alexander p
play

Alexander P . Conrad , Robert S. Roos , and Gregory M. Kapfhammer - PowerPoint PPT Presentation

Feb 22, 2024 •234 likes •1.42k views

Introduction Regression Testing Empirical Study Conclusion Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization Alexander P . Conrad , Robert S. Roos , and Gregory M. Kapfhammer

  1. Introduction Regression Testing Empirical Study Conclusion Importance of Test Suite Prioritization CE = 0.5789 3, 1, 2 5 Test Orderings Covered Test Requirements ( C ( T , l )) 4 1,2,3 1,2,3 1,2,3 1,2,3 3 1,3,2 1,3,2 1,3,2 2 2,3,1 2,3,1 1 3,1,2 0 0 5 10 15 Testing Time ( l ) Greedy methods often produce high effectiveness orderings Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 4 / 15

  2. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods T 1 T 3 T 2 T 4 Possible configuration of the coverage report Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  3. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods T 1 T 3 T 2 T 4 R 1 R 2 R 3 Possible configuration of the coverage report Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  4. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods T 1 T 1 T 3 T 3 T 2 T 2 T 4 T 4 R 1 R 1 R 2 R 2 R 3 R 3 Possible configuration of the coverage report Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  5. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods T 1 T 1 T 1 T 3 T 3 T 3 T 2 T 2 T 2 T 4 T 4 T 4 R 1 R 1 R 1 R 2 R 2 R 2 R 3 R 3 R 3 Possible configuration of the coverage report Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  6. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods time ( T 1 ) = 1 T 1 T 1 T 1 T 3 T 3 T 3 T 2 T 2 T 2 T 4 T 4 T 4 time ( T 2 ) = 1 time ( T 3 ) = 1 R 1 R 1 R 1 R 2 R 2 R 2 R 3 R 3 R 3 Execution time of the test cases may mislead greedy Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  7. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods time ( T 1 ) = 1 T 1 T 1 T 1 T 3 T 3 T 3 T 2 T 2 T 2 T 4 T 4 T 4 time ( T 2 ) = 1 time ( T 3 ) = 1 R 1 R 1 R 1 R 2 R 2 R 2 R 3 R 3 R 3 time ( T 4 ) = 2 . 45 Execution time of the test cases may mislead greedy Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  8. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods time ( T 1 ) = 1 T 1 T 1 T 1 T 3 T 3 T 3 T 2 T 2 T 2 T 4 T 4 T 4 time ( T 2 ) = 1 time ( T 3 ) = 1 R 1 R 1 R 1 R 2 R 2 R 2 R 3 R 3 R 3 time ( T 4 ) = 2 . 45 T 1 T 2 T 3 T 4 Original ordering has low effectiveness score Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  9. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods time ( T 1 ) = 1 T 1 T 1 T 1 T 3 T 3 T 3 T 2 T 2 T 2 T 4 T 4 T 4 time ( T 2 ) = 1 time ( T 3 ) = 1 R 1 R 1 R 1 R 2 R 2 R 2 R 3 R 3 R 3 time ( T 4 ) = 2 . 45 T 1 T 2 T 3 T 4 CE ( T ) = 0 . 54 Original ordering has low effectiveness score Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  10. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods time ( T 1 ) = 1 T 1 T 1 T 1 T 3 T 3 T 3 T 2 T 2 T 2 T 4 T 4 T 4 time ( T 2 ) = 1 time ( T 3 ) = 1 R 1 R 1 R 1 R 2 R 2 R 2 R 3 R 3 R 3 time ( T 4 ) = 2 . 45 T 1 T 3 T 2 T 1 T 3 T 2 T 4 T 4 Greedy method constructs suite with marginal improvement Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  11. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods time ( T 1 ) = 1 T 1 T 1 T 1 T 3 T 3 T 3 T 2 T 2 T 2 T 4 T 4 T 4 time ( T 2 ) = 1 time ( T 3 ) = 1 R 1 R 1 R 1 R 2 R 2 R 2 R 3 R 3 R 3 time ( T 4 ) = 2 . 45 T 1 T 3 T 2 T 1 T 3 T 2 T 4 T 4 CE ( T ′ ) = 0 . 55 Greedy method constructs suite with marginal improvement Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  12. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods time ( T 1 ) = 1 T 1 T 1 T 1 T 3 T 3 T 3 T 2 T 2 T 2 T 4 T 4 T 4 time ( T 2 ) = 1 time ( T 3 ) = 1 R 1 R 1 R 1 R 2 R 2 R 2 R 3 R 3 R 3 time ( T 4 ) = 2 . 45 T 1 T 3 T 2 T 1 T 3 T 2 T 4 T 4 CE ( T ′ ) = 0 . 55 Greedy can exhibit high run-times (Jiang et al. ASE 2009) Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  13. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods time ( T 1 ) = 1 T 1 T 1 T 1 T 3 T 3 T 3 T 2 T 2 T 2 T 4 T 4 T 4 time ( T 2 ) = 1 time ( T 3 ) = 1 R 1 R 1 R 1 R 2 R 2 R 2 R 3 R 3 R 3 time ( T 4 ) = 2 . 45 T 3 T 1 T 1 T 1 T 2 T 2 T 3 T 2 T 4 T 4 T 4 T 3 Genetic algorithm finds a higher quality ordering Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  14. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods time ( T 1 ) = 1 T 1 T 1 T 1 T 3 T 3 T 3 T 2 T 2 T 2 T 4 T 4 T 4 time ( T 2 ) = 1 time ( T 3 ) = 1 R 1 R 1 R 1 R 2 R 2 R 2 R 3 R 3 R 3 time ( T 4 ) = 2 . 45 T 1 T 3 T 1 T 2 T 1 T 2 T 3 T 2 T 4 T 3 T 4 T 4 CE ( T ′ ) = 0 . 63 Genetic algorithm finds a higher quality ordering Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  15. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods time ( T 1 ) = 1 T 1 T 1 T 1 T 3 T 3 T 3 T 2 T 2 T 2 T 4 T 4 T 4 time ( T 2 ) = 1 time ( T 3 ) = 1 R 1 R 1 R 1 R 2 R 2 R 2 R 3 R 3 R 3 time ( T 4 ) = 2 . 45 T 1 T 3 T 1 T 2 T 1 T 2 T 3 T 2 T 4 T 3 T 4 T 4 CE ( T ′ ) = 0 . 63 Genetic algorithm is amenable to parallelization Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  16. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods time ( T 1 ) = 1 T 1 T 1 T 1 T 3 T 3 T 3 T 2 T 2 T 2 T 4 T 4 T 4 time ( T 2 ) = 1 time ( T 3 ) = 1 R 1 R 1 R 1 R 2 R 2 R 2 R 3 R 3 R 3 time ( T 4 ) = 2 . 45 T 1 T 3 T 1 T 2 T 1 T 2 T 3 T 2 T 4 T 3 T 4 T 4 CE ( T ′ ) = 0 . 63 Genetic algorithm supports “human in the loop” Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  17. Introduction Regression Testing Empirical Study Conclusion Limitations of Greedy Methods time ( T 1 ) = 1 T 1 T 1 T 1 T 3 T 3 T 3 T 2 T 2 T 2 T 4 T 4 T 4 time ( T 2 ) = 1 time ( T 3 ) = 1 R 1 R 1 R 1 R 2 R 2 R 2 R 3 R 3 R 3 time ( T 4 ) = 2 . 45 T 1 T 3 T 1 T 2 T 1 T 2 T 3 T 2 T 4 T 3 T 4 T 4 CE ( T ′ ) = 0 . 63 Genetic algorithm constructs diverse test orderings Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 5 / 15

