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CSE 555 Protocol Engineering

CSE 555 Protocol Engineering. Dr. Mohammed H. Sqalli Computer Engineering Department King Fahd University of Petroleum & Minerals Credits: Dr. Abdul Waheed (KFUPM) Spring 2004 (Term 032). Protocol Engineering.

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CSE 555 Protocol Engineering

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  1. CSE 555Protocol Engineering Dr. Mohammed H. Sqalli Computer Engineering Department King Fahd University of Petroleum & Minerals Credits: Dr. Abdul Waheed (KFUPM) Spring 2004 (Term 032)

  2. Protocol Engineering Reference:“Communication Protocol Specification and Verification” by Richard Lai and Ajin Jirachiefpattana, Kluwer Academic Publishers, 1998.

  3. Protocol Engineering • Development of a systematic methodology to progress from an initial protocol requirement for a system to the realization of an implementation • Concerned with activities involved with design and implementation of a protocol using FDTs during its stages of development • Subset of software engineering 1-2-3

  4. A Protocol Development Methodology(Waterfall model) User Requirements Informal Specification Formal Specification Errors detected Protocol Verification Implementation Development Errors detected Conformance Testing Interoperability Testing Maintenance 1-2-4

  5. A Protocol Development Methodology (Cont.) • Specification: definition of an object or a class of objects, by way of an FDT. • Informal Specification: user requirements are studied and desired communication services are then specified in plain language. This includes: protocol model, services provided, reliability and handling of errors. • Formal Specification: specification using a formal language. This enables the specifications to be analyzed. 1-2-5

  6. A Protocol Development Methodology (Cont.) • Protocol Verification: process of examining this specification for the presence of various errors that could lead to improper system operation. • Attempt to demonstrate the correctness and consistency of a protocol design. • Correctness: protocol performs according to specification • Consistency: protocol’s behavior can be determined • Automated protocol verification: the use of computer tools to verify a communication protocol based on its formal specification (Billington et al. 1985) • If errors are detected, the specification is iterated. 1-2-6

  7. A Protocol Development Methodology (Cont.) • Implementation Development: realization of protocol specifications on a physical system. • Development of a communication software • Can be derived manually or automatically from formal specifications using implementation tools. 1-2-7

  8. A Protocol Development Methodology (Cont.) • Protocol Testing • Conformance testing: validation of protocol implementation by testing. • Whether protocol implementation conforms to protocol specification rules • Which defined options are supported by the implementation • Protocol is fed with selected set of test input • Output generated are observed and checked against specifications • Formal specification may be used as the basis for deriving test sequences • Interoperability testing: validation of the protocol implementation using implementations from different manufacturers. • Ensures the protocol achieves the purpose of open communication. 1-2-8

  9. Protocol Properties • Usually defined informally • Definitions of the properties of a protocol (Sajkowski, 1985): • Deadlock Freeness = a protocol never enters a state in which no further progress is possible • Safety = a protocol never enters an unacceptable state • Liveness = every state of the system is reachable • Boundedness = during the operation of a protocol, the number of messages in each channel between protocol entities can never exceed the capacity of the channel. • Termination or Progress = completion of required services of a protocol within a finite amount of time (reaches desired final states in terminating protocols or initial state in cyclic protocols) 1-2-9

  10. Protocol Properties (Cont.) • Definitions of the properties of a protocol (Sajkowski, 1985): • Livelock Freeness = a system will never get into a situation where non-productive cycles are possible and the exchange of the same messages are performed • Mutual Exclusion = certain actions in both users of a protocol cannot be executed simultaneously (e.g., access to same resource) • Partial Correctness = a protocol is guaranteed to provide a required service if it reaches its desired final state • Absence of Overspecification = lack of redundant parts in protocol specification. E.g., absence of unexercised message receptions. • Completeness = descriptions of the reactions to all possible inputs are included in protocol specifications (i.e., specification if free of unspecified message receptions) 1-2-10

