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On Scaling Data-Driven Loop Invariant Inference [article]

Sahil Bhatia, Saswat Padhi, Nagarajan Natarajan, Rahul Sharma, Prateek Jain
2020 arXiv   pre-print
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  ...  the invariant inference track of the Syntax Guided Synthesis competition.  ...  Data-driven invariant inference techniques can handle challenging loops with confusing program text by applying ML techniques to mine patterns directly from data.  ... 
arXiv:1911.11728v2 fatcat:qtqbwjoln5f6fnetgf7u5gp24i

LoopInvGen: A Loop Invariant Generator based on Precondition Inference [article]

Saswat Padhi and Rahul Sharma and Todd Millstein
2019 arXiv   pre-print
Instead, we start with no initial features, and use program synthesis techniques to grow the set on demand.  ...  We describe the LoopInvGen tool for generating loop invariants that can provably guarantee correctness of a program with respect to a given specification.  ...  We extend the data-driven paradigm for inferring sufficient loop invariants.  ... 
arXiv:1707.02029v4 fatcat:mywfv2j3x5c7vdkxpvq67cjx5e

Data-Driven Invariant Learning for Probabilistic Programs [article]

Jialu Bao, Nitesh Trivedi, Drashti Pathak, Justin Hsu, Subhajit Roy
2022 arXiv   pre-print
Guided by this perspective, we develop the first data-driven invariant synthesis method for probabilistic programs.  ...  We also develop a data-driven approach to learn sub-invariants from data, which can be used to upper- or lower-bound expected values.  ...  Hanoi [32] uses counterexample-based inductive synthesis (CEGIS) [38] to build a data-driven invariant inference engine that alternates between weakening and strengthening candidates for synthesis.  ... 
arXiv:2106.05421v3 fatcat:33jj7grb4jenlksdd35joqfpuy

Learning Loop Invariants for Program Verification

Xujie Si, Hanjun Dai, Mukund Raghothaman, Mayur Naik, Le Song
2018 Neural Information Processing Systems  
A fundamental problem in program verification concerns inferring loop invariants. The problem is undecidable and even practical instances are challenging.  ...  theorem prover only after the complete loop invariant is proposed.  ...  The central role played by loop invariants in program verification has led to a large body of work to automatically infer them.  ... 
dblp:conf/nips/SiDRNS18 fatcat:v6p2voe25zbqrgl7ofqq6s4nlq

Toward a Motor Theory of Sign Language Perception [chapter]

Sylvie Gibet, Pierre-François Marteau, Kyle Duarte
2012 Lecture Notes in Computer Science  
This paper focuses on the imbrication of motion and meaning for the analysis, synthesis and evaluation of sign language gestures.  ...  Researches on signed languages still strongly dissociate lin- guistic issues related on phonological and phonetic aspects, and gesture studies for recognition and synthesis purposes.  ...  Alternatively, data-driven animation methods can be substituted for these pure synthesis methods.  ... 
doi:10.1007/978-3-642-34182-3_15 fatcat:t46atl45wjeuvkxlzpeeja4bju

Developing Verified Software Using Leon [chapter]

Viktor Kuncak
2015 Lecture Notes in Computer Science  
We have also developed resource bound invariant inference for Leon and used it to check abstract worst-case execution time.  ...  Synthesis in Leon is currently based on a custom deductive synthesis framework incorporating, for example, syntax-driven rules, rules supporting synthesis procedures, and a form of counterexample-guided  ...  In this approach we have also shown that function postconditions can be inferred or strengthened automatically.  ... 
doi:10.1007/978-3-319-17524-9_2 fatcat:7662wo733nhhhmxyzzrtsb2ucm

Data-driven equivalence checking

Rahul Sharma, Eric Schkufza, Berkeley Churchill, Alex Aiken
2013 Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications - OOPSLA '13  
We present a data driven algorithm for equivalence checking of two loops. The algorithm infers simulation relations using data from test runs.  ...  The algorithm is sound: insufficient data will cause the proof to fail.  ...  For DDEC to handle such programs we would need better invariant inference algorithms that can infer quantified invariants fully automatically.  ... 
doi:10.1145/2509136.2509509 dblp:conf/oopsla/0001SCA13 fatcat:gq6o6vl6anconhxxviqblguski

Data-driven equivalence checking

Rahul Sharma, Eric Schkufza, Berkeley Churchill, Alex Aiken
2013 SIGPLAN notices  
We present a data driven algorithm for equivalence checking of two loops. The algorithm infers simulation relations using data from test runs.  ...  The algorithm is sound: insufficient data will cause the proof to fail.  ...  For DDEC to handle such programs we would need better invariant inference algorithms that can infer quantified invariants fully automatically.  ... 
doi:10.1145/2544173.2509509 fatcat:6yzrt2snrzaifbea2yrtmbwnsm