  18. Introduction Regression Testing Empirical Study Conclusion Test Prioritization with Genetic Algorithms Initialize Fitness Selection Crossover Mutation Randomly create suites by repeatedly shuffling � T 1 , . . . , T n � Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 6 / 15

  19. Introduction Regression Testing Empirical Study Conclusion Test Prioritization with Genetic Algorithms Initialize Execute Fitness Fitness Selection Selection Crossover Crossover Mutation Mutation Execute the phases until the genetic algorithm stagnates Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 6 / 15

  20. Introduction Regression Testing Empirical Study Conclusion Test Prioritization with Genetic Algorithms Initialize Execute Execute Fitness Fitness Fitness Selection Selection Selection Crossover Crossover Crossover Mutation Mutation Mutation Operators Use coverage effectiveness in this study - others possible Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 6 / 15

  21. Introduction Regression Testing Empirical Study Conclusion Test Prioritization with Genetic Algorithms Initialize Execute Execute Fitness Fitness Fitness Selection Selection Selection Crossover Crossover Crossover Mutation Mutation Mutation Operators CE Use coverage effectiveness in this study - others possible Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 6 / 15

  22. Introduction Regression Testing Empirical Study Conclusion Test Prioritization with Genetic Algorithms Initialize Execute Execute Fitness Fitness Fitness Selection Selection Selection Crossover Crossover Crossover Mutation Mutation Mutation Operators CE APFD APRC Use coverage effectiveness in this study - others possible Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 6 / 15

  23. Introduction Regression Testing Empirical Study Conclusion Test Prioritization with Genetic Algorithms Execute Execute Execute Initialize Fitness Fitness Fitness Fitness Selection Selection Selection Selection Crossover Crossover Crossover Crossover Mutation Mutation Mutation Mutation Choose orderings to become parents of next generation Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 6 / 15

  24. Introduction Regression Testing Empirical Study Conclusion Test Prioritization with Genetic Algorithms Execute Execute Execute Initialize Fitness Fitness Fitness Fitness Selection Selection Selection Selection Crossover Crossover Crossover Crossover Mutation Mutation Mutation Mutation Operators Choose orderings to become parents of next generation Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 6 / 15

  25. Introduction Regression Testing Empirical Study Conclusion Test Prioritization with Genetic Algorithms Execute Execute Execute Initialize Fitness Fitness Fitness Fitness Selection Selection Selection Selection Crossover Crossover Crossover Crossover Mutation Mutation Mutation Mutation Operators ROU TOU TRU Choose orderings to become parents of next generation Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 6 / 15

  26. Introduction Regression Testing Empirical Study Conclusion Test Prioritization with Genetic Algorithms Execute Execute Execute Execute Initialize Fitness Fitness Fitness Fitness Fitness Selection Selection Selection Selection Selection Crossover Crossover Crossover Crossover Crossover Mutation Mutation Mutation Mutation Mutation Seven possible operators combine parents to produce children Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 6 / 15

  27. Introduction Regression Testing Empirical Study Conclusion Test Prioritization with Genetic Algorithms Execute Execute Execute Execute Initialize Fitness Fitness Fitness Fitness Fitness Selection Selection Selection Selection Selection Crossover Crossover Crossover Crossover Crossover Mutation Mutation Mutation Mutation Mutation Operators Seven possible operators combine parents to produce children Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 6 / 15

