1. 272: Software Engineering Fall 2008 Instructor: Tevfik Bultan Lecture 15: Interface Extraction

2. Software Interfaces Here are some basic questions about software interfaces –How to specify software interfaces? –How to check conformance to software interfaces? –How to extract software interfaces from existing software? –How to compose software interfaces? Today we will talk about some research that addresses these questions

3. Software Interfaces In this lecture we will talk about interface extraction for software components Interface of a software component should answer the following question –What is the correct way to interact with this component? –Equivalently, what are the constraints imposed on other components that wish to interact with this component? Interface descriptions in common programming languages are not very informative –Typically, an interface of a component would be a set of procedures with their names and with the argument and return types

4. Software Interfaces Let’s think about an object oriented programming language –You interact with an object by sending it a message (which means calling a method of that object) –What do you need to know to call a method? The name of the method and the types of its arguments What are the constraints on interacting with an object –You need a reference to the object –You have to have access (public, protected, private) to the method that you are calling One may want to express other kinds of constraints on software interfaces –It is common to have constraints on the order a component’s methods can be called For example: a call to the consume method is allowed only after a call to the produce method –How can we specify software interfaces that can express such constraints?

5. Software Interfaces Note that object oriented programming languages enforce one simple constraint about the order of method executions: –The constructor of the object must be executed before any other method can be executed –This rule is very static: it is true for every object of every class in every execution We want to express restrictions on the order of the method executions –We want a flexible and general way of specifying such constraints

6. Software Interfaces First, I will talk about the following paper –``Automatic Extraction of Object-Oriented Component Interfaces,'' J. Whaley, M. C. Martin and M. S. Lam Proceedings of the International Symposium on Software Testing and Analysis, July 2002. –The following slides are based on the above paper and the slides from Whaley’s webpage

7. Automatic Interface Extraction The basic idea is to extract the interface from the software automatically –Interface is not written as a separate specification There is no possibility of inconsistency between the interface specification and the code since the interface specification is extracted from the code The extracted interface can be used for dynamic or static analysis of the software It can be helpful as a reverse engineering tool

8. What are Software Interfaces In the scope of the work by Whaley et al. interfaces are constraints on the orderings of method calls For example: –method m1 can be called only after a call to method m2 –both methods m1 and m2 have to be called before method m3 is called

9. How to Specify the Orderings Use a Finite State Machine (FSM) to express ordering constraints States correspond to methods Transitions imply the ordering constraints M2M1 Method M2 can be called after method M1 is called

10. open S TART read write close E ND Example: File There are two special states Start and End indicating the start and end of execution

11. open S TART read write close E ND m1(); m2() is legal, new state is m2 m1m2 A Simple OO Component Model Each object follows an FSM model. One state per method, plus START & END states. Method call causes a transition to a new state.

12. The Interface Model Note that this is a very simple model It only remembers what the last called method is There is no differentiation between different invocations of the same method This simple model reduces the number of possible states Obviously all the orderings cannot be expressed this way

13. Adding more precision With above model we cannot express constraints such as –Method m1 has to be called twice before method m2 can be called We can add more precision by remembering the last k method calls –If we have n methods this will create n k states in the FSM Whaley et al. suggest other ways of improving the precision without getting this exponential blow up

14. The Interface Model If the only state information is the name of the last method called then what are the situations that this information is not precise enough? Problem 1: Assume that there are two independent sequences of methods that can be interleaved arbitrarily –once we call a method from one of the sequences we will lose the information about the other sequence Problem 2: Assume that there is a method which can be followed by all the other methods –then once we get to that method any following behavior is possible independent of the previous calls

15. Problem 1 Consider the following scenario –An object has two fields, a and b –There are four methods set_a(), get_a(), set_b(), get_b() –Each field must be set before being read We would like to have an interface specification that specifies the above constraints –Can we build an FSM that corresponds to these constraints?

