Filters








10,909 Hits in 3.1 sec

Code2Inv: A Deep Learning Framework for Program Verification [chapter]

Xujie Si, Aaditya Naik, Hanjun Dai, Mayur Naik, Le Song
2020 Lecture Notes in Computer Science  
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

Jiaying Li, Jun Sun, Li Li, Quang Loc Le, Shang-Wei Lin
2017 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE)  
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]

Sahil Bhatia, Saswat Padhi, Nagarajan Natarajan, Rahul Sharma, Prateek Jain
2020 arXiv   pre-print
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

Kung-Kiu Lau
2007 Proceedinds of the 38th SIGCSE technical symposium on Computer science education - SIGCSE '07  
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

Kung-Kiu Lau
2007 ACM SIGCSE Bulletin  
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

Omar M. Alhawi, Herbert Rocha, Mikhail R. Gadelha, Lucas C. Cordeiro, Eddie Batista
2020 International Journal on Software Tools for Technology Transfer (STTT)  
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]

Andreas Humenberger, Laura Kovács
2021 Lecture Notes in Computer Science  
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]

Daisuke Ishii, Guillaume Melquiond, Shin Nakajima
2013 Lecture Notes in Computer Science  
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

Xueqi Cheng, Michael S. Hsiao
2008 2008 Design, Automation and Test in Europe  
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

Xueqi Cheng, Michael S. Hsiao
2008 Proceedings of the conference on Design, automation and test in Europe - DATE '08  
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]

Rahul Sharma, Saurabh Gupta, Bharath Hariharan, Alex Aiken, Aditya V. Nori
2013 Lecture Notes in Computer Science  
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]

Joost-Pieter Katoen
2015 Lecture Notes in Computer Science  
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]

Mirco Giacobbe, Daniel Kroening, Julian Parsert
2022 arXiv   pre-print
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]

Gabriel Ryan, Justin Wong, Jianan Yao, Ronghui Gu, Suman Jana
2019 arXiv   pre-print
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]

Soumya Banerjee
2009 arXiv   pre-print
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
« Previous Showing results 1 — 15 out of 10,909 results