  11. Protocol Properties (Cont.) • Definitions of the properties of a protocol (Sajkowski, 1985): • Recovery from failures = after any abnormal situation, a protocol will return to a normal state within a finite amount of time • Fairness = every protocol entity gets a chance to make progress, regardless of what others do • Total Correctness = a protocol is partially correct and additionally reaches its desired final state (in the case of a terminating protocol) or its initial state (in the case of a cyclic protocol), for all allowed sequences of inputs (messages or user commands) • These protocol properties are not necessarily independent from each other • The possession of these properties do not ensure that a communication protocol will be working as expected, but it ensures that the protocol does not enter into an undesirable state 1-2-11

  12. Design and Analysis of Communication Software Credits: David L. Dill (Stanford University), Patrice Godefroid (Bell Labs), Joost-Pieter Katoen (University of Twaente), and High Integrity Embedded Systems Group (University of Northumbria)

  13. Peculiarities of Communication Software • Communication software coordinates the information flow between interconnected components in: • Telephone networks • Internet • Wireless networks (cell phones, pagers, etc.) • Distributed databases (e.g., flight reservation systems) • Communication software is widely used • Communication software is hard to develop and test 1-2-13

  14. Overview • Main steps of the software development process. • Main tools used for each of these steps in industry today. • More detailed discussion on testing. 1-2-14

  15. Software Development Process • The waterfall model • In real-life, steps overlap and have feedback loops 1-2-15

  16. Software Development Steps • Requirements specifications and analysis: • Determine customer-visible features, feasibility study, development costs, and price of the product • Determine “what to do” • Done by “system engineers” • Design: • Determine high-level and detailed design of product that will meet requirements • Determine “how to do it” • Done by the “architects” 1-2-16

  17. Software Development Steps (Cont’d) • Coding and unit testing: • Produces the actual code that will be delivered to the customer • Test individual modules in isolation • Done by “developers” (aks “programmers”) • Integration and system testing • Test the integration of individual modules and the whole system • Testing implies running the code • Done by “testers” • Delivery and maintenance • Deliver the product to the customer and provide documentation, training, field support, and bug fixes • “Product manager” manages the end-to-end process 1-2-17

  18. Development Tools • What are the most common tools used at each step of the development process today in the software industry? • Warnings: • The list that follows is not exhaustive • Only general-purpose tools are considered, not application-specific tools (for GUI, web, databases,…). • Names of commercial products are used only as examples, for illustration purposes only. 1-2-18

  19. Tools for Requirements Specification and Analysis • MS Word and PowerPoint!! • Done informally • Requirements are often imprecise, ambiguous, and incomplete • This can be done partly on purpose… • “Formal Notations”: • Use-cases, state machines, message sequence charts, tables, decision trees, etc. • Less ambiguous than English text • Enable simple automatic analysis of specification (check for consistency) • Can only cover a subset of requirements • In practice, used in conjunction with English text 1-2-19

  20. Tools for Design • MS Word and PowerPoint… • With diagrams, tables, state-machines, message sequence charts, etc. • Modeling Languages (for design and high-level coding) • UML, SDL, ObjectTime, VFSM, etc. • Less ambiguous than English text • Enables automatic analysis • Code can be generated automatically of a template of the implementation 1-2-20

  21. Tools for Coding • Defect tracking and resolution managers: • Track problem reports and the status of their resolution • Record “history” of the system • Also used by testers as well as during maintenance • Version control systems: • Controls and coordinates the various versions of the software (e.g., SCCS, CVS, etc.) • Code browsers and editors: • Help navigate through the code • Also help navigate the history of the code • Help compare different version of the code (e.g., diff) 1-2-21

  22. Tools for Coding (Cont’d) • Compilers • Translate high-level source language to lower-level target language • Report syntax errors • Linkers • Combine mutually referencing object code fragments • Report errors at module interface level • Code reviewers (static analyzers) • Examine source code to detect programming errors • Provide suggestion on code structure and style (type checkers, Lint, etc.) • Automated tools for detecting semantic errors through symbolic execution • Colleagues ! 1-2-22

  23. Tools for Testing • Debuggers: • Requires code instrumentation (usually during compilation) • Control and examine code execution • Memory analyzers: • Detect memory leaks and overflows • Memory leaks (= memory allocated, no more reachable but not freed) • Memory overflow (= access to unauthorized memory address), (unallocated/uninitialized memory, array out-of-bounds,…). • Parse and instrument source or binary code to check properties at runtime 1-2-23