16. Problem 1 These kind of constraints create a problem because once we call the set_a method it is possible to go to any other method –FSM does not remember the history of the method calls –FSM only keeps track of the last method call Solution: Use one FSM for each field and take their product set_a get_a set_b get_b S TART set_aget_aset_bget_bE ND set_a get_b FSM below allows the following sequence: start; set_a(); get_b();

17. set_a get_a set_b get_b S TART set_aget_aset_bget_bE ND S TART set_bget_bE ND S TART set_a get_a E ND ImpreciseAdds more precision Splitting by fields Separate the constraints about different fields into different, independent constraints: –Use multiple FSMs executing concurrently (or use a product FSM)

18. S TART set_a,start E ND get_a,start set_a,set_bget_a,get_bstart,set_b start,get_b get_a,set_bset_a,get_b There is a transition from each state to the END state The product FSM does not allow the following sequence: start; set_a(); get_b(); Product FSM

19. Product FSM has more number of states than the FSM which just remembers the last call Assume that there are n 1 methods for field 1 and n 2 methods for field 2 –simple FSM n 1 + n 2 states –product FSM n 1  n 2 states Note that the number states in the product FSM will be exponential in the number of fields

20. Problem 2 It is common to have methods which are used to query the state of an object These methods do not change the state of the object After such state-preserving methods all other methods can be called –Calling a state preserving method does not change the state of the object –If a method can be called before a call to a state preserving method, then it can be called after the call to the state preserving method –Since only information we keep in the FSM is the last method call, if there exists an object state where a method can be called, then that method can also be called after a call to a state-preserving method

21. Problem 2 getFileDescriptor is state- preserving Once getFileDescriptor is called then any behavior becomes possible The FSM for Socket allows the sequence: start; getFileDescriptor(); connect(); Solution –distinguish between state-modifying and state- preserving methods –Calls to state-preserving methods do not change the state of the FSM start S TART create E ND connect close getFileDescriptor S TART getFileDescriptorconnect FSM for Socket

22. State-preserving methods start S TART create E ND connect close S TART getFileDescriptor m1 is state-modifying m2 is state-preserving m1(); m2() is legal, new state is m1 m1m2 Calls to state-preserving methods do not change the state of the FSM

23. Summary of Model Product of FSMs –Per-thread, per-instance One submodel per field –Use static analysis to find the methods that either read the value of the field or modify the value of the field. Identifies the methods that belong to a submodel –The methods that read and write to a field will be in the FSM for that field Separates state-modifying and state-preserving methods. One submodel per Java interface –Implementation not required

24. Extraction Techniques StaticDynamic For all possible program executionsFor one particular program execution ConservativeExact (for that execution) Analyze implementationAnalyze component usage Detect illegal transitionsDetect legal transitions Superset of ideal model (upper bound) Subset of ideal model (lower bound)

25. Static Model Extraction Static model extraction relies on defensive programming style Programmers generally put checks in the code that will throw exceptions in case the methods are not used in the correct order Such checks implicitly encode the software interface The static extraction algorithm infers the method orderings from these checks that come from defensive programming

26. Static Model Extractor Defensive programming –Implementation throws exceptions (user or system defined) on illegal input. public void connect() { connection = new Socket(); } public void read() { if (connection == null) throw new IOException(); } S TART connectread connection

27. Extracting Interface Statically The static algorithm has two main steps 1.For each method m identify those fields and predicates that guard whether exceptions can be thrown 2.Find the methods m’ that set those fields to values that can cause the exception This means that immediate transitions from m’ to m are illegal Complement of the illegal transitions forms the model of transitions accepted by the static analysis

28. Detecting Illegal Transitions Only support simple predicates –Comparisons with constants, null pointer checks The goal is to find method pairs such that: –Source method executes: field = const ; –Target method executes: if (field == const) throw exception;

