CS 290 C Formal Models for Web Software

CS 290 C: Formal Models for Web Software Lecture 3: Verification of Navigation Models with the Spin Model Checker Instructor: Tevfik Bultan

Verifying navigation models • Web application typically have some navigation constraints that they wish to enforce. • For example – Transition to a particular page must be via a specific other page. For example, a page displaying the contents of the shopping cart must be displayed before proceeding to the checkout – From any position in the application the users should be able to go back to the home page • We would like to check these types of constraints on the navigation model

Verifying navigation models • Verification of navigation models can be done in two ways 1. We can first construct the navigation model, verify it, and then while implementing the application we can enforce the navigation model (forward engineering) • If we can enforce the navigation model precisely, then verification results hold for the final application 2. We can try to extract the navigation model from an existing application by analyzing the application, and then verify the properties of the extracted model (reverse engineering) • If the automatically extracted model is precise, then the verification results hold for the application

What can we do after generating navigation models? • As I said last week, after we obtain a formal model of the navigation behavior we can verify it. • This brings up two questions: 1. How are we going to characterize properties of navigation models? • For this we will briefly discuss temporal logics 2. How are we going to verify these properties on the navigation model • For this we will discuss the Spin model checker

Transition Systems • Transition systems are a very basic model used in automated verification • The idea is to model a system with just states and transitions – It is basically a flat state machine • A transition system T = (S, I, R) consists of – a set of states S – a set of initial states I S – and a transition relation R S S • Semantics of many formal languages (including statecharts) can be defined using transition systems

Execution Paths • An execution path is an infinite sequence of states x = s 0, s 1, s 2, . . . such that s 0 I and for all i 0, (si, si+1) R Notation: For any path x xi denotes the i’th state on the path (i. e. , si) xi denotes the i’th suffix of the path (i. e. , si+1, si+2, . . . )

Temporal Logics • Temporal logics are a type of modal logics – Modal logics were developed to express modalities such as “necessity” or “possibility” – Temporal logics focus on the modality of temporal progression • Temporal logics can be used to express, for example, that: – an assertion is an invariant (i. e. , it is true all the time) – an assertion eventually becomes true (i. e. , it will become true sometime in the future)

Temporal Logics • We will assume that there is a set of basic (atomic) properties called AP – Atomic properties are the basic, non-temporal properties that can be checked by looking at the current state of the system • We will use the usual boolean connectives: • We will also use four temporal operators: Invariant p : Gp (aka p) Eventually p : Fp (aka p) Next p : Xp (aka p) p Until q : p. Uq , , (Globally) (Future) (ne. Xt)

Atomic Properties • In order to define the semantics we will need a function L which evaluates the truth of atomic properties on states: L : S AP {True, False} So given a state of the transition system and an atomic property, the function L determines if the property holds on that state or not.

Linear Time Temporal Logic (LTL) Semantics Given an execution path x and LTL properties p and q x |= p q iff iff L(x 0, p) = True, where p AP not x |= p and x |= q x |= p or x |= q x |= X p x |= G p x |= F p x |= p U q iff iff x 1 |= p for all i 0, xi |= p there exists an i 0 such that xi |= q and for all 0 j < i, xj |= p

LTL Properties . . . Xp p . . . Gp p p . . . Fp p. Uq p p . . . p p q

LTL Equivalences • We do not really need all four temporal operators – X and U are enough (i. e. , X, U, AP and boolean connectives form a basis for LTL) F p = true U p G p = (F p) = (true U p)

LTL Model Checking • Given a transition system T and an LTL property p T |= p iff for all execution paths x in T, x |= p Model checking problem: Given a transition system T and an LTL property p, determine if T is a model for p (i. e. , if T |=p)

LTL Properties Transition System T = (S, I, R) p p s 1 s 2 s 3 s 4 T |= X p T |= F p T |= G p T |= F p S = {s 1, s 2, s 3, s 4} I = {s 3} R ={(s 1, s 2), (s 2, s 3), (s 3, s 1), (s 3, s 4), (s 4, s 3)} s 1 |= p s 2 |= p s 3 |= p s 4 |= p

