1. Introduction to Computer-Aided Verification Rajeev Alur University of Pennsylvania CAV Mentoring Workshop, July 2015

2. Systems Software Can Microsoft Windows version X be bug-free? Millions of lines of code Types of bugs that cause crashes well-known Enormous effort spent on debugging/testing code Certifying third-party code (e.g. device drivers) do{ KeAcquireSpinLock(); nPacketsOld = nPackets; if(request){ request = request->Next; KeReleaseSpinLock(); nPackets++; } }while(nPackets!= nPacketsOld); KeReleaseSpinLock(); Do lock operations, acquire and release strictly alternate on every program execution?

3. Concurrency Libraries Exploiting concurrency efficiently and correctly dequeue(queue_t *queue, value_t *pvalue) { node_t *head; node_t *tail; node_t *next; while (true) { head = queue->head; tail = queue->tail; next = head->next; if (head == queue->head) { if (head == tail) { if (next == 0) return false; cas(&queue->tail, tail, next); } else { *pvalue = next->value; if (cas(&queue->head, head, next)) break; } delete_node(head); return true; } Concurrent Queue (MS’92) Can the code deadlock? Is sequential semantics of a queue preserved? (Sequential consistency)

4. Security Checks for Java Applets How to certify applications for data integrity / confidentiality ? By listening to messages, can one infer whether a particular entry is in the addressbook? https://java.sun.com/javame/ public Vector phoneBook; public String number; public int Selected; public void sendEvent() { phoneBook = getPhoneBook(); selected = chhoseReceiver(); number=phoneBook.elementAt(selected); if ((number==null)|(number=“”)){ //output error } else{ String message = inputMessage(); sendMessage(number, message); } EventSharingMidlet from J2ME

5. Certification of Safety-Critical Software How to verify that a pacemaker meets all the correctness requirements published by the FDA ?

6.  Correctness is formalized as a mathematical claim to be proved or falsified rigorously Always with respect to the given specification  Challenge: Impossibility results for automated verifier Verification problem is undecidable Even approximate versions are computationally intractable (model checking is Pspace-hard) Verifier software/model correctness specification yes/proof no/bug In Search of the Holy Grail…

7.  History of CAV (not comprehensive…)  Some guidelines for choosing a research problem This Talk

8. BubbleSort (A : array[1..n] of int) { B = A : array[1..n] of int; for (i=0; i<n; i++) { Permute(A,B) Sorted(B[n-i,n]) for 0<k<=n-i-1 and n-i<=k’<=n B[k]<=B[k’] for (j=0; j<n-i; j++) { Permute(A,B), Sorted(B[n-i,n], for 0<k<=n-i-1 and n-i<=k’<=n B[k]<=B[k’] for 0<k<j B[k] <= B[j] if (B[j]>B[j+1]) swap(B,j,j+1) } }; return B; } 1970s: Proof calculi for program correctness BubbleSort (A : array[1..n] of int) { B = A : array[1..n] of int; for (i=0; i<n; i++) { for (j=0; j<n-i; j++) { if (B[j]>B[j+1]) swap(B,j,j+1) } }; return B; } Key to proof: Finding suitable loop invariants

9. Deductive Program Verification  Powerful mathematical logic (e.g. first-order logic, Higher- order logics) needed for formalization  Great progress in decision procedures  Finding proof decomposition requires expertise, but modern tools support many built-in proof tactics  Contemporary theorem provers: Coq, PVS, ACL2, ESC-Java  In practice …  User partially annotates the program with invariants, and the tool infers remaining invariants needed to complete the proof  Success story: CompCert: Fully verified optimizing compiler for a subset of C  Current research: Automatic synthesis of loop invariants

10. 1980s: Finite-state Protocol Analysis Automated analysis of finite-state protocols with respect to temporal logic specifications  Network protocols, Distributed algorithms Specs: Is there a deadlock? Does every req get ack? Does a buffer overflow? Tools: SPIN, Murphi, CADP …

11. Battling State-space Explosion Analysis is basically a reachability problem in a HUGE graph  Size of graph grows exponentially as the number of bits required for state encoding  Graph is constructed only incrementally, on-the-fly  Many techniques for exploiting structure: symmetry, data independence, hashing, partial order reduction …  Great flexibility in modeling: Scale down parameters (buffer size, number of network nodes…) State Transition Bad states

12. 1990s: Symbolic Model Checking Constraint-based analysis of Boolean systems  Symbolic Boolean representations (propositional formulas, OBDDs) used to encode system dynamics  Success in finding high-quality bugs in hardware applications (VHDL/Verilog code) M P UIC MP Global bus Cluster bus Read-shared/read-owned/write-invalid/write-shared/… Deadlock found in cache coherency protocol Gigamax by model checker SMV

13. Symbolic Reachability Problem Model variables X ={x1, … xn} Each var is of finite type, say, boolean Initialization: I(X): a formula over X e.g. (x1 && ~x2) Update: T(X,X’) How new vars X’ are related to old vars X as a result of executing one step of the program: Disjunction of clauses obtained by compiling individual instructions e.g. (x1 && x1’ = x1 && x2’ = ~x2 && x3’ = x3) Target set: F(X) e.g. (x2 && x3) Computational problem: Can F be satisfied starting with I by repeatedly applying T ? K-step reachability reduces to propositional satisfiability (SAT): Bounded Model Checking I(X 0 ) && T(X 0,X 1 ) && T(X 1,X 2 ) && --- && T(X k-1,X k ) && F(X k )