  24. Tools for Testing (Cont’d) • Performance analyzers (profilers) and code coverage tools: • Count number of occurrences of execution of program statements or procedures • Report time spent in each part of the program execution • Parse and instrument source or binary code to record runtime information • Languages and platforms for test automation: • Example: expect • Capture/replay tools: • Record/replay actions performed during manual testing at standard interface • Example: GUI/web testing 1-2-24

  25. Tools for Testing (Cont’d) • Load Generators: • Simulate environment through standard interface • Example: network traffic • Test case generation from specification: • Generate sets of tests from higher-level specification of I/O behavior • Easier test management with better coverage • Test management tools: • Process: help record test plans, track, and report the status of testing project • Code: store and execute test code, compare, and store results • Used by testing organizations only 1-2-25

  26. More on Testing • Why test? • “To find errors” • “The process of executing a program with the extent of finding errors.” [Myers,1979] • What is an “error”? • “Any problem visible to the end user” • Programming errors, conflicts with requirements, unexpected behaviors, features too hard to use, etc. • When to stop testing? • In theory, when full coverage is reached • Coverage can be defined versus requirements, formal I/O, code, or state-space • In practice, test until shipment date! 1-2-26

  27. Tools for Testing: Summary • Three main types of tools for testing: 1. Code Inspection: analyses (parses) the code to find programming errors. 2. Code Instrumentation: analyses (parses) source code or binary code and inserts code (such as assertions) to check properties at run-time. 3. Code Execution: help generate, execute and evaluate tests performed by running the code in conjunction with a representation of its environment. 1-2-27

  28. Different Levels of Testing • Level 1: manual testing • Most testing organizations • Some tests cannot be automated • Level 2: automated testing • Automated test execution and evaluation • Advantage: automated regression testing • Level 3: automatic test generation • Automated test generation from higher-level specs • Advantages: easier test management with better coverage 1-2-28

  29. What is Communication Software? • Coordinates the information flow between interconnected components • Each component can be viewed as a reactive system • I.e., continually interacts with its environment • Examples: telephone, internet, wireless, etc. 1-2-29

  30. Peculiarities of Communication Software Revisited • Communication software is like any other software • Same overall development process and general-purpose tools • Developing communication • Many possible sequences of interactions between components • Coordination problems • Race conditions • Timing issues • Testing communication software is harder! • Traditional testing provides poor coverage • Debugging communication software is harder! • Scenarios leading to errors can be hard to reproduce 1-2-30

  31. Why Harder? • Implementation looks non-deterministic due to concurrency and real-time • Related to scheduling and processing speed, respectively • “Non-deterministic” means unpredictable • Same sequence of inputs does not imply same sequence of outputs • Fundamentally, parallel composition is not “compositional”: • Given 2 function f(x) and g(x), f(g(x)) is easy to understand 1-2-31

  32. Tools for Dealing with Concurrency • Debuggers for concurrent/distributed systems: • Control and track the execution of more than one process/thread • Tools for detecting runtime coordination problems: • Detect race conditions at runtime • simultaneous writes in same address • Detect coordination problems at runtime • Deadlocks • Instrument the execution of the processes/threads while minimizing the impact on timing • Record scheduling information (“trace”) for faithfully replaying multiprocess scenarios leading to errors. • Generate a consistent representation (snapshot) of the state of a distributed system • Example: Eraser, Assure (for Java) 1-2-32

  33. Tools for Dealing with Real-Time • Schedulability analyzers: • Analyze a set of real-time scheduling constraints and generate a schedule if there exists one • Constraints are imposed by the architecture and properties to satisfy (specs) • Worst-case execution time analyzers: • Determine worst-case execution time (WCET) of fragments of code • Performance modeling tools: • Analyze performance of an architectural model • Uses queuing theory, stochastic processes, etc. 1-2-33

  34. Another Approach: Formal Verification • What is verification? • 4 elements define a verification framework: Verification: to check if all possible behaviors of the implementation are compatible with the specification • While testing can only find errors, verification can also prove their absence (= exhaustive testing) • Example of approaches: theorem proving and model checking 1-2-34

