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Predicting Rankings of Software Verification Competitions [article]

Mike Czech, Eyke Hüllermeier, Marie-Christine Jakobs, Heike Wehrheim
2017 arXiv   pre-print
Software verification competitions, such as the annual SV-COMP, evaluate software verification tools with respect to their effectivity and efficiency.  ...  Typically, the outcome of a competition is a (possibly category-specific) ranking of the tools.  ...  REPRESENTING VERIFICATION TASKS Our objective is to predict rankings of so ware veri cation competitions via machine learning.  ... 
arXiv:1703.00757v1 fatcat:rk4hob6lhjdmtfzdjf4ce4bdxe

Predicting rankings of software verification tools

Mike Czech, Eyke Hüllermeier, Marie-Christine Jakobs, Heike Wehrheim
2017 Proceedings of the 3rd ACM SIGSOFT International Workshop on Software Analytics - SWAN 2017  
Using data sets from the software verification competition SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy (rank correlation > 0.6  ...  A software developer is hence faced with the problem of choosing a tool appropriate for her program at hand. A ranking of tools on programs could facilitate the choice.  ...  REPRESENTING VERIFICATION TASKS Our objective is to predict rankings of software verification competitions via machine learning.  ... 
doi:10.1145/3121257.3121262 dblp:conf/sigsoft/CzechHJW17 fatcat:sxx7gdxg75g2pnou4qqa6si75m

PeSCo: Predicting Sequential Combinations of Verifiers [chapter]

Cedric Richter, Heike Wehrheim
2019 Msphere  
PeSCo is a tool for predicting a (likely best) sequential combination of verifiers on a given verification task and then running it.  ...  The approach is based on machine learning, more precisely on learning rankings of verifiers on verification tasks (where the ordering of verifiers is based on the SV-COMP scoring schema).  ...  Verification Approach Composing verification techniques in sequence has in the past been a promising approach in the annual software verification competition SV-COMP.  ... 
doi:10.1007/978-3-030-17502-3_19 fatcat:qzj23iv7rvcqfbme3t72l5hp5m

Empirical Software Metrics for Benchmarking of Verification Tools [chapter]

Yulia Demyanova, Thomas Pani, Helmut Veith, Florian Zuleger
2015 Lecture Notes in Computer Science  
We study empirical metrics for software source code, which can predict the performance of verification tools on specific types of software.  ...  This gives strong empirical evidence for the predictive power of our metrics and demonstrates the viability of portfolio solvers for software verification.  ...  Acknowledgements Open access funding provided by Austrian Science Fund (FWF) Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http:  ... 
doi:10.1007/978-3-319-21690-4_39 fatcat:y7dktz6gfreq5dwhlck3ho3wwi

Empirical software metrics for benchmarking of verification tools

Yulia Demyanova, Thomas Pani, Helmut Veith, Florian Zuleger
2017 Formal methods in system design  
We study empirical metrics for software source code, which can predict the performance of verification tools on specific types of software.  ...  This gives strong empirical evidence for the predictive power of our metrics and demonstrates the viability of portfolio solvers for software verification.  ...  Acknowledgements Open access funding provided by Austrian Science Fund (FWF) Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http:  ... 
doi:10.1007/s10703-016-0264-5 pmid:32103858 pmcid:PMC7010381 fatcat:mgg2g4ltdnf5defpcbhe3sdlre

Predicting SMT Solver Performance for Software Verification

Andrew Healy, Rosemary Monahan, James F. Power
2017 Electronic Proceedings in Theoretical Computer Science  
The Why3 IDE and verification system facilitates the use of a wide range of Satisfiability Modulo Theories (SMT) solvers through a driver-based architecture.  ...  Our approach benefits software engineers by providing a single utility to delegate proof obligations to the solvers most likely to return a useful result.  ...  The BenchExec [9] framework was developed by the organisers of the SVCOMP [7] software verification competition to reliably measure CPU time, wall-clock time and memory usage of software verification  ... 
doi:10.4204/eptcs.240.2 fatcat:5rfj2xpgzrbprh77rizz67d4re

Algorithm Selection for Software Verification using Graph Attention Networks [article]

Will Leeson, Matthew B Dwyer
2022 arXiv   pre-print
The field of software verification has produced a wide array of algorithmic techniques that can prove a variety of properties of a given program.  ...  We evaluate Graves on a set of 10 verification tools and over 8000 verification problems and find that it improves the state-of-the-art in verification algorithm selection by 11\%.  ...  ACKNOWLEDGEMENTS We would like to thank Hongning Wang for his advice on graph neural networks and prediction systems. This material is based in part upon work supported by the U.S.  ... 
arXiv:2201.11711v2 fatcat:xha5cjfsi5fubi2nzruft27dsm

Algorithm selection for software validation based on graph kernels

Cedric Richter, Eyke Hüllermeier, Marie-Christine Jakobs, Heike Wehrheim
2020 Automated Software Engineering : An International Journal  
The evaluation, which is based on data sets from the annual software verification competition SV-COMP, demonstrates our kernel to generalize well and to achieve rather high prediction accuracy, both for  ...  ), and (2) ranking several verification tools, from presumably best to worst, for property proving.  ...  To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.  ... 
doi:10.1007/s10515-020-00270-x fatcat:4b62vpw6izg3xce26ysdhovtp4