Example navigation properties in temporal logic • F(main-page): main-page is eventually reached • G(F(exit-page)): always eventually exit-page is reached • G(purchase-page => X(X(payment-page))): payment-page is reached two clicks after the purchase-page

Model Checkers • There are model checking tools that can check temporal logic properties on transition systems • Spin is a well-known LTL model checker – We will discuss it next • Spin can be used to check properties of finite state systems (such as statecharts specifications)

Verifying navigation models One specific approach to verification of navigation models • Write navigation properties in LTL • Write the navigation model in the Promela language (the input language for the Spin model checker) • Use the Spin model checker to check the properties on the Promela specification • Spin model checker outputs error traces for the properties that are violated

SPIN • • Explicit state model checker Finite state Temporal logic for specifying properties: LTL Input language: PROMELA – Asynchronous processes – Shared variables – Message passing through (bounded) communication channels – Variables: boolean, char, integer (bounded), arrays (fixed size) – Structured data types A good overview paper for the Spin model checker: ``The Model Checker SPIN, ’’ Gerard J. Holzmann, IEEE Trans. Soft. Eng. , 23(5) 279 -295, 1997.

LTL Model Checking Summary • Each LTL property can be converted to a type of automaton • Generate the property automaton from the negated LTL property • Generate the product of the property automaton and the transition system • Show that there is no accepting (infinite) path in the product automaton (check language emptiness) – i. e. , show that the intersection of the paths generated by the transition system and the paths accepted by the (negated) property automaton is empty • If there is an accepting path, it corresponds to a counterexample behavior that demonstrates the bug

LTL properties can be converted to automata true Gp p p true Fp G (F p) p p These are specific type of automata that accepts infinite strings (they are called Buchi autoamata)

LTL Model Checking Summary • We can think of the input transition system as also an automaton if we make all the states of the transition system accepting states • Then, if we generate a property automaton from the negated LTL property and take the product of the property automaton and the transition system – If the transition system satisfies the property, this product automaton should not accept any strings • So, we can check if the language accepted by the property automaton is empty or not – If it is empty, then the transition system satisfies the property – If it is not empty, any accepting sequence corresponds to a behavior that violates the original property

SPIN Verification in SPIN • Uses the LTL model checking approach • Constructs the product automaton on-the-fly – It is possible to find an accepting path (i. e. a counterexample) without constructing the whole state space • Uses a nested depth-first search algorithm to look for an accepting path (which is must end in a cycle since we are looking for infinite paths) • Uses various heuristics to improve the efficiency of the nested depth first search: – partial order reduction – state compression

Promela (PROcess MOdeling LAnguage) • Basic data types and ranges – bit (or bool) 0. . 1 – byte 0. . 255 – short -215 -1. . 215 -1 – int -231 -1. . 231 -1 bool flag; int state; /* declares a boolean array */ /* declares an integer variable called state */ • Array variables byte myarray[N] /* decleras an array of size N */

Processes • You can define processes using proctype A() { byte state; /* local variable */ state = 3 } ; used as a separator not terminator, so no ; after the last statement

Processes • you can also use -> instead of ; byte state = 2; proctype A() { (state==1) -> state=3 } proctype B() { state = state – 1 }

Process Instantiation • You use an init block to identify initial states of the system init { skip } • You can use the init block to instantiate processes init { run A(); run B() } • run is used to start executing a process and can also pass arguments (basic data types) to a process

Atomic sequences • A sequence of statements can be executed atomically using the atomic construct atomic { (state==1) -> state=state+1 }

Message passing • Channels can be used to model transfer of data from one process to another – Channels are basically FIFO message queues – Channels can store tuples of values at each location in the message queue chan qname = [16] of { int } – A channel that stores integer values chan qname = [16] of {int, bool} – A channel that stores tuples that consist of two integers and one boolean value

Message passing • Send operations: qname!expr sends the value of the expression expr to the channel named qname • Receive operations: qname? msg retrieves the message from the head of the channel qname and stores it in the variable msg

Rendez-Vous Communication • If the channel size is set to 0 then, the communication corresponds to synchronous communication – Sender and receiver must execute matching send and receive actions at the same time – If the sender (the receiver) reaches the send (the receive) operation before the receiver (the sender) reaches the receive (the send) operation, it has to wait chan port = [0] of {byte}

Control flow: case selection if : : (a!=b) -> option 1 : : (a==b) -> option 2 fi If more than one guard is executable, then one option is chosen nondeterministically

Control flow: repetition (loops) do : : count = count+1 : : count= count-1 : : (count==0) -> break od Only one option is selected for execution at a time. After that option is executed, the process is repeated.