  35. Theorem Proving • Goal: automate mathematical (logical) reasoning • Verification through theorem proving: • Implementation represented by a logic formula I • Specification represented by a logic formula S • Does “I implies S” hold? • Proof is carried out at syntactic level • This framework is very general • Many programs and properties can be checked this way • However, most proofs are not fully automatic • A theorem prover is actually a proof assistant and a proof checker • Limited usefulness 1-2-35

  36. Model Checking • Model checking is more restricted in scope but is fully automatic • Verification using model checking: • Implementation represented by a finite state machine M (called, state space) • Specification represented by a temporal logic formula f • Example: Linear-time Temporal Logic (LTL) • Specify properties of infinite sequences s0, s1, s2, …. of states • Temporal operations include: G (always), F (eventually) and X (next) • Example: G(p -> Fq) • Does “M satisfies f” hold? (hence the term “model checking”) • For LTL, do all infinite computations of M satisfy f? • Proof is carried out at semantic level via state-space exploration 1-2-36

  37. State-Space of A Concurrent System • The state space of a concurrent system is a graph representing the joint behavior of all its components. • Each node represents a state of the whole system. • Each path represents a scenario (sequence of actions) that can be executed by the system. • Many properties of a system can be checked by exploring its state space: deadlocks, dead code,… and model-checking. • Systematic State Space Exploration is simple: • easy to understand, • easy to implement, • easy to use: automatic! • Main limitation: “state explosion” problem! (State-space exploration is fundamentally hard) • Many tools are based on systematic state-space exploration: CAESAR, COSPAN, MURPHI, SMV, SPIN, etc. 1-2-37

  38. Formal Verification Vs. Testing • Model checking (e.g., SPIN) can be very effective in detecting subtle design errors • In practice, formal verification is actually testing because of approximations: • When modeling the system • When modeling the environment • When specifying properties • When performing the verification • Therefore, “bug hunting” is really the name of the game! 1-2-38

  39. Formal Verification Vs. Testing (Cont’d) • Model based testing 1-2-39

  40. Applications: Hardware • Hardware verification is a booming application of model checking and related techniques • The finite-state assumption is not unrealistic for hardware • The cost of errors can be enormous (e.g., Pentium bug) • The complexity of designs is increasing very rapidly (e.g., system on a chip) • However, model-checking still does not scale very well • Many designs and implementations are too big and complex • Hardware description languages (Verilog, VHDL, etc.) are very expressive • Using model checking property requires experience 1-2-40

  41. Applications: Software Models • Analysis of software models: (e.g., SPIN) • Analysis of communication protocols, distributed algorithms • Models specified in extended FSM notation • Restricted to design • Analysis of software models that can be compiled (e.g., SDL, VFSM) • Same as above except that FSM can be compiled to generate the core of the implementation • More popular with software developers since reuse of “model” is possible • Analysis still restricted to “FSM part” of the implementation 1-2-41

  42. Summary: Formal Methods • Formal methods are based on applied discrete mathematics • Set theory • Logic • Languages, analysis techniques, and tools • Applicable to the development of • Computer hardware • Computer software • Used for: • Description • Specifying requirements • Precise and unambiguous • Analysis • Demonstrate that program function implements design • Rigor of mathematics helps develop convincing argument • Reduces reliance on human intuition and judgment 1-2-42

  43. Summary: Reality Gap • Formal methods help fill the reality gap 1-2-43

  44. Summary: Filling the Reality Gap • Informally: review, testing • Formal methods are not complete solutions • How to fill the remaining space • Formal specification of required properties • Formal model of the delivered system • Formal demonstration that the system model has the required properties • Is it worth the trouble? • Apply only for design phases • Apply only for critical components and properties 1-2-44

  45. Summary: Validation Techniques • Wide spectrum of languages and inference rules • Process algebras: CCS, CSP • Logical methods • Set theory, predicate calculus • Temporal logics • Rewriting logic: OBJ, Maude • Proving equivalence or entailment • Not easy to fully automate • Theorem proving assistants • Model checkers • SPIN, SMV, UPPAAL, KRONOS • This approach can be fully automated 1-2-45

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