  28. Introduction Regression Testing Empirical Study Conclusion Test Prioritization with Genetic Algorithms Execute Execute Execute Initialize Execute Fitness Fitness Fitness Fitness Fitness Selection Selection Selection Selection Selection Crossover Crossover Crossover Crossover Crossover Mutation Mutation Mutation Mutation Mutation Operators MPX PMX POS Seven possible operators combine parents to produce children Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 6 / 15

  29. Introduction Regression Testing Empirical Study Conclusion Test Prioritization with Genetic Algorithms Execute Execute Execute Execute Execute Initialize Fitness Fitness Fitness Fitness Fitness Fitness Selection Selection Selection Selection Selection Selection Crossover Crossover Crossover Crossover Crossover Crossover Mutation Mutation Mutation Mutation Mutation Mutation Six possible operators make random changes to orderings Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 6 / 15

  30. Introduction Regression Testing Empirical Study Conclusion Test Prioritization with Genetic Algorithms Execute Execute Execute Execute Execute Initialize Fitness Fitness Fitness Fitness Fitness Fitness Selection Selection Selection Selection Selection Selection Crossover Crossover Crossover Crossover Crossover Crossover Mutation Mutation Mutation Mutation Mutation Mutation Operators Six possible operators make random changes to orderings Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 6 / 15

  31. Introduction Regression Testing Empirical Study Conclusion Test Prioritization with Genetic Algorithms Execute Execute Execute Execute Execute Initialize Fitness Fitness Fitness Fitness Fitness Fitness Selection Selection Selection Selection Selection Selection Crossover Crossover Crossover Crossover Crossover Crossover Mutation Mutation Mutation Mutation Mutation Mutation Operators ISM IVM SIM Six possible operators make random changes to orderings Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 6 / 15

  32. Introduction Regression Testing Empirical Study Conclusion Configuration of the Genetic Algorithm Possible Configurations Child Density Population Stagnancy Mutation Rate Explored a wide variety of genetic algorithm configurations Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 7 / 15

  33. Introduction Regression Testing Empirical Study Conclusion Configuration of the Genetic Algorithm Possible Configurations Possible Configurations Child Density Child Density Population Population Stagnancy Stagnancy Mutation Rate Mutation Rate How frequently do we modify individual test orderings ? Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 7 / 15

  34. Introduction Regression Testing Empirical Study Conclusion Configuration of the Genetic Algorithm Possible Configurations Possible Configurations Child Density Child Density Population Population Stagnancy Stagnancy Mutation Rate Mutation Rate Parameter Values How frequently do we modify individual test orderings ? Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 7 / 15

  35. Introduction Regression Testing Empirical Study Conclusion Configuration of the Genetic Algorithm Possible Configurations Possible Configurations Child Density Child Density Population Population Stagnancy Stagnancy Mutation Rate Mutation Rate Parameter Values 0 . 10 0 . 33 0 . 67 How frequently do we modify individual test orderings ? Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 7 / 15

  36. Introduction Regression Testing Empirical Study Conclusion Configuration of the Genetic Algorithm Possible Configurations Possible Configurations Possible Configurations Child Density Child Density Child Density Population Population Population Stagnancy Stagnancy Stagnancy Mutation Rate Mutation Rate Mutation Rate How many children should be in the next population ? Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 7 / 15

  37. Introduction Regression Testing Empirical Study Conclusion Configuration of the Genetic Algorithm Possible Configurations Possible Configurations Possible Configurations Child Density Child Density Child Density Population Population Population Stagnancy Stagnancy Stagnancy Mutation Rate Mutation Rate Mutation Rate Parameter Values How many children should be in the next population ? Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 7 / 15

  38. Introduction Regression Testing Empirical Study Conclusion Configuration of the Genetic Algorithm Possible Configurations Possible Configurations Possible Configurations Child Density Child Density Child Density Population Population Population Stagnancy Stagnancy Stagnancy Mutation Rate Mutation Rate Mutation Rate Parameter Values 0 . 50 0 . 75 1 . 00 How many children should be in the next population ? Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 7 / 15

  39. Introduction Regression Testing Empirical Study Conclusion Configuration of the Genetic Algorithm Possible Configurations Possible Configurations Possible Configurations Possible Configurations Child Density Child Density Child Density Child Density Population Population Population Population Stagnancy Stagnancy Stagnancy Stagnancy Mutation Rate Mutation Rate Mutation Rate Mutation Rate How many test suites should exist in the population? Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 7 / 15

  40. Introduction Regression Testing Empirical Study Conclusion Configuration of the Genetic Algorithm Possible Configurations Possible Configurations Possible Configurations Possible Configurations Child Density Child Density Child Density Child Density Population Population Population Population Stagnancy Stagnancy Stagnancy Stagnancy Mutation Rate Mutation Rate Mutation Rate Mutation Rate Parameter Values How many test suites should exist in the population? Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 7 / 15

  41. Introduction Regression Testing Empirical Study Conclusion Configuration of the Genetic Algorithm Possible Configurations Possible Configurations Possible Configurations Possible Configurations Child Density Child Density Child Density Child Density Population Population Population Population Stagnancy Stagnancy Stagnancy Stagnancy Mutation Rate Mutation Rate Mutation Rate Mutation Rate Parameter Values 75 150 225 How many test suites should exist in the population? Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 7 / 15