29. Algorithm How to find the target method: Control dependence –Find the following predicates: A predicate such that throwing an exception is control dependent on that predicate This can be done by computing the control dependence information for each method –For each exception check if the predicate guarding its execution (i.e., the predicate that it is control dependent on) is a single comparison between a field of the current object and a constant value the field is not written in the current method before it is tested –Such fields are marked as state variables

30. Algorithm The second step looks for methods which assign constant values to state variables How to find the source method: Constant propagation –Does a method set a field to a constant value always at the exit? If we find such a method and see that –that constant value satisfies the predicate that guards an exception in an other method then this means that we found an illegal transition

31. Sidenote: Control Dependence A statement S in the program is control dependent on a predicate P (an expression that evaluates to true or false) if the evaluation of that predicate at runtime may decide if S will be executed or not For example, in the following program segment if (x > y) max:=x; else max:=y; the statements max:=x; and max:=y; are control dependent on the predicate (x > y) A common compiler analysis technique is to construct a control dependence graph –In a control dependence graph there is an edge from a node n 1 to another node n 2 if n 2 is control dependent on n 1

32. Sidenote: Constant Propagation Constant propagation is a well-known static analysis technique Constant propagation statically determines the expressions in the program which always evaluate to a constant value Example y:=0; if (x > y) then x:=5; else x:=5+y; z := x*x; The assigned value to z is the constant 25 and we can determine this statically (at compile time) Constant propagation is used in compilers to optimize the generated code. –Constant folding: If an expression is known to have a constant value, it can be replaced with the constant value at compile time preventing the computation of the expression at runtime.

33. Static Extraction Static analysis of the java.util.AbstractList.ListItr with lastRet field as the state variable The analysis identifies the following transitions illegal: –start  set –start  remove –remove  set, add  set –remove  remove –add  remove The interface FSM contains all the remaining transitions

34. next, previous S TART set remove add Automatic documentation Interface generated for java.util.AbstractList.ListItr

35. Dynamic Interface Extractor Goal: find the legal transitions that occur during an execution of the program Java bytecode instrumentation –insert code to the method entry and exits to track the last-call information For each thread, each instance of a class: –Track last state-modifying method for each submodel.

36. Dynamic Interface Checker Dynamic Interface Checker uses the same mechanism as the dynamic interface extractor –When there is a transition which is not in the model instead of adding it to the model it throws an exception

37. Experiences Whaley et al. applied these techniques to several applications ProgramDescriptionLines of code Java.net 1.3.1Networking library12,000 Java libraries 1.3.1General purpose library300,000 J2EE 1.2.1Business platform900,000 joeqJava virtual machine65,000

38. start S TART begin E ND suspend resume rollbackcommit J2EE TransactionManager (dynamic) An example FSM model that is dynamically generated and provides a specification of the interface Automatic documentation

39. Test coverage Dynamically extracted interfaces can be used as a test coverage criteria The transitions that are not present in the interface imply that those method call sequences were not generated by the test cases For example, the fact that there are no self-edges in the FSM on the right implies that only a max recursion depth of 1For example, the fact that there are no self-edges in the FSM on the right implies that only a max recursion depth of 1 was tested increaseRecursionDepth decreaseRecursionDepth increaseRecursionDepth simpleWriteObject S TART E ND J2EE IIOPOutputStream (dynamic)

40. Upper/lower bound of model start S TART create E ND connect available getInputStream getOutputStream close SocketImpl model (dynamic) (+static) getFileDescriptor Statically generated transitions provide an upper approximation of the possible method call sequences Dynamically generated transitions provide a lower approximation of the possible method call sequences

41. S TART load prepare compile Expected API for jq_Method: S TART load prepare compile Actual API for jq_Method: setOffset Finding API bugs Automated interface extraction can be used to detect bugs The interface extracted from the joeq virtual machine showed unexpected transitions

42. Summary: Automatic Interface Extraction Product of FSM –Model is simple, but useful Static and dynamic analysis techniques –Generate upper and lower bounds for the interfaces Useful for: –Documentation generation –Test coverage –Finding API bugs

43. Automated Interface Extraction, Continued There is a more recent work on interface extraction for Java –“Synthesis of Interface Specifications for Java Classes,” R. Alur, P. Cerny, P. Madhusan, W. Nam, in Proceedings of Principles of Programming Languages, (POPL 2005). –They built a tool called JIST (Java Interface Synthesis Tool). I will discuss this work in the rest of the lecture.

