1 / 11

Using Symbolic PathFinder at NASA

Using Symbolic PathFinder at NASA. Corina P ã s ã reanu Carnegie Mellon/NASA Ames. Introduction. Modern software Multi-core, cloud-based, cyber-physical Needs to be highly reliable and secure Testing Most widely used May miss important errors Formal analysis

milton
Télécharger la présentation

Using Symbolic PathFinder at NASA

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Using Symbolic PathFinder at NASA Corina Pãsãreanu Carnegie Mellon/NASA Ames

  2. Introduction • Modern software • Multi-core, cloud-based, cyber-physical • Needs to be highly reliable and secure • Testing • Most widely used • May miss important errors • Formal analysis • theorem proving, model checking, symbolic execution, static analysis, etc. • Strong guarantees of reliability and safety • Goal • Use formal techniques to make testing more effective

  3. Symbolic PathFinder (SPF) • Performs • Automated test case generation • Property checking • Integrates • Symbolic execution, model checking and constraint solving • Uses analysis engine of Ames’ JPF tool Test case generation – the easiest path towards the infusion of advanced formal verification technologies JPF

  4. Uses of SPF • Analysis/test generation for • Byte-code • Models (Simulink/Stateflow, UML) • Translation into Java • Polyglot Framework – w/ U. Vanderbilt, U. Minnesota • SPF handles • Pre-conditions, data structures, concurrency • User customizes • Coverage, search strategy, output format • Used at NASA, Fujitsu, academia Polyglot Orion orbits the moon (Image Credit: Lockheed Martin)

  5. OAE Structure Inputs Checks Flight Rules to see if an abort must occur Select Feasible Aborts Pick Highest Ranked Abort Experience: Onboard Abort Executive (OAE) Prototype for CEV ascent abort handling being developed by JSC GN&C Results • Baseline • Manual testing: time consuming • Guided random testing could not cover all aborts • Symbolic PathFinder • Generates tests to cover all aborts and flight rules • Found major bug in new version of OAE • Environment constraints • Input data is constrained by environment/physical laws Example: inertial velocity can not be 24000 ft/s when the geodetic altitude is 0 ft • Need to encode these environment constraints explicitly • Solution • Use simulation runs and machine learning to get data correlations

  6. Lessons Learned • Not enough to generate test cases that achieve good coverage • Should also make sense to developers • Environment assumptions need to be made explicit • Using both input from the user and automated inference techniques • True for any analysis tool that can only analyze components (not entire systems)

  7. Usability Challenges • Tool Usability • Research Challenges • Domain Specific

  8. Tool Usability • Easy set-up -- Not true for JPF/SPF • Portability for multiple platforms • Animation and/or graphical display of results • Intermediate results • Important for tools that do not give “instant” results like SPF • Informative counterexamples/Explanation of errors • Documentation easily accessible on-line -- JPF’s wiki • Integration with other tools familiar with developers • JPF’s Eclipse plug-in; SPF outputs JUnit tests and Antares scripts • Clear display of different options --Not true for JPF/SPF • Availability --JPF is open-source • …

  9. Research Challenges • Scalable and timely verification • Analysis as efficient as possible • Parallelization, compositional techniques, fast constraint solvers • New constraint solvers • Solve complex, non-linear constraints (NASA applications) • Solve strings combined with numeric constraints (web applications, security) • Automatic discovery of interfaces • With (minimal) inputs from user • Combinations of automata and machine learning techniques • …

  10. Domain Specific Knowledge • Add the user to the picture • Integration of domain specific knowledge • Adaptation of tools to users’; specific needs • E.g. NASA uses models; adapt SPF from code to models; customize search strategies/output format • …

  11. ?

More Related