  42. Introduction Regression Testing Empirical Study Conclusion Configuration of the Genetic Algorithm Possible Configurations Possible Configurations Possible Configurations Possible Configurations Possible Configurations Child Density Child Density Child Density Child Density Child Density Population Population Population Population Population Stagnancy Stagnancy Stagnancy Stagnancy Stagnancy Mutation Rate Mutation Rate Mutation Rate Mutation Rate Mutation Rate How many generations without fitness improvement ? Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 7 / 15

  43. Introduction Regression Testing Empirical Study Conclusion Configuration of the Genetic Algorithm Possible Configurations Possible Configurations Possible Configurations Possible Configurations Possible Configurations Child Density Child Density Child Density Child Density Child Density Population Population Population Population Population Stagnancy Stagnancy Stagnancy Stagnancy Stagnancy Mutation Rate Mutation Rate Mutation Rate Mutation Rate Mutation Rate Parameter Values How many generations without fitness improvement ? Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 7 / 15

  44. Introduction Regression Testing Empirical Study Conclusion Configuration of the Genetic Algorithm Possible Configurations Possible Configurations Possible Configurations Possible Configurations Possible Configurations Child Density Child Density Child Density Child Density Child Density Population Population Population Population Population Stagnancy Stagnancy Stagnancy Stagnancy Stagnancy Mutation Rate Mutation Rate Mutation Rate Mutation Rate Mutation Rate Parameter Values 20 30 40 How many generations without fitness improvement ? Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 7 / 15

  45. Introduction Regression Testing Empirical Study Conclusion Configuration of the Genetic Algorithm Possible Configurations Possible Configurations Possible Configurations Possible Configurations Possible Configurations Child Density Child Density Child Density Child Density Child Density Population Population Population Population Population Stagnancy Stagnancy Stagnancy Stagnancy Stagnancy Mutation Rate Mutation Rate Mutation Rate Mutation Rate Mutation Rate Parameter Values 20 30 40 See the paper for further operator and configuration details Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 7 / 15

  46. Introduction Regression Testing Empirical Study Conclusion Analysis Techniques: Tree Models sel_method: TOU3, TOU4, TOU5 Tree Models: Recursive partitioning creates hierarchical view of data Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 8 / 15

  47. Introduction Regression Testing Empirical Study Conclusion Analysis Techniques: Tree Models sel_method: TOU3, TOU4, TOU5 child_density < 0.875 0.9674 Tree Models: Recursive partitioning creates hierarchical view of data Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 8 / 15

  48. Introduction Regression Testing Empirical Study Conclusion Analysis Techniques: Tree Models sel_method: TOU3, TOU4, TOU5 child_density < 0.875 0.9674 0.9573 Tree Models: Recursive partitioning creates hierarchical view of data Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 8 / 15

  49. Introduction Regression Testing Empirical Study Conclusion Analysis Techniques: Tree Models sel_method: TOU3, TOU4, TOU5 child_density < 0.875 0.9674 pop_size < 187.5 0.9573 Tree Models: Recursive partitioning creates hierarchical view of data Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 8 / 15

  50. Introduction Regression Testing Empirical Study Conclusion Analysis Techniques: Tree Models sel_method: TOU3, TOU4, TOU5 sel_method child_density < 0.875 0.9674 child_density pop_size pop_size < 187.5 0.9573 Explanatory Variable: Configuration of the genetic algorithm Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 8 / 15

  51. Introduction Regression Testing Empirical Study Conclusion Analysis Techniques: Tree Models sel_method: TOU3, TOU4, TOU5 sel_method sel_method child_density < 0.875 0.9674 child_density pop_size pop_size < 187.5 0.9573 Categorical Non-parametric techniques that handles different variable types Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 8 / 15

  52. Introduction Regression Testing Empirical Study Conclusion Analysis Techniques: Tree Models sel_method: TOU3, TOU4, TOU5 sel_method child_density < 0.875 0.9674 child_density child_density pop_size pop_size pop_size < 187.5 0.9573 Categorical Categorical Numerical Non-parametric techniques that handles different variable types Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 8 / 15

  53. Introduction Regression Testing Empirical Study Conclusion Analysis Techniques: Tree Models sel_method: TOU3, TOU4, TOU5 sel_method child_density < 0.875 0.9674 0.9674 child_density pop_size pop_size < 187.5 0.9573 0.9573 Categorical Categorical Categorical Numerical Numerical Response Variable: Fitness of the final test ordering (CE score) Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 8 / 15

  54. Introduction Regression Testing Empirical Study Conclusion Experimental Goals and Design | T | |R ( T ) | Name CCN NCSS DS 110 40 1.35 1243.00 GB 51 88 2.60 1455.00 JD 54 783 1.64 2716.00 LF 13 6 1.40 215.00 RM 13 19 2.13 569.00 SK 27 117 2.00 628.00 TM 27 46 2.21 748.00 RP 76 221 2.65 6822.00 Several applications and test suites - coverage reports derived from call-tree based adequacy (McMaster and Memon ICSM 2005) Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 9 / 15

  55. Introduction Regression Testing Empirical Study Conclusion Experimental Goals and Design | T | |R ( T ) | Name CCN NCSS DS 110 40 1.35 1243.00 GB 51 88 2.60 1455.00 JD 54 783 1.64 2716.00 LF 13 6 1.40 215.00 RM 13 19 2.13 569.00 SK 27 117 2.00 628.00 TM 27 46 2.21 748.00 RP 76 221 2.65 6822.00 Several applications and test suites - coverage reports derived from call-tree based adequacy (McMaster and Memon ICSM 2005) Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 9 / 15