14. The Story of SAT 2001 Chaff  10k var 1986 BDDs  100 var 1992 GSAT  300 var 1996 Stålmarck  1000 var 1996 GRASP  1k var 1960 DP  10 var 1988 SOCRATES  3k var 1994 Hannibal  3k var 1962 DLL  10 var 1952 Quine  10 var 1996 SATO  1k var 2002 Berkmin  10k var Propositional Satisfiability: Given a formula over Boolean variables, is there an assignment of 0/1’s to vars which makes the formula true  Canonical NP-hard problem (Cook 1973)  Enormous progress in tools that can solve instances with thousands of variables and millions of clauses  Extensions to richer classes of constraints (SMT solvers)

15. 2000s: Model Checking of C code Phase 1: Given a program P, build an abstract finite-state (Boolean) model A such that set of behaviors of P is a subset of those of A (conservative abstraction) Phase 2: Model check A wrt specification: this can prove P to be correct, or reveal a bug in P, or suggest inadequacy of A Shown to be effective on Windows device drivers in Microsoft Research project SLAM (follow-up: SDV) do{ KeAcquireSpinLock(); nPacketsOld = nPackets; if(request){ request = request->Next; KeReleaseSpinLock(); nPackets++; } }while(nPackets!= nPacketsOld); KeReleaseSpinLock(); Do lock operations, acquire and release, strictly alternate on every program execution?

16. Software Model Checking  Tools for verifying source code combine many techniques  Program analysis techniques such as slicing, range analysis  Abstraction  Model checking  Refinement from counter-examples (CEGAR)  New challenges for model checking (beyond finite-state reachability analysis)  Recursion gives pushdown control  Pointers, dynamic creation of objects, inheritence….  Active research area  Abstraction-based tools: SLAM, BLAST,…  Direct state encoding: F-SOFT, CBMC, CheckFence…

17. SMT Success Story SMT-LIB Standardized Interchange Format (smt-lib.org) Problem classification + Benchmark repositories LIA, LIA_UF, LRA, QF_LIA, … + Annual Competition (smt-competition.org) Z3 YicesCVC4MathSAT5 CBMCSAGEVCCSpec#

18. Since 1990s: Cyber-Physical Systems Discrete software interacting with a continuously evolving physical system  Need to model physical world using differential equations/timing delays  Models: Timed automata, Hybrid automata  Symbolic reachability analysis over sets of real-valued variables  Finite-state abstractions  Beyond correctness: Stability, Timely response  Fruitful collaboration between control theory and formal methods

19. Formal Methods for Cyber-Physical Systems  Tools for verifying timed/hybrid systems models Uppaal, Taliro, Keymaera, dReal, Space-Ex …  Applications  Medical devices (infusion pump, pacemaker)  Autonomous driving (collision avoidance protocols)  Industrial technology transfer  Model-based design tools (e.g. Hybrid automata as Simulink domain)  Simulink Design Verifier (model-based testing, static analysis)  Industry research groups (Toyota, Ford…)

20. How to choose a research problem ?  Common Themes in CAV Success Stories  Phase 1: Initial demonstration of a compelling match between the capability of a research prototype and real-world need  Phase 2: Sustained research on improving scalability  But the path to the promised land is unclear …

21. Incremental vs. Transformative  Symbolic model checking using binary decision diagrams (McMillan et al, 1990)  Importance was immediately obvious and celebrated  Critical for industrial adoption of hardware model checking  Chaff: Engineering an efficient SAT solver (Malik etal,2001)  Low-level optimization exploiting cache perforamce  Played critical role in boosting performance of SAT solvers  Don’t keep searching for “big” ideas by dismissing research problems as incremental

22. Source: Existing Literature vs. Real-world Problems?  Hybrid automata (Alur, Henzinger et al, 1991)  Started as a theoretical extension of timed automata  Now with significant research and adoption in CPS community  SAGE (Godefroid et al, CACM 2012)  A response to pressing industrial need for effective testing for discovering security vulnerabilities  Integration of many research ideas into a highly successful tool  Keep looking everywhere!

23. Theoretical Results vs. Prototype Tools  Nested depth-first search (CVWY, CAV 1990)  Beautiful algorithm for on-the-fly detection of fair cycles  Key ingredient of all explicit-state LTL model checkers  SLAM (Ball and Rajamani, 2001)  Integration of predicate abstraction, symbolic model checking, and counter-example guided abstraction refinement  Prototype tool and evaluation essential to demonstrate utility  CAV offers many options for research: theoretical, practical, and theory in practice!

24. Advice 1: Be sure of the motivation  If you were to succeed in finding a good solution to the problem you are studying, what would be the consequence?  Tool: who is a potential user?  Algorithm: which tool can use and why should it use?  Method: which design/analysis task can be done better?  Be convinced of the answer yourself first, and worry about reviewers later

25. Advice 2: Know the related work  Is your idea new?  How does it fit into what people know and have tried earlier?  Vast literature, but there is no way around this question  Be an expert on work related to your thesis  Caution: this is not an excuse for inaction!

26. Advice 3: Don’t live in a silo!  Computer science is rapidly expanding in exciting directions  Need to know at a high level what’s happening around you  Organization into conferences/sub-disciplines is artificial  Other fields can be a source of new ideas, applications, solution techniques  How can statistical machine learning help CAV?  Can CAV techniques be applied to problems in system biology?  Goal: Become an expert in Formal Methods AND X