The PeRIPLO Propositional Interpolator
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1 The PeRIPLO Propositional Interpolator N. Sharygina Formal Verification and Security Group University of Lugano joint work with Leo Alt, Antti Hyvarinen, Grisha Fedyukovich and Simone Rollini October 2, 2015 Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
2 Outline 1 Formal Verification at USI, Lugano Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
3 Outline 1 Formal Verification at USI, Lugano 2 Interpolation-based Model Checking Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
4 Outline 1 Formal Verification at USI, Lugano 2 Interpolation-based Model Checking 3 Flexible Interpolation Framework Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
5 Outline 1 Formal Verification at USI, Lugano 2 Interpolation-based Model Checking 3 Flexible Interpolation Framework Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
6 Background Formal Verification in Lugano, Switzerland Program Verification Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
7 Background Formal Verification in Lugano, Switzerland Program Verification Model checking software (FunFrog, EvolCheck, LoopFrog), ANSI-C programs Efficient decision procedures as computational engines of verification (OpenSMT) Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
8 Background Formal Verification in Lugano, Switzerland Program Verification Model checking software (FunFrog, EvolCheck, LoopFrog), ANSI-C programs Efficient decision procedures as computational engines of verification (OpenSMT) Abstractions Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
9 Background Formal Verification in Lugano, Switzerland Program Verification Model checking software (FunFrog, EvolCheck, LoopFrog), ANSI-C programs Efficient decision procedures as computational engines of verification (OpenSMT) Abstractions Interpolation-based Bounded Model Checking Function summarization [ATVA 12] Upgrade checking, Incremental verification [FMCAD 13], [TACAS 13] Recursion depth detection [STTT 15] Verification-aided regression testing [ISSTA 13] Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
10 Background Formal Verification in Lugano, Switzerland Abstractions Leveraging Interpolant strength [CAV 12] Loop Summarization [ATVA 08], [ASE 09] Program Termination [CAV 10], [TACAS 11] Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
11 Background Formal Verification in Lugano, Switzerland Abstractions Leveraging Interpolant strength [CAV 12] Loop Summarization [ATVA 08], [ASE 09] Program Termination [CAV 10], [TACAS 11] Synergy of Abstractions [STTT 10] Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
12 Background Formal Verification in Lugano, Switzerland An SMT-based verification framework for software systems handling arrays [FMSD 15] A quantifier-free interpolation procedure extending Lazy Abstraction [McMillan 06] to a quantified level [LPAR 12] Identification of a class of relations over arrays admitting definable first-order acceleration [TACAS 13] Booster: An Acceleration-Based Verification Framework for Array Programs [ATVA 14] Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
13 Background Formal Verification in Lugano, Switzerland Boolean and Theory Reasoning (SAT/SMT) Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
14 Background Formal Verification in Lugano, Switzerland Boolean and Theory Reasoning (SAT/SMT) Proof reduction and proof manipulation for interpolation [FMSD 15] Proof Sensitive Interpolation [VSTTE 15] Search-Space Partitioning for Parallelizing SMT Solvers [SAT 15] Procedure for bit-vector extraction and concatenation [ICCAD 09] Generation of explanations in theory propagation [MEMOCODE 10] Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
15 Background Formal Verification in Lugano, Switzerland Boolean and Theory Reasoning (SAT/SMT) Solver, OpenSMT, combines MiniSAT2 SAT-Solver with state-of-the-art decision procedures for QF EUF, LRA, BV, RDL, IDL Extensible: the SAT-to-theory interface facilites design and plug-in of new decision procedures Incremental: suitable for incremental verification Open-source: available under MIT license Parallelized: efficient search space partitioning Efficient: competitive open-source SMT Solver for QF UF, IDL, RDL, LRA according to SMT-Comp. Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
16 Outline 1 Formal Verification at USI, Lugano 2 Interpolation-based Model Checking 3 Flexible Interpolation Framework Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
17 Interpolation Background WIde application in symbolic model checking Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
18 Interpolation Background WIde application in symbolic model checking Bounded model checking: approximate cheaper reachability set computation [McMillan03] Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
19 Interpolation Background WIde application in symbolic model checking Bounded model checking: approximate cheaper reachability set computation [McMillan03] Predicate abstraction refinement based on spurious behaviors [Henzinger04] Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
20 Interpolation Background WIde application in symbolic model checking Bounded model checking: approximate cheaper reachability set computation [McMillan03] Predicate abstraction refinement based on spurious behaviors [Henzinger04] Property-based transition relation approximation [Jhala05]... Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
21 Interpolation Background WIde application in symbolic model checking Bounded model checking: approximate cheaper reachability set computation [McMillan03] Predicate abstraction refinement based on spurious behaviors [Henzinger04] Property-based transition relation approximation [Jhala05]... Forementioned applications involve Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