  56. Introduction Regression Testing Empirical Study Conclusion Experimental Goals and Design | T | |R ( T ) | Name CCN NCSS DS 110 40 1.35 1243.00 GB 51 88 2.60 1455.00 JD 54 783 1.64 2716.00 LF 13 6 1.40 215.00 RM 13 19 2.13 569.00 SK 27 117 2.00 628.00 TM 27 46 2.21 748.00 RP 76 221 2.65 6822.00 Several applications and test suites - coverage reports derived from call-tree based adequacy (McMaster and Memon ICSM 2005) Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 9 / 15

  57. Introduction Regression Testing Empirical Study Conclusion Experimental Goals and Design | T | |R ( T ) | Name CCN NCSS DS 110 40 1.35 1243.00 GB 51 88 2.60 1455.00 JD 54 783 1.64 2716.00 LF 13 6 1.40 215.00 RM 13 19 2.13 569.00 SK 27 117 2.00 628.00 TM 27 46 2.21 748.00 RP 76 221 2.65 6822.00 Use additional case study applications and adequacy criteria as future work in order to control threats to external validity Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 9 / 15

  58. Introduction Regression Testing Empirical Study Conclusion Experimental Goals and Design | T | |R ( T ) | Name CCN NCSS DS 110 40 1.35 1243.00 GB 51 88 2.60 1455.00 JD 54 783 1.64 2716.00 LF 13 6 1.40 215.00 RM 13 19 2.13 569.00 SK 27 117 2.00 628.00 TM 27 46 2.21 748.00 RP 76 221 2.65 6822.00 Use random and hill climbing (first and steepest ascent) as control methods for comparison to the genetic algorithm prioritizer Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 9 / 15

  59. Introduction Regression Testing Empirical Study Conclusion Experimental Goals and Design | T | |R ( T ) | Name CCN NCSS DS 110 40 1.35 1243.00 GB 51 88 2.60 1455.00 JD 54 783 1.64 2716.00 LF 13 6 1.40 215.00 RM 13 19 2.13 569.00 SK 27 117 2.00 628.00 TM 27 46 2.21 748.00 RP 76 221 2.65 6822.00 See the paper for more details about the design of the empirical study (e.g., configuration of random and hill climbing prioritizers) Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 9 / 15

  60. Introduction Regression Testing Empirical Study Conclusion Results: Selection Method Importance sel_method: TOU3, TOU4, TOU5 RM child_density < 0.875 0.9674 Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 10 / 15

  61. Introduction Regression Testing Empirical Study Conclusion Results: Selection Method Importance sel_method: TOU3, TOU4, TOU5 RM sel_method child_density < 0.875 0.9674 Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 10 / 15

  62. Introduction Regression Testing Empirical Study Conclusion Results: Selection Method Importance sel_method: TOU3, TOU4, TOU5 RM sel_method child_density < 0.875 0.9674 sel_method: TOU3, TOU4, TOU5 GB mutation_rate < 0.17 cross_operator: OX1, VR Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 10 / 15

  63. Introduction Regression Testing Empirical Study Conclusion Results: Selection Method Importance sel_method: TOU3, TOU4, TOU5 RM sel_method child_density < 0.875 0.9674 sel_method sel_method: TOU3, TOU4, TOU5 GB mutation_rate < 0.17 cross_operator: OX1, VR Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 10 / 15

  64. Introduction Regression Testing Empirical Study Conclusion Results: Selection Method Importance sel_method: TOU3, TOU4, TOU5 RM sel_method child_density < 0.875 0.9674 sel_method sel_method: TOU3, TOU4, TOU5 GB mutation_rate < 0.17 cross_operator: OX1, VR The sel_method variable is always the most important parameter Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 10 / 15

  65. Introduction Regression Testing Empirical Study Conclusion Results: Selection Method Importance sel_method: TOU3, TOU4, TOU5 RM sel_method child_density < 0.875 0.9674 sel_method sel_method: TOU3, TOU4, TOU5 GB mutation_rate < 0.17 cross_operator: OX1, VR Importance of sel_method holds for all case study applications Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 10 / 15

  66. Introduction Regression Testing Empirical Study Conclusion Results: Selection Method Importance sel_method: TOU3, TOU4, TOU5 RM sel_method child_density < 0.875 0.9674 sel_method sel_method: TOU3, TOU4, TOU5 GB mutation_rate < 0.17 cross_operator: OX1, VR How does the selection method impact the genetic algorithm? Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 10 / 15

  67. Introduction Regression Testing Empirical Study Conclusion Results: Selection Intensity Name ROUE ROUL TRU60 TRU40 TOU2 TOU5 DS 0.9742 0.9837 0.9893 0.9915 0.9514 0.9706 GB 0.9500 0.9572 0.9668 0.9700 0.9062 0.9402 JD 0.9247 0.9328 0.9431 0.9451 0.8993 0.9192 LF 0.9903 0.9903 0.9903 0.9903 0.9903 0.9903 RM 0.9665 0.9670 0.9681 0.9682 0.9328 0.9475 RP 0.9774 0.9824 0.9868 0.9879 0.9570 0.9705 SK 0.9859 0.9878 0.9911 0.9915 0.9667 0.9763 TM 0.9585 0.9605 0.9662 0.9672 0.9503 0.9579 Avg. 0.9659 0.9702 0.9752 0.9765 0.9443 0.9591 Except for the smallest application (LF), the CE scores of the evolved orderings are better than the initial and reverse test suites Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 11 / 15

