Development of dynamically evolving and selfadaptive software. 1. Background


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1 Development of dynamically evolving and selfadaptive software 1. Background LASER 2013 Isola d Elba, September 2013 Carlo Ghezzi Politecnico di Milano DeepSE DEIB 1
2 Requirements Functional requirements refer to services that the system shall provide Nonfunctional requirements constrain how such services shall be provided NonFunctional Requirement Quality of Service Compliance Architectural Constraint Development Constraint Accuracy Safety Security Reliability Performance Interface Installation Distribution Cost Maintainability Cost Deadline Variability Confidentiality Integrity Availability Time Space User interaction Device interaction Software interoperability Subclass link Usability Convenience van Lamsweerde, Requirements Engineering, J. Wiley & Sons
3 Models During software development, software engineers often build abstractions of the system in the form of models [noun] A system or thing used as an example to follow or imitate a simplified description, esp. a mathematical one, of a system or process, to assist calculations or predictions Oxford American Dictionaries 3
4 Why do we use models? To communicate  They embody a shared lexicon E.g., state, transition To simplify descriptions and help focus, ignoring details that distract from the essence of the problem To reason about the modeled system  Mathematics makes reasoning formal  Through models we can predict properties of the real system before it exists 4
5 What makes a good model? A model is good if it carries the right amount of information you need  It is at the right level of abstraction A model abstracts from details  Make sure that they are details, not the essence  Be aware of the approximations A model serves a purpose  Different models for different purposes (views) Expert judgment always needed!!! 5
6 From model(s) to implementation Model driven development tries to support a development process that goes through correctnesspreserving transformations Ideally, once correct models are developed, implementation is correct by construction Reality still far from the ideal world... However, focus on models and verification important to achieve better quality products 6
7 Models Perhaps the most used (and useful) models are finitestate models given as Labelled Transition Systems of some kind OFF 0 1 ON 7
8 Labeled Transition System (Kripke Structure) x ~p k p Transitions represent execution steps y ~p h ~p State labels represent predicates true in the state z ~p 8
9 Definition An LTS is a tuple S, I, R, AP, L where  S is a set of states;  I S is the set of initial states;  R S S is the set of transitions;  AP is a set of atomic propositions;  L : S 2 AP is a labelling function. A (maximal) path from a state s0 is either a finite sequence of states that ends in a terminal state or an infinite sequence of states  π = s0, s1, s2,... such that (si, si+1) R, for all i 0. 9
10 An example Two process mutual exclusion with shared semaphore Each process has three states  Noncritical (N)  Trying (T)  Critical (C) Semaphore can be available (S0) or taken (S1) Initially both processes are in N and the semaphore is available  N1 N2 S0 N 1 T 1 T 1 S 0 C 1 S 1 C 1 N 1 S 0 N 2 T 2 T 2 S 0 C 2 S 1 C2 N 2 S 0 10
11 Consider the following model Does a system behaving like this LTS satisfy our expectations in terms of mutual exclusion: Never a state where both C1 and C2 hold can be reached N 1 N 2 S 0 T 1 N 2 S 0 N 1 T 2 S 0 C 1 N 2 S 1 T 1 T 2 S 0 N 1 C 2 S 1 C 1 T 2 S 1 T 1 C 2 S 1 11
12 How can requirements be specified? For example, we need to formalize statements like:  No matter where you are, there is always a way to get to the initial state Temporal logic to formally express properties  In classical logic, formulae are evaluated within a single fixed world For example, a proposition such as it is raining must be either true or false Propositions are then combined using operators such as,, etc.  In temporal logic, evaluation takes place within a set of worlds, corresponding to time instants it is raining may be satisfied in some worlds, but not in others  The set of worlds correspond to moments in time 12
13 Temporal logic Linear Time  Every moment has a unique successor  Infinite sequences (words)  Linear Time Temporal Logic (LTL) Branching Time  Every moment has several successors  Infinite tree  Computation Tree Logic (CTL) 13
14 LTL: syntax and semantics φ ::= true a φ1 φ2 φ oφ φ1 U φ2 oφ also written Xφ true U φ also written Fφ and also φ F φ also written Gφ and also o φ An LTL property stands for a property of a path For a state s, a formula φ is satisfied if all paths exiting s satisfy the formula Model checking Given an LTS and a formula, verify that initial states satisfy it 14
15 Mutual exclusion Always at least one process is not in the critical section N 1 N 2 S 0 T 1 N 2 S 0 N 1 T 2 S 0 C 1 N 2 S 1 T 1 T 2 S 0 N 1 C 2 S 1 C 1 T 2 S 1 T 1 C 2 S 1 (not C1 not C2) 15
16 CTL State formulae: ϕ ::= true a ϕ1 ϕ2 ϕ φ φ Path formulae: φ ::= o ϕ ϕ1 U ϕ2 X (o), F ( ) and G (o ) can be introduced as for LTL, often also written as E, A Mutual exclusion in CTL: G( C1 C2) Note: CTL and LTL have incomparable expressiveness 16
17 Quantitative modelling LTSs support qualitative modelling Often we need to model quantitative aspects, such as the cost of a certain action or the probability that a certain event occurs Here we review Markov models, an important and useful extension of LTSs 17
18 Discretetime Markov Chains A DTMC is defioned by a tuple (S, s0, P, AP, L) where S is a finite set of states s0 S is the initial state P: S S [0;1] is a stochastic matrix AP is a set of atomic propositions L: S 2 AP is a labelling function. The modelled process must satisfy the Markov property, i.e., the probability distribution of future states does not depend on past states; the process is memoryless 18