22 Interpolation Background WIde application in symbolic model checking Bounded model checking: approximate cheaper reachability set computation [McMillan03] Predicate abstraction refinement based on spurious behaviors [Henzinger04] Property-based transition relation approximation [Jhala05]... Forementioned applications involve Problem encoding into logic (SAT, SMT) Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
23 Interpolation Background WIde application in symbolic model checking Bounded model checking: approximate cheaper reachability set computation [McMillan03] Predicate abstraction refinement based on spurious behaviors [Henzinger04] Property-based transition relation approximation [Jhala05]... Forementioned applications involve Problem encoding into logic (SAT, SMT) Problem solving by means of resolution based engines (SAT solvers, SMT solvers) Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
24 Notation Interpolation Craig s interpolant I for unsatisfiable conjunction of formulae A B [Craig57] Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
25 Notation Interpolation Craig s interpolant I for unsatisfiable conjunction of formulae A B [Craig57] A I, I B unsatisfiable Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
26 Notation Interpolation Craig s interpolant I for unsatisfiable conjunction of formulae A B [Craig57] A I, I B unsatisfiable I defined over common symbols of A and B Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
27 Notation Interpolation Craig s interpolant I for unsatisfiable conjunction of formulae A B [Craig57] A I, I B unsatisfiable I defined over common symbols of A and B I as over-approximation A conflicting with B Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
28 Notation Interpolation Craig s interpolant I for unsatisfiable conjunction of formulae A B [Craig57] A I, I B unsatisfiable I defined over common symbols of A and B I as over-approximation A conflicting with B Example Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
29 Notation Interpolation Craig s interpolant I for unsatisfiable conjunction of formulae A B [Craig57] A I, I B unsatisfiable I defined over common symbols of A and B I as over-approximation A conflicting with B Example A (p q) (p q) B (q r) (q r) Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
30 Notation Interpolation Craig s interpolant I for unsatisfiable conjunction of formulae A B [Craig57] A I, I B unsatisfiable I defined over common symbols of A and B I as over-approximation A conflicting with B Example A (p q) (p q) B (q r) (q r) Interpolant q Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
31 Notation Interpolation Craig s interpolant I for unsatisfiable conjunction of formulae A B [Craig57] A I, I B unsatisfiable I defined over common symbols of A and B I as over-approximation A conflicting with B Example A (p q) (p q) B (q r) (q r) Interpolant q A q q B unsatisfiable Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
32 Interpolation Background Craig s interpolant I for unsatisfiable conjunction of formulae A B [Craig57] I as over-approximation A conflicting with B A I B Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
33 Interpolation-based Model Checking Problems Problems Size affects efficiency Interpolants different in their logical strength are needed Collection of individual algorithms, no possibilities for adjustments wrt the model checking tasks Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
34 Outline 1 Formal Verification at USI, Lugano 2 Interpolation-based Model Checking 3 Flexible Interpolation Framework Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
35 Interpolation-based Model Checking Motivation PeRIPLO is a multi-purpose interpolation framework Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
36 Interpolation-based Model Checking Motivation PeRIPLO is a multi-purpose interpolation framework aims at producing interpolants that are suitable for the whole spectrum of interpolation applications emphasis on constructing small interpolants flexibility in strength Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
37 Interpolation-based Model Checking Motivation PeRIPLO is a multi-purpose interpolation framework aims at producing interpolants that are suitable for the whole spectrum of interpolation applications emphasis on constructing small interpolants flexibility in strength Pre-processing approaches proof reduction and compression proof manipulation for interpolation Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
38 Interpolation-based Model Checking Motivation PeRIPLO is a multi-purpose interpolation framework aims at producing interpolants that are suitable for the whole spectrum of interpolation applications emphasis on constructing small interpolants flexibility in strength Pre-processing approaches proof reduction and compression proof manipulation for interpolation Proof senstive Interpolation Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
39 The Bird s Eye View to PeRIPLO PeRIPLO proof skeleton Resolution proof optimized proof SAT solver Proof expander Proof optimizer Interpolator φ Itp Application Given an unsatisfiable propositional formula φ PeRIPLO constructs an interpolant in circuit form by 1 Solving φ and extracting a compact proof skeleton from the SAT solver 2 Expanding the proof skeleton to a resolution proof 3 Optimizing the resolution proof to a smaller proof 4 Constructing an interpolant from the optimized proof Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
40 PeRIPLO Features Basic interpolation: A B A I and I B var(i ) var(a) var(b) Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
41 PeRIPLO Features Basic interpolation: A B A I and I B var(i ) var(a) var(b) Variations: Path, Tree and DAG interpolation Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
