geometric concepts in machine learning. ...
Using samples for reachable and bad states and by applying well known machine learning algorithms for classification, we are able to generate inductive assertions. ...
Acknowledgements We thank Hongseok Yang and the anonymous reviewers for their constructive comments. ...
doi:10.1007/978-3-642-38856-9_21
fatcat:g2h32zq7lngnbnetfymuc4h2ca
Probabilistic Programming: A True Verification Challenge
[chapter]
2015
Lecture Notes in Computer Science
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Loop invariants of probabilistic programs typically involve quantitative statements and synthesizing them requires more involved techniques than for ordinary programs [12] . ...
Probabilistic programming is at the heart of machine learning for describing distribution functions; Bayesian inference is pivotal in their analysis. ...
Loop invariants of probabilistic programs typically involve quantitative statements and synthesizing them requires more involved techniques than for ordinary programs [12] . ...
doi:10.1007/978-3-319-24953-7_1
fatcat:i7bwx2oaufguhpzbedvqoap26m
Neural Termination Analysis
[article]
2022
arXiv
pre-print
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This includes programs that use loop guards with disjunctions and programs that exhibit nonlinear behaviour. ...
We learn ranking functions from execution traces by training a neural network so that its output decreases along the sampled executions; then, we use symbolic reasoning to formally verify that it generalises ...
ACKNOWLEDGMENTS We are grateful to Isaac Dunn, Hosein Hasanbeig and Hadrien Pouget for revising parts of this manuscript. ...
arXiv:2102.03824v3
fatcat:ympkdbamffejzgl3fe6swlkt64
CLN2INV: Learning Loop Invariants with Continuous Logic Networks
[article]
2019
arXiv
pre-print
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Inferring loop invariants is one of the main challenges behind automated verification of real-world programs which often contain many loops. ...
In this paper, we present Continuous Logic Network (CLN), a novel neural architecture for automatically learning loop invariants directly from program execution traces. ...
Learning loop invariants
for program verification. In Advances in Neural Information Processing Systems, pp. 7751-7762,
2018. ...
arXiv:1909.11542v3
fatcat:wgtfpa47brcrlfanboif76cxgm
An Immune System Inspired Approach to Automated Program Verification
[article]
2009
arXiv
pre-print
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It is proposed to use this AIS algorithm for a specific automated program verification task: that of predicting shape of program invariants. ...
It is shown that the algorithm correctly predicts program invariant shape for a variety of benchmarked programs. ...
Deepak Kapur and ThanhVu Nguyen for helpful comments. ...
arXiv:0905.2649v1
fatcat:5rdy2r4unrddxn772iw4nd45om
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