Enumerated variables • You can define enumerated variables using mtype = {ack, nak, err, next, accept} chan q = [4] of {mtype, bit, short}

Example Mutual Exclusion Protocol Two concurrently executing processes are trying to enter a critical section without violating mutual exclusion Process 1: while (true) { out: a : = true; turn : = true; wait: await (b = false or turn = false); cs: a : = false; } || Process 2: while (true) { out: b : = true; turn : = false; wait: await (a = false or turn); cs: b : = false; }

Example Mutual Exclusion Protocol in Promela #define cs 1 process 1@cs #define cs 2 process 2@cs #define wait 1 process 1@wait #define wait 2 process 2@wait #define true 1 #define false 0 bool a; bool b; bool turn; proctype process 1() { out: a = true; turn = true; wait: (b == false || turn == false); cs: a = false; goto out; } proctype process 2() { out: b = true; turn = false; wait: (a == false || turn == true); cs: b = false; goto out; } init { run process 1(); run process 2() }

Property automaton generation % spin -f "! [] (! (cs 1 && cs 2))“ never { T 0_init: /* ! [] (! (cs 1 && cs 2)) */ if : : ((cs 1) && (cs 2)) -> goto accept_all : : (1) -> goto T 0_init fi; accept_all: skip } % spin -f "!([](wait 1 -> <>(cs 1)))“ never { T 0_init: • Input formula “[]” means G “<>” means F • “spin –f” option generates a Buchi automaton for the input LTL formula /* !([](wait 1 -> <>(cs 1))) */ if : : (! ((cs 1)) && (wait 1)) -> goto accept_S 4 : : (1) -> goto T 0_init fi; accept_S 4: if : : (! ((cs 1))) -> goto accept_S 4 fi; } Concatanate the generated never claims to the end of the specification file

SPIN • “spin –a mutex. pml” generates a C program “pan. c” from the specification file – You need to use the “-a” flag to verify temporal logic formulas – The generated pan. c is a C program that implements the on-the-fly nested-depth first search algorithm – You compile “pan. c” and run it to do the model checking (you need to use the “-a” flag when you run the executable) • Spin generates a counter-example trace if it finds out that a property is violated – You can view the counter-example trace using “spin –t –p mutex. pml”

%mutex -a warning: for p. o. reduction to be valid the never claim must be stutter-invariant (never claims generated from LTL formulae are stutter-invariant) (Spin Version 4. 2. 6 -- 27 October 2005) + Partial Order Reduction Full statespace search for: never claim assertion violations acceptance cycles invalid end states + + (if within scope of claim) + (fairness disabled) - (disabled by never claim) State-vector 28 byte, depth reached 33, errors: 0 22 states, stored 15 states, matched 37 transitions (= stored+matched) 0 atomic steps hash conflicts: 0 (resolved) 2. 622 memory usage (Mbyte) unreached in proctype process 1 line 18, state 6, "-end-" (1 of 6 states) unreached in proctype process 2 line 27, state 6, "-end-" (1 of 6 states) unreached in proctype : init: (0 of 3 states)

Modeling state machines with spin • To model a basic (flat) state machine with Spin we can do the following – Declare the states of the state machine as an mtype – Declare a variable called state that will store the current state of the state machine – Use the init block to initialize the state variable to an initial state of the state machine – Model the transitions of the state machine as a switch cases in a loop do : : (state==s 1) -> state=s 2 : : . . . /* one choice for each transition */. . . od