44. Java Interface Synthesis Tool (JIST) Here is the problem that JIST is trying to solve: –Given a class and a property such as “the exception E should not be raised” generate a behavioral interface specification for the class that corresponds to the most general way of invoking the methods in the class without violating the safety property.

45. Safe Interface Let E denote the unsafe states of the program (for example an exception is raised) –E specifies the safety requirement, i.e., a state satisfying E should not be reached An interface specification for a class is a safe interface with respect to a requirement E –if it is guaranteed that the program never reaches the unsafe state E as long as the class is used according to the interface specification

46. Most Permissive Safe Interface The most permissive safe interface is a safe interface that puts the least amount of restrictions on the users of the class –Interface I is more permissive than interface I’, if any call sequence allowed by I’ is also allowed by I –If I is the most permissive safe interface, then for any safe interface I’, I is more permissive than I’ JIST is guaranteed to find a safe interface but it is not guaranteed to find the most permissive safe interface

47. Interface Synthesis Steps STEP 1: Abstract the class to a Boolean program using predicate abstraction –The predicates are provided by the user STEP 2: Find a winning strategy in a two-player partial information game –Player-0 is the user of the class. Player-0 chooses to invoke one of the methods of the class. –Player-1, the abstract class, chooses a corresponding possible execution through the abstract state-transition graph which results in an abstract return value. –A strategy for Player-0 is winning if the game always stays away from the abstract states satisfying the requirement E (E is provided by the user) The most permissive winning strategy can be represented as a DFA –They use the L* algorithm to compute this DFA –L* is an algorithm for learning a regular language using membership and equivalence queries

48. JIST Architecture Java Java Byte Code Jimple Boolean Jimple NuSMV Language Interface Automaton Symbolic Class Boolean Symbolic Class Interface Soot Predicate Abstractor Predicates Game Language Converter Interface Synthesizer STEP1 : Abstraction STEP 2: Partial Information Game Solving Java compiler

49. STEP 1: Predicate Abstraction JIST uses a predicate abstraction technique similar to the one used in SLAM model checker Predicate abstraction is an automated abstraction technique which can be used to reduce the state space of a program The basic idea in predicate abstraction is to remove some variables from the program by just keeping information about a set of predicates about them For example a predicate such as x = y maybe the only information necessary about variables x and y to determine the behavior of the program –In that case we can just store a boolean variable which corresponds to the predicate x = y and remove variables x and y from the program –Predicate abstraction is a technique for doing such abstractions automatically

50. Predicate Abstraction Given a program and a set of predicates, predicate abstraction abstracts the program so that only the information about the given predicates are preserved The abstracted program adds nondeterminism since in some cases it may not be possible to figure out what the next value of a predicate will be based on the predicates in the given set One needs an automated theorem prover to compute the abstraction

51. Predicate Abstraction, Simple Example Assume that we have two integer variables x,y Abstract the program “y := y+1” using a single predicate “x=y” We will represent the predicate “x=y” as the boolean variable B in the abstract program –“B=true” will mean “x=y” and “B=false” will mean “x  y” Concrete Statement y := y + 1 y := y + 1 {x = y} y := y + 1 {x  y} {x  y + 1} {x = y + 1} Step 1: Calculate the preconditions Step 2: Use Decision Procedures to determine if the predicates used for abstraction imply any of the preconditions x = y  x = y + 1 ? No x  y  x = y + 1 ? No x = y  x  y + 1 ? Yes x  y  x  y + 1 ? No Step 3: Generate Abstract Code IF B THEN B := false ELSE B := true | false (Example taken from Matt Dwyer’s slides) precondition for B being false after executing the statement y:=y+1

