1 / 18

Test II 精读

Test II 精读. 廖钧. Paper. Background. Termination analysis Determine whether a program will terminate Correctness of an algorithm depends on termination Test termination properties of programs Can a test suite for a program be used profitably for constructing termination proofs?. Problem.

nathan
Download Presentation

Test II 精读

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. Test II 精读 廖钧

  2. Paper

  3. Background • Termination analysis • Determine whether a program will terminate • Correctness of an algorithm depends on termination • Test termination properties of programs • Can a test suite for a program be used profitably for constructing termination proofs?

  4. Problem • Generate a novel termination prover based on information derived from tests. (Called TpT) • What kind of program is TpT’s input? • How to define termination?

  5. Preliminaries • Define while programs • Input of TpT • Loop: L = while B do S over variables() • A program with loops

  6. Preliminaries • Define termination • Loop bound • () -> () • Linear expression = • If each loop has a loop bound, then termination

  7. Preliminaries • Generate log data from test inputs • Assume • Get two matrices • Machine learning Candidate loop bound Free invariant

  8. Preliminaries • Check loop bound • Add free_invariant and assert into program • Free_invariant can be ignored

  9. Algorithm • Input • While program • Test-Suite • Output • Loop bound

  10. Algorithm • Inner phase • Generate matrices A and C • “Off-the-self” quadratic programming to learn loop bounds • Fail: does not have integral coeffcients

  11. Algorithm • Validate phase • “Off-the-self checker” • Return counterexample(new tests) • Use new tests to next iteration

  12. Algorithm • Optimize • Round ??? • If a value between N-0.1 and N+0.1, round to N • Celling for positive and floor for negative • Can only make the bound looser？？？ • Example • [1.9,1,-1,0.95,0.24] -> [2,1,-1,1,1] • 2a + b – c + d + e • Partition • Loop does not have single bound • Different behavior for different inputs

  13. Evaluation • Micro-Benchmarks for termination • Oct: OCTANAL distribution • E.g. heapsort and bubblesort • Driver: Windows device drivers • Code fragments from them • Poly: POLYRANK distribution • http://www.eecs.qmul.ac.uk/~aziem/esop/polyrank/

  14. Evaluation • Windows Device Drivers • SDV-RP toolkit • Claim that other may have fail to prove termination

  15. Evaluation • They claim that • Prove 15% more benchmark • Inner phase of TpT is very efficient • Other technologies may have fail to prove termination

  16. Summary • Process • Prove more termination • Effeciency Prove termination while program and test suites Generate input for forming model Machine learning

  17. My opinion • All about algorithm • Many formulae • So many black box • Off-the-self, state-of-the-art????WTF

  18. Thank you

More Related