42 PeRIPLO Features Basic interpolation: A B A I and I B var(i ) var(a) var(b) Variations: Path, Tree and DAG interpolation Proof Optimization [FMSD15, ICCAD10]: removing resolution steps which reintroduce already resolved pivot variables postponing unit resolution steps until the end of the resolution proof using different local rewriting rules which preserve the validity of the proof Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
43 PeRIPLO Interface Features Multifaceted interface A clear API for C++ for tuning the interpolation algorithm, inserting a formula to PeRIPLO, and fetching the interpolant from PeRIPLO Reading and writing smtlib2 Reading and writing the Aiger format Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
44 Interpolation in PeRIPLO Labeling functions Labeled Interpolation System (LIS) framework [D Silva et al. 2010] construction of interpolation algorithms from labeling functions generalization of various interpolation algorithms (i.e., M s [McMillan03], P [Pudlak97], M w [D Silva10]) template for labeling function L LIS framework (R, A, B) Interpolation algorithm I Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
45 Interpolation in PeRIPLO Definitions Given a resolution proof R, (v, C) denotes that the variable v occurs in a clause C of R The labeling function L assigns a label from the set {a, b, ab} to each occurrence (v, C) in R Given a propositional formula A B, a variable is either local, if it occurs only in A or B, or shared if it occurs in both A and B Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
46 Interpolation in PeRIPLO Labeling Functions [VSSTE15] The label L(v, C) = b if v is local to A and L(v, C) = a if v is local to B. The label can be chosen freely for occurrences of shared variables Tuning the label for the shared occurrences results in different interpolation algorithms Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
47 PeRIPLO and Different Interpolation Algorithms Labeling all shared variable occurrences as a results in the weakest interpolant M w available in LIS b results in a strong interpolant M s available in LIS ab results in an interpolant P that is somewhere in the middle The aforementioned approaches are fixed schemas with no possibility for adopt to the task Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
48 PeRIPLO and Proof-Sensitive Interpolation PeRIPLO offers certain labeling functions that specifically address the interpolant size: Labeling all occurrences in A as a and B as b results in an interpolant with minimum number of distinct variables By analyzing the number of occurrences in the A and B part of the proof R it is possible to construct interpolants that have a small number of connectives Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
49 The Proof-Sensitive (PS) Labeling Functions PeRIPLO implements the proof-sensitive labeling functions specifically targeted for constructing small interpolants let f A (p) = {(p, C) C A} be the number of times the variable p occurs in A, and f B (p) = {(p, C) C B} the number the variable p occurs in B. The proof-sensitive labeling function L PS is defined as { a if f A (p) f B (p) L PS (p, C) = b if f A (p) < f B (p). Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
50 The Proof-Sensitive Labeling Functions PeRIPLO also provides weak and strong versions of Proof-Sensitive Approach Hierarchy of Interpolation Algorithms provided by PeRIPLO D min M s PS s PS PS w M w P Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
51 PeRIPLO API Application Interpolant I A I B I Interpolator Labeling function Partitions A and B Strength requirement Labeling Partitions A and B f A f B Partitions A and B Proof analysis Proof of UNSAT φ = A B SAT solver SAT or UNSAT The API of PeRIPLO provides the application with a full control over the interpolant generation Many of the more routine tasks are implemented efficiently inside PeRIPLO so that the user does not need to take care of such details The system makes it comfortable to construct and experiment with new labeling functions Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
52 Reduction Approach Evaluation [ICCAD 10] Experimental results over SMT: QF UF, QF IDL, QF LRA, QF RDL # Avg nodes Avg edges Avg core T (s) Max nodes Max edges Max core RP % 7.5% 1.3% % 68.9% 39.1% Ratio % 10.7% 1.4% % 70.2% 45.7% % 11.9% 1.5% % 79.9% 45.7% % 13.0% 1.6% % 81.2% 45.7% % 13.8% 1.7% % 81.2% 45.7% % 14.4% 1.7% % 83.6% 45.7% % 16.6% 1.9% % 84.4% 45.7% % 18.4% 2.0% % 85.2% 45.7% % 19.5% 2.1% % 86.1% 45.7% % 20.4% 2.2% % 86.1% 45.7% Ratio time threshold as fraction of solving time # number of benchmarks solved Avg nodes, Avg edges, Avg core average reduction in proof size T (s) average transformation time in seconds Max nodes, Max edges, Max core max reduction in proof size Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
53 Applications - FunFrog [FMCAD12] and evolcheck [TACAS13] Bounded Model Checkers Interpolants used as Function Summaries FunFrog - C Incremental Checker Stronger interpolants suit better [CAV12] evolcheck - C Upgrade Checker Weaker interpolants suit better [CAV12] Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
54 Applications - FunFrog and evolcheck Veri cation time (seconds) M P M s PS PS PS s D min Veri cation time (seconds) P M s PS s # Benchmarks # Benchmarks PS and PS s consistently lead to better verification time Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
55 Conclusions PeRIPLO is an interpolation tool for propositional logic Generic and flexible framework for producing interpolants on demand Provides an API, an smtlib2, and an AIGER interface for communicating with other tools Particular emphasis on constructing small interpolants while maintaining guarantees of interpolant strength For more information see Future work Extending the interpolation to first-oder logics in SMT Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
56 Thank you for your attention! Natasha Sharygina (USI) Flexible Interpolation October 2, / 32
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