Learning Concise Models from Long Execution Traces [article]

Natasha Yogananda Jeppu, Tom Melham, Daniel Kroening, John O'Leary
2020 arXiv   pre-print
We describe a new algorithm for automatically extracting useful models, as automata, from execution traces of a HW/SW system driven by software exercising a use-case of interest.  ...  Our algorithm leverages modern program synthesis techniques to generate predicates on automaton edges, succinctly describing system behaviour.  ...  We thank Daniel Bristot for his help with the RT Linux Kernel.  ... 
arXiv:2001.05230v3 fatcat:m3sfunqbobcdvhmb3rgieyfk7i

Verified lifting of stencil computations

Shoaib Kamil, Alvin Cheung, Shachar Itzhaky, Armando Solar-Lezama
2016 SIGPLAN notices  
, with the translated code achieving median performance speedups of 4.1× and up to 24× as compared to the original implementation.  ...  The technique is sound and mostly automated, and leverages counter-example guided inductive synthesis (CEGIS) to find provably correct translations.  ...  For example, the largest stencil computation for which we applied our system required it to automatically infer five loop invariants each with five universally quantified variables and 457 AST nodes.  ... 
doi:10.1145/2980983.2908117 fatcat:wxgagts4ajbt3lpxstczysqjyi

Verified lifting of stencil computations

Shoaib Kamil, Alvin Cheung, Shachar Itzhaky, Armando Solar-Lezama
2016 Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation - PLDI 2016  
, with the translated code achieving median performance speedups of 4.1× and up to 24× as compared to the original implementation.  ...  The technique is sound and mostly automated, and leverages counter-example guided inductive synthesis (CEGIS) to find provably correct translations.  ...  For example, the largest stencil computation for which we applied our system required it to automatically infer five loop invariants each with five universally quantified variables and 457 AST nodes.  ... 
doi:10.1145/2908080.2908117 dblp:conf/pldi/KamilCIS16 fatcat:siayelnp4vb4jogvfxdsso7oxm

Termination proofs from tests

Aditya V. Nori, Rahul Sharma
2013 Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering - ESEC/FSE 2013  
On the other hand, if all loop bounds are valid, then we have a proof of termination. We also describe a simple extension to our approach that allows us to infer polynomial loop bounds automatically.  ...  If a loop bound is invalid, then the safety checker provides a test or a counterexample that is used to generate more data which is subsequently used by the next infer phase to compute better estimates  ...  Being driven by tests also allows us to easily extend TpT to infer polynomial loop bounds automatically (see Section 5) . TpT is easy to implement.  ... 
doi:10.1145/2491411.2491413 dblp:conf/sigsoft/Nori013 fatcat:qi5iybfmgnck7cdg7hfzweaduq

Language support for dynamic, hierarchical data partitioning

Sean Treichler, Michael Bauer, Alex Aiken
2013 Proceedings of the 2013 ACM SIGPLAN international conference on Object oriented programming systems languages & applications - OOPSLA '13  
We present a data driven algorithm for equivalence checking of two loops. The algorithm infers simulation relations using data from test runs.  ...  The algorithm is sound: insufficient data will cause the proof to fail.  ...  For DDEC to handle such programs we would need better invariant inference algorithms that can infer quantified invariants fully automatically.  ... 
doi:10.1145/2509136.2509545 dblp:conf/oopsla/TreichlerBA13 fatcat:tdcyzqw3qzfrvcoumlwrxxa5du

Language support for dynamic, hierarchical data partitioning

Sean Treichler, Michael Bauer, Alex Aiken
2013 SIGPLAN notices  
We present a data driven algorithm for equivalence checking of two loops. The algorithm infers simulation relations using data from test runs.  ...  The algorithm is sound: insufficient data will cause the proof to fail.  ...  For DDEC to handle such programs we would need better invariant inference algorithms that can infer quantified invariants fully automatically.  ... 
doi:10.1145/2544173.2509545 fatcat:jou3mu7bgvclxhdylv3xnssnci

Data-Driven Abductive Inference of Library Specifications [article]

Zhe Zhou, Robert Dickerson, Benjamin Delaware, Suresh Jagannathan
2021 arXiv   pre-print
In this paper, we present a novel data-driven abductive inference mechanism that infers specifications for library methods sufficient to enable verification of the library's clients.  ...  Our technique combines a data-driven learning-based framework to postulate candidate specifications, along with SMT-provided counterexamples to refine these candidates, taking special care to prevent generating  ...  Padhi et al. [2016] use program synthesis to automatically learn features on demand when inferring preconditions for data-structure manipulating library functions.  ... 
arXiv:2108.04783v2 fatcat:5kkyzimwundfvpxwkfdck7skby
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