  68. Introduction Regression Testing Empirical Study Conclusion Results: Selection Intensity Name ROUE ROUL TRU60 TRU40 TOU2 TOU5 DS 0.9742 0.9837 0.9893 0.9915 0.9514 0.9706 GB 0.9500 0.9572 0.9668 0.9700 0.9062 0.9402 JD 0.9247 0.9328 0.9431 0.9451 0.8993 0.9192 LF 0.9903 0.9903 0.9903 0.9903 0.9903 0.9903 RM 0.9665 0.9670 0.9681 0.9682 0.9328 0.9475 RP 0.9774 0.9824 0.9868 0.9879 0.9570 0.9705 SK 0.9859 0.9878 0.9911 0.9915 0.9667 0.9763 TM 0.9585 0.9605 0.9662 0.9672 0.9503 0.9579 Avg. 0.9659 0.9702 0.9752 0.9765 0.9443 0.9591 Study a type of operator as it increases in intensity , or the change in average fitness due to selection (Blickle & Thiele, Evol Comp 1997) Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 11 / 15

  69. Introduction Regression Testing Empirical Study Conclusion Results: Selection Intensity Name ROUE ROUL TRU60 TRU40 TOU2 TOU5 DS 0.9742 0.9837 0.9893 0.9915 0.9514 0.9706 GB 0.9500 0.9572 0.9668 0.9700 0.9062 0.9402 JD 0.9247 0.9328 0.9431 0.9451 0.8993 0.9192 LF 0.9903 0.9903 0.9903 0.9903 0.9903 0.9903 RM 0.9665 0.9670 0.9681 0.9682 0.9328 0.9475 RP 0.9774 0.9824 0.9868 0.9879 0.9570 0.9705 SK 0.9859 0.9878 0.9911 0.9915 0.9667 0.9763 TM 0.9585 0.9605 0.9662 0.9672 0.9503 0.9579 Avg. 0.9659 0.9702 0.9752 0.9765 0.9443 0.9591 Increasing selection intensity improves the CE scores of test orderings, even though it does not cause more rapid convergence Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 11 / 15

  70. Introduction Regression Testing Empirical Study Conclusion Results: Selection Intensity Name ROUE ROUL TRU60 TRU40 TOU2 TOU5 DS 0.9742 0.9837 0.9893 0.9915 0.9514 0.9706 GB 0.9500 0.9572 0.9668 0.9700 0.9062 0.9402 JD 0.9247 0.9328 0.9431 0.9451 0.8993 0.9192 LF 0.9903 0.9903 0.9903 0.9903 0.9903 0.9903 RM 0.9665 0.9670 0.9681 0.9682 0.9328 0.9475 RP 0.9774 0.9824 0.9868 0.9879 0.9570 0.9705 SK 0.9859 0.9878 0.9911 0.9915 0.9667 0.9763 TM 0.9585 0.9605 0.9662 0.9672 0.9503 0.9579 Avg. 0.9659 0.9702 0.9752 0.9765 0.9443 0.9591 Increasing selection intensity improves the CE scores of test orderings, even though it does not cause more rapid convergence Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 11 / 15

  71. Introduction Regression Testing Empirical Study Conclusion Results: Selection Intensity Name ROUE ROUL TRU60 TRU40 TOU2 TOU5 DS 0.9742 0.9837 0.9893 0.9915 0.9514 0.9706 GB 0.9500 0.9572 0.9668 0.9700 0.9062 0.9402 JD 0.9247 0.9328 0.9431 0.9451 0.8993 0.9192 LF 0.9903 0.9903 0.9903 0.9903 0.9903 0.9903 RM 0.9665 0.9670 0.9681 0.9682 0.9328 0.9475 RP 0.9774 0.9824 0.9868 0.9879 0.9570 0.9705 SK 0.9859 0.9878 0.9911 0.9915 0.9667 0.9763 TM 0.9585 0.9605 0.9662 0.9672 0.9503 0.9579 Avg. 0.9659 0.9702 0.9752 0.9765 0.9443 0.9591 Increasing selection intensity improves the CE scores of test orderings, even though it does not cause more rapid convergence Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 11 / 15

  72. Introduction Regression Testing Empirical Study Conclusion Results: Selection Intensity Name ROUE ROUL TRU60 TRU40 TOU2 TOU5 DS 0.9742 0.9837 0.9893 0.9915 0.9514 0.9706 GB 0.9500 0.9572 0.9668 0.9700 0.9062 0.9402 JD 0.9247 0.9328 0.9431 0.9451 0.8993 0.9192 LF 0.9903 0.9903 0.9903 0.9903 0.9903 0.9903 RM 0.9665 0.9670 0.9681 0.9682 0.9328 0.9475 RP 0.9774 0.9824 0.9868 0.9879 0.9570 0.9705 SK 0.9859 0.9878 0.9911 0.9915 0.9667 0.9763 TM 0.9585 0.9605 0.9662 0.9672 0.9503 0.9579 Avg. 0.9659 0.9702 0.9752 0.9765 0.9443 0.9591 Low intensity selection causes search to meander around low quality test suite prioritizations, making fitness stagnate and the GA terminate Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 11 / 15

  73. Introduction Regression Testing Empirical Study Conclusion Results: Selection Intensity Name ROUE ROUL TRU60 TRU40 TOU2 TOU5 DS 0.9742 0.9837 0.9893 0.9915 0.9514 0.9706 GB 0.9500 0.9572 0.9668 0.9700 0.9062 0.9402 JD 0.9247 0.9328 0.9431 0.9451 0.8993 0.9192 LF 0.9903 0.9903 0.9903 0.9903 0.9903 0.9903 RM 0.9665 0.9670 0.9681 0.9682 0.9328 0.9475 RP 0.9774 0.9824 0.9868 0.9879 0.9570 0.9705 SK 0.9859 0.9878 0.9911 0.9915 0.9667 0.9763 TM 0.9585 0.9605 0.9662 0.9672 0.9503 0.9579 Avg. 0.9659 0.9702 0.9752 0.9765 0.9443 0.9591 High intensity selection focuses on a local optimum of high quality instead of hunting for hard-to-find global optimum Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 11 / 15

  74. Introduction Regression Testing Empirical Study Conclusion Results: Selection Intensity Name ROUE ROUL TRU60 TRU40 TOU2 TOU5 DS 0.9742 0.9837 0.9893 0.9915 0.9514 0.9706 GB 0.9500 0.9572 0.9668 0.9700 0.9062 0.9402 JD 0.9247 0.9328 0.9431 0.9451 0.8993 0.9192 LF 0.9903 0.9903 0.9903 0.9903 0.9903 0.9903 RM 0.9665 0.9670 0.9681 0.9682 0.9328 0.9475 RP 0.9774 0.9824 0.9868 0.9879 0.9570 0.9705 SK 0.9859 0.9878 0.9911 0.9915 0.9667 0.9763 TM 0.9585 0.9605 0.9662 0.9672 0.9503 0.9579 Avg. 0.9659 0.9702 0.9752 0.9765 0.9443 0.9591 One Explanation: the fitness landscape for coverage effectiveness contains many local optima that are good test orderings Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 11 / 15

  75. Introduction Regression Testing Empirical Study Conclusion Results: Comparison to Random GB 1.0 Coverage Effectiveness (CE) 0.8 0.6 0.4 0.2 0.0 50 500 5000 Number of Random Prioritizations GB: Random orderings have average CE scores around 0.6 Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 12 / 15

  76. Introduction Regression Testing Empirical Study Conclusion Results: Comparison to Random SK 1.0 Coverage Effectiveness (CE) 0.8 0.6 0.4 0.2 50 500 5000 Number of Random Prioritizations SK: Random orderings have average CE scores around 0.7 Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 12 / 15

  77. Introduction Regression Testing Empirical Study Conclusion Results: Comparison to Random SK 1.0 Coverage Effectiveness (CE) 0.8 0.6 0.4 0.2 50 500 5000 Number of Random Prioritizations Conclusion: Random is not as effective as the genetic algorithm Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 12 / 15

  78. Introduction Regression Testing Empirical Study Conclusion Results: Comparison to Hill Climbing HC−FA−FN 1.0 Coverage Effectiveness (CE) 0.8 0.6 0.4 0.2 DS GB JD LF RM RP SK TM Case Study Application First Ascent: Across all applications, average CE score below 0.8 Empirically Studying the Role of Selection Operators During Search-Based Test Suite Prioritization 13 / 15

Recommend

Alexander Ostrovsky (1823-1886) 1 1 Alexander Ostrovsky , Wikipedia ,

Alexander Ostrovsky (1823-1886) 1 1 Alexander Ostrovsky , Wikipedia ,

Alexander Ostrovsky (1823-1886) 1 1 Alexander Ostrovsky , Wikipedia , http://en.wikipedia.org/wiki/File:Wassilij_Grigorjewitsch_Perow_003.jpg 110629 Bibliotheca Alexandrina Compiled by: Ahmed Ghazi, Basma

239 views • 8 slides
ALEXANDER MILES INVENTED THE ELEVATOR Kalaya S Alexander Miles was born in Duluth, MN.

ALEXANDER MILES INVENTED THE ELEVATOR Kalaya S Alexander Miles was born in Duluth, MN.

ALEXANDER MILES INVENTED THE ELEVATOR Kalaya S Alexander Miles was born in Duluth, MN. He got married and his wife's name was Mrs.Candace and they had a kid named Grace. Alexander Miles was born May 18,1838. He died May 7, 1918

357 views • 9 slides
Hostes conjecture and roots of the Alexander polynomial Alexander Stoimenow Department of

Hostes conjecture and roots of the Alexander polynomial Alexander Stoimenow Department of

Hostes conjecture and roots of the Alexander polynomial Alexander Stoimenow Department of Mathematics, Keimyung University, Daegu, Korea May 22, 2012 Topology Seminar Pusan National

668 views • 28 slides
ALEXANDER CALDER 3 SOURCE: http://www.smith-davidson.com/artists/alexander-calder ALEXANDER

ALEXANDER CALDER 3 SOURCE: http://www.smith-davidson.com/artists/alexander-calder ALEXANDER

ALEXANDER CALDER 3 SOURCE: http://www.smith-davidson.com/artists/alexander-calder ALEXANDER CALDER 3 SOURCE: http://creartart.blogspot.com.es/2013_05_01_archive.html ALEXANDER CALDER 3 SOURCE:

244 views • 13 slides
Protected Areas, Law &amp; Governance Alexander Paterson Alexander.Paterson@uct.ac.za Before we

Protected Areas, Law &amp; Governance Alexander Paterson Alexander.Paterson@uct.ac.za Before we

Protected Areas, Law &amp; Governance Alexander Paterson Alexander.Paterson@uct.ac.za Before we begin Complexity in 60 Minutes! Some Context IUCN ELC PA Law Programme Phase 1 Phase 2 2013 2011 2013-2015 Legal Guidance Capacity Building An

748 views • 44 slides
Literature Review Alexander Radovic College of William and Mary Alexander Radovic 1