19 An#example#!A simple communication protocol operating with a channel! 1 start S D T L S D T L delivered try lost matrix representation Note: sum of probabilities for transitions leaving a given state equals 1 C. Baier, JP Katoen, Principles of model checking MIT Press,
20 Discrete Time Markov Reward Models Like a DTMC, plus  labelling states with a state reward  labelling transitions with a transition reward (we just use state rewards) Rewards can be any realvalued, additive, non negative measure; we use nonnegative real functions Usage in modelling: rewards represent energy consumption, average execution time, outsourcing costs, pay per use cost, CPU time 20
21 Reward DTMC A RDTMC is a tuple (S, s0, P, AP, L, µ), where S, s0, P, L are defined as for a DTMC, while µ is defined as follows:  µ : S R 0 is a state reward function assigning a nonnegative real number to each state... at step 0 the system enters the initial state s0. At step 1, the system gains the reward µ(s0) associated with the state and moves to a new state... 21
22 Which model(s) should we use? Different models provide different viewpoints from which a system can be analyzed Focus on nonfunctional properties leads to models where we can deal with uncertainty and specify quantitative aspects Examples DTMCs for reliability CTMCs for performance Reward DTMCs for energy/cost/performance 22
23 Quantitative requirements specification Specification can be qualitative ( the system shall do... ) or quantitative ( average response time shall be less than xxx ) LTL, CTL temporal logic are typical examples of qualitative specification languages Nonfunctional requirements ask for quantitative specification Quantitative specs then require quantitative verification 23
24 PCTL Probabilistic extension of CTL In a state, instead of existential and universal quantifiers over paths we can predicate on the probability for the set of paths (leaving the state) that satisfy property In addition, path formulas also include stepbounded until ϕ1 U k ϕ2 ::= P ( ) ::= An example of a reachability property  P>0.8 [ (system state = success)] 1 absorbing state 24
25 RPCTL RewardProbabilistic CTL for RDTMC ::= P ( ) ::= R ( ) ::= = R ( = ) R ( ) R ( ) 25
26 Example R ( = ) Expected state reward to be gained in the state entered at step k along the paths originating in the given state The expected cost gained after exactly 10 time steps is less than 5 R < ( = ) 26
27 Example R ( ) T Expected cumulated reward within k time steps ext Text The expected energy consumption within the first 50 time units of operation is less than 6 kwh R < ( ) 27
28 Example R ( ) Expected cumulated reward until a state satisfying is reached Text Text The average execution time until a user session is complete is lower than 150 s R < ( ) 28
29 A bit of theory Probability for a finite path traversed is 1 if otherwise to be A state sj is reachable from state si if a finite path exists leading to sj from si The probability of moving from si to sj in exactly 2 steps is which is the entry of The probability of moving from si to sj in exactly k steps is the entry =1 of = s 0,s 1,s Q 2,... 2 k=0 P (s k,s k+1 ) Ps x 2S p ix p xj (i, j) P 2 (i, j) P k 29
30 A bit of theory A state is recurrent if the probability that it will be eventually visited again after being reached is 1; it is otherwise transient (a nonzero probability that it will never be visited again) A recurrent state sk where pk,k = 1 is called absorbing Here we assume DTMCs to be wellformed, i.e.  every recurrent state is absorbing  all states are reachable from initial state  from every transient state it is possible to reach an absorbing state 30
31 An example C A 3 Probability of reaching an absorbing state (e.g., 2) 2 can be reached by reaching 1 in 0, 1, 2,... steps and then 2 with prob.5 ( ) x 0.5 = ( 0.2 n ) x 0.5 = (1/(10.2)) x 0.5 = Similarly, for state 3, (1/(10.2)) x 0.3 = Notice that an absorbing state is reached with prob 1 31
32 A bit of theory Consider a DTMC with r absorbing and t transient states Its matrix can be restructured as Q R P = 0 I  Q is a nonzero t t matrix  R is a t r matrix  0 is a r t matrix  I is a r r identity matrix Q k! 0 as k!1 Theorem  In a wellformed Markov chain, the probability of the process to be eventually absorbed is 1 (1) 32
33 Focus on reachability properties A reachability property has the following form P./p ( ) states that the probability of reaching a state where holds matches the constraint./ p Typically, they refer to reaching an absorbing state (denoting success/failure for reliability analysis) It is a flat formula (i.e. no subformula contains P./p ( )) These properties are the most commonly found 33
34 A bit of theory Consider again P = Q ni,k expected # of visits of transient state sk from si, i.e., the sum of the probablities of visiting it 0, 1, 2,...times Theorem: The geometric series converges to Consider R 0 I N = I + Q 1 + Q 2 + Q 3 + = B = N R absorbing state sk from si is 1X k=0 Q k (1). The probability of reaching b ik = X k=0..t 1 n ij r jk (I Q) 1 34
35 Proving reachability properties = Pr( s = End ) j n r 0, j j, End n0,j is the sum of the probabilities to reach state j in 1, 2, 3,... steps 35
36 Model checking tools SPIN (Holzmann) analyzes LTL properties for LTSs expressed in Promela (Nu)SMV (Clarke et al, Cimatti et al.) can also analyze CTL properties and uses a symbolic representation of visited states (BDDs) to address the state explosion problem PRISM (Kwiatkowska et al.) and MRMC (Katoen et al.) support Markov models and perform probabilistic model checking 36
37 Question How do modelling notations and verification fit software evolution?  A modification to an existing system viewed as a new system  No support to reasoning on the changes and their effects 37
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