Class Imbalance Evolution and Verification Latency in Just-in-Time Software Defect Prediction

George G. Cabral, Leandro L. Minku, Emad Shihab, Suhaib Mujahid
2019 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE)  
Just-in-Time Software Defect Prediction (JIT-SDP) is an SDP approach that makes defect predictions at the software change level.  ...  Compared to state-of-the-art class imbalance evolution learning approaches, the predictive performance of JIT-SDP approaches was up to 97.2% lower in terms of g-mean.  ...  RQ4 How to improve the predictive performance of JIT-SDP, given class imbalance evolution and verification latency?  ... 
doi:10.1109/icse.2019.00076 dblp:conf/icse/CabralMSM19 fatcat:jzy2udsihrgnhfd3favdvbwpam

2019 Index IEEE Transactions on Software Engineering Vol. 45

2020 IEEE Transactions on Software Engineering  
Matinnejad, R., +, TSE Sept. 2019 919-944 Verification Templates for the Analysis of User Interface Software Design.  ...  ., +, TSE July 2019 683-711 Ranking (statistics) Correction of "A Comparative Study to Benchmark Cross-Project Defect Prediction Approaches".  ...  T Terminology Correction of "A Comparative Study to Benchmark Cross-Project Defect Prediction Approaches".  ... 
doi:10.1109/tse.2019.2960169 fatcat:526iwottmfazrfp5x2ojop4sh4

MachSMT: A Machine Learning-based Algorithm Selector for SMT Solvers [chapter]

Joseph Scott, Aina Niemetz, Mathias Preiner, Saeed Nejati, Vijay Ganesh
2021 Lecture Notes in Computer Science  
(e.g., BV, LIA, NRA, etc.) in verification, program analysis, and software engineering.  ...  Given an SMT formula $$\mathcal {I}$$ I as input, MachSMT leverages these learnt models to output a ranking of solvers based on predicted run time on the formula $$\mathcal {I}$$ I .  ...  At runtime, given I, MachSMT queries all EHMs for all solvers (that were considered during training) over I, and outputs a ranking of solvers based on their predicted runtimes (top-ranked solver is predicted  ... 
doi:10.1007/978-3-030-72013-1_16 fatcat:xpdqzsxqnvfvplyd7gp5sd2qre

A Learning Framework for Intelligent Selection of Software Verification Algorithms

Weipeng Cao, Zhongwu Xie, Xiaofei Zhou, Zhiwu Xu, Cong Zhou, Georgios Theodoropoulos, Qiang Wang
2020 Journal on Artificial Intelligence  
Software verification is a key technique to ensure the correctness of software.  ...  In this work, we propose a general learning framework for the intelligent selection of software verification algorithms, and instantiate the framework with two state-of-the-art learning algorithms: Broad  ...  Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.  ... 
doi:10.32604/jai.2020.014829 fatcat:lgmijs5rcvdwplxakeij4v33ae

The 1st Verified Software Competition: Experience Report [chapter]

Vladimir Klebanov, Peter Müller, Natarajan Shankar, Gary T. Leavens, Valentin Wüstholz, Eyad Alkassar, Rob Arthan, Derek Bronish, Rod Chapman, Ernie Cohen, Mark Hillebrand, Bart Jacobs (+10 others)
2011 Lecture Notes in Computer Science  
We, the organizers and participants, report our experiences from the 1st Verified Software Competition, held in August 2010 in Edinburgh at the VSTTE 2010 conference.  ...  For example, for Invert we proved surjectivity of A from injectivity and boundedness.  ...  Completing Invert and N Queens required developing quite a bit of theory, which was labor-intensive but possible in VeriFast.  ... 
doi:10.1007/978-3-642-21437-0_14 fatcat:vkjleltbkrdchok44arrshxfua

Valection: design optimization for validation and verification studies

Christopher I Cooper, SMC-DNA Challenge Participants, Delia Yao, Dorota H Sendorek, Takafumi N Yamaguchi, Christine P'ng, Kathleen E Houlahan, Cristian Caloian, Michael Fraser, Kyle Ellrott, Adam A Margolin, Robert G Bristow (+2 others)
2018 BMC Bioinformatics  
for the selection of verification candidates.  ...  To determine how to create subsets of predictions for validation that maximize accuracy of global error profile inference, we developed Valection, a software program that implements multiple strategies  ...  The main Valection project page is at: http:// labs.oicr.on.ca/boutros-lab/software/valection.  ... 
doi:10.1186/s12859-018-2391-z pmid:30253747 pmcid:PMC6157051 fatcat:khcbmr546van3crehm2v7zgsbq

Multi-Criteria Models for Clusters Design

Irina Radeva
2013 Cybernetics and Information Technologies  
The first one allows selection of economic agents. The second one aims at definition of alternative cluster designs. The third model evaluates the risk of the clusters.  ...  The development of suitable software solutions based on scenario simulation would improve the accuracy of the predicted variables and solutions.  ...  The verification concerns the predicted criteria values for a post-integration period. Its objective is a confirmation that all agents maintain or improve their performance.  ... 
doi:10.2478/cait-2013-0003 fatcat:56lxn3qsvjcstkymtm7vrm2tqi
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