An example A simple state machine and the corresponding Promela model (saved it in a file fsm. pml) p p s 1 s 2 s 3 s 4 #define state 1 state==s 1 #define state 2 state==s 2 #define state 3 state==s 3 #define state 4 state==s 4 #define p (state==s 1 || state==s 4) mtype = {s 1, s 2, s 3, s 4}; mtype state; proctype fsm() { do : : state==s 1 -> state=s 2; : : state==s 2 -> state=s 3; : : state==s 3 -> state=s 1; : : state==s 3 -> state=s 4; : : state==s 4 -> state=s 3 od } init { state = s 3; run fsm() }

An example • We can check properties such as the following LTL formulas (<> is F and [] is G): <> p (this property holds on our example) [] p (this property does not hold on our example) [] !p (this property does not hold our example either) []<> p (this property holds on our examle)

An example • To check propety <>p we create a never claim for its negation: % spin -f “! <> p” never { /* ! <> p */ accept_init: T 0_init: if : : (! ((p))) -> goto T 0_init fi; }

An example • Concatenate the never claim to fsm. pml and then do the following: % spin -a fsm. pml % gcc pan. c -o fsm %. /fsm –a • We get the output in the next page

Verification output $. /fsm –a warning: for p. o. reduction to be valid the never claim must be stutter-invariant (never claims generated from LTL formulae are stutter-invariant) (Spin Version 4. 2. 6 -- 27 October 2005) + Partial Order Reduction Full statespace search for: never claim assertion violations acceptance cycles invalid end states + + (if within scope of claim) + (fairness disabled) - (disabled by never claim) State-vector 24 byte, depth reached 8, errors: 0 7 states, stored (14 visited) 4 states, matched 18 transitions (= visited+matched) 0 atomic steps hash conflicts: 0 (resolved) 4. 879 memory usage (Mbyte) unreached in proctype fsm line 12, state 2, "state = s 2" line 13, state 4, "state = s 3" line 16, state 10, "state = s 3" line 18, state 14, "-end-" (4 of 14 states) unreached in proctype : init: (0 of 3 states) Means that the property holds

An example • Now let’s check the following property [] !p we create a never claim for its negation: % spin -f “! [] ! p” never { /* ! [] ! p */ T 0_init: if : : ((p)) -> goto accept_all : : (1) -> goto T 0_init fi; accept_all: skip }

An example %. /fsm –a warning: for p. o. reduction to be valid the never claim must be stutter-invariant (never claims generated from LTL formulae are stutter-invariant) pan: claim violated! (at depth 11) pan: wrote test. trail (Spin Version 4. 2. 6 -- 27 October 2005) Warning: Search not completed + Partial Order Reduction Full statespace search for: never claim + assertion violations + (if within scope of claim) acceptance cycles + (fairness disabled) invalid end states - (disabled by never claim) State-vector 24 byte, depth reached 11, errors: 1 6 states, stored 0 states, matched 6 transitions (= stored+matched) 0 atomic steps hash conflicts: 0 (resolved) 4. 879 memory usage (Mbyte) Means that the property is violated

An example • Then we can generate the counter-example trace using “spin –t –p fsm. pml” This is referring to the state of the whole specification This is the variable % spin -t -p fsm. pml we declared Starting : init: with pid 0 Starting : never: with pid 1 Never claim moves to line 32 [(1)] 2: proc 0 (: init: ) line 19 "fsm. pml" (state 1) [state = s 3] Starting fsm with pid 2 4: proc 0 (: init: ) line 20 "fsm. pml" (state 2) [(run fsm())] 6: proc 1 (fsm) line 13 "fsm. pml" (state 5) [((state==s 3))] 8: proc 1 (fsm) line 13 "fsm. pml" (state 6) [state = s 1] Never claim moves to line 31 [(((state==s 1)||(state==s 4)))] 10: proc 1 (fsm) line 11 "fsm. pml" (state 1) [((state==s 1))] Never claim moves to line 35 [(1)] spin: trail ends after 11 steps #processes: 2 state = s 1 11: proc 1 (fsm) line 11 "fsm. pml" (state 2) 11: proc 0 (: init: ) line 21 "fsm. pml" (state 3) <valid end state> 11: proc - (: never: ) line 36 "fsm. pml" (state 8) <valid end state> 2 processes created