52. STEP 1: Predicate Abstraction JIST’s predicate abstraction implementation does not handle the following: –Floating point types, arrays, recursive method calls (then inline the method calls by inlining), exceptions (other than the one used for the requirement E) They do not use an automated theorem prover since they only handle simple expressions The result of the abstraction step is an Abstract class which only contains boolean variables and is nondeterministic –It provides an over-approximation of the behaviors that can be generated by the concrete class –I.e., if a call sequence does not reach E in the abstract class then it is guaranteed that it will not reach E in the concrete class

53. STEP 2: Game Solving Player-0 user of the abstract class Player-1 the abstract class Game –Player-0 chooses a method and calls it –Player-1 picks a possible execution for the method that is called (remember that there is non-determinism) Player-0 wins if E is not reached Question: Find the most permissive winning strategy for Player-0 –The most permissive winning strategy corresponds to the interface for the class

54. Game Solving via Learning Results from game theory show that the winning strategy can be characterized as a DFA JIST uses a learning algorithm called L* to find a winning strategy L* is an algorithm that can compute a DFA by repeatedly asking membership and equivalence queries –Membership query: Is this string accepted by the target DFA? –Equivalence query: Given a DFA (a guess) is it equal to the target DFA? If the equivalence query returns false, it should also give a counter-example string that is accepted by one of the DFAs but not the other If these two types of queries can be answered, then L* algorithm can compute the target DFA

55. Implementing Equivalence Queries Let G be the DFA guessed by the learning algorithm and let T be the target DFA –Equivalence query: Are the language accepted by G and the language accepted by T equal? The equivalence query can be divided to two separate queries L(G) = L(T) if and only if L(G)  L(T) and L(T)  L(G) They can handle subset queries precisely –Membership queries can also be translated to subset queries (generate a DFA that accepts only the input string) They cannot handle superset queries precisely, and because of that they are not guaranteed to compute the most permissive interface –However, they always compute a safe interface

56. Implementing Subset Queries Checking a Subset query means the following –The learning algorithm suggests an interface I –They compute the composition of this interface I with the abstract class A (A || I) –Then they check if A || I satisfies the property AG(  E) using the model checker NuSMV E is the requirement (and interface is a safe interface if E never becomes true) If A || I satisfies AG(  E), then I is a safe interface and hence it accepts a subset of the language accepted by the most permissive interface –The answer to the subset query is TRUE If A || I violates AG(  E), then they generate a counter-example execution which shows that I can lead to violation of property E, i.e., it is not a safe interface –The answer to the subset query is FALSE and the counter- example is returned to the learning algorithm

57. Implementing Superset Queries Checking a Superset query means the following –The learning algorithm suggests an interface I –I is the superset of the most permissive safe interface if all the call sequences that are not allowed by I lead to some execution of class A which reaches E –There is no efficient way of checking this They check the following: –If in any call sequence, the first method call that is not allowed by I always reaches E, then the answer to the superset query is TRUE –Otherwise, we look at the counter-example call sequence generated by the model checker and check if that call sequence is safe If it is, then the answer to the superset query is FALSE and that call sequence is a counter-example to the superset query If it is not safe, then we do not know the answer to the superset query, but we can still report the interface as a safe interface since it passed the subset query

58. Experiments The automatically synthesized interfaces for some Java classes: –Signature, ServerTableEntry, ListItr, PipedOutputStream The computation time is 5 to 100 seconds In 4 our of 6 cases they found the most permissive interface s0s0 s1s1 s2s2 initSign initVerify update sign initSign initVerify initSign update verify initVerify Signature class interface