Literature Review Alexander Radovic College of William and Mary Alexander Radovic 1

Literature Review Alexander Radovic College of William and Mary Alexander Radovic 1 Where to start? You dont need a formal education in ML to use its tools. But it doesnt hurt to work through a online textbook or course. Here are

593 views • 14 slides
Deterministic Fast User Space Synchronization Alexander Zpke alexander.zuepke@hs-rm.de

Deterministic Fast User Space Synchronization Alexander Zpke alexander.zuepke@hs-rm.de

Deterministic Fast User Space Synchronization Alexander Zpke alexander.zuepke@hs-rm.de RheinMain University of Applied Sciences Wiesbaden, Germany OSPERT A. Zpke Overview 2013-07-09 2 / 42 Futex Basics Challenge: Futexes for

684 views • 42 slides
STRATEGIC ACQUISITION OF ALEXANDER DENNIS Alexander Dennis is a Scotland headquartered global

STRATEGIC ACQUISITION OF ALEXANDER DENNIS Alexander Dennis is a Scotland headquartered global

STRATEGIC ACQUISITION OF ALEXANDER DENNIS Alexander Dennis is a Scotland headquartered global leader of lightweight single and double deck buses and motor coaches May 28, 2019 Company Overview Business Description ADL is a leading global

413 views • 20 slides
Alexander Duyck Open Source Technologist Intel Corporation Alexander.Duyck@gmail.com Agenda

Alexander Duyck Open Source Technologist Intel Corporation Alexander.Duyck@gmail.com Agenda

Alexander Duyck Open Source Technologist Intel Corporation Alexander.Duyck@gmail.com Agenda Alexander Duyck Implementing Page Based Receive Paths with Page Reuse DMA API Changes to Enable Better Use of build_skb and XDP Jesper

529 views • 10 slides
with Layer-by-Layer Control of the Coverage and Composition Denis Alikin 1 , Alexander Abramov 1 ,

with Layer-by-Layer Control of the Coverage and Composition Denis Alikin 1 , Alexander Abramov 1 ,

Chemical Solution Deposition of BiFeO 3 Films with Layer-by-Layer Control of the Coverage and Composition Denis Alikin 1 , Alexander Abramov 1 , Alexander Sobol, Vladislav Slabov, Lev Trusov, Violetta Safina, Alexander Vasiliev, Vladimir Shur,

137 views • 10 slides
Drawing Graphs on Few Circles and Few Spheres Alexander Ravsky Alexander Wolff Myroslav Kryven

Drawing Graphs on Few Circles and Few Spheres Alexander Ravsky Alexander Wolff Myroslav Kryven

Drawing Graphs on Few Circles and Few Spheres Alexander Ravsky Alexander Wolff Myroslav Kryven Julius-Maximilians-Universit at W urzburg, Germany Pidstryhach Institute for Applied Problems of Mechanics and Mathematics, National Academy

930 views • 63 slides
Alexander Kanevskiy OpenIoT Summit Europe 2016-10-12 Who am I? Alexander.Kanevskiy@Intel.com 2

Alexander Kanevskiy OpenIoT Summit Europe 2016-10-12 Who am I? Alexander.Kanevskiy@Intel.com 2

Alexander Kanevskiy OpenIoT Summit Europe 2016-10-12 Who am I? Alexander.Kanevskiy@Intel.com 2 Agenda Yocto-based distributions: platforms and products Continuous Integration and Continuous Delivery 101 Source Code Management Tools

411 views • 37 slides
Caosdb Alexander Schlemmer (MPIDS, G ottingen) CaosDB 1 / 25 CaosDB Alexander Schlemmer

Caosdb Alexander Schlemmer (MPIDS, G ottingen) CaosDB 1 / 25 CaosDB Alexander Schlemmer

Evaluation of the semantic research data management system CaosDB in glaciology Alexander Schlemmer 1,* , Henrik tom W orden 1,* , Johannes Freitag 2 , Timm Fitschen 1,* , Johanna Kerch 2 , York Schlomann 2 , Daniel Hornung 1,* , Ilka Weikusat 2

575 views • 23 slides
EDGE: Extreme-scale Discontinuous Galerkin Environment Alexander Breuer, Alexander Heinecke

EDGE: Extreme-scale Discontinuous Galerkin Environment Alexander Breuer, Alexander Heinecke

EDGE: Extreme-scale Discontinuous Galerkin Environment Alexander Breuer, Alexander Heinecke (Intel), Yifeng Cui Getting Started: Advection Equation q ( x, t ) t + v q ( x, t ) x = 0 , v R Simplest hyperbolic Partial

447 views • 16 slides
Presence-Condition Simplification: Problem, Solutions, Applications Alexander von Rhein,

Presence-Condition Simplification: Problem, Solutions, Applications Alexander von Rhein,

Presence-Condition Simplification: Problem, Solutions, Applications Alexander von Rhein, Alexander Grebhahn, Sven Apel, Norbert Siegmund, Dirk Beyer, and Thorsten Berger University of Passau, Germany Alexander von Rhein Presence-Condition

384 views • 12 slides
Tracking efficiency at the LHCb experiment Alexander Shires IC HEP 1 st Year PhD Alexander

Tracking efficiency at the LHCb experiment Alexander Shires IC HEP 1 st Year PhD Alexander

Tracking efficiency at the LHCb experiment Alexander Shires IC HEP 1 st Year PhD Alexander Shires - IC HEP First Year 02/03/2010 1 PhD Talk Outline LHCb Physics overview The LHCb detector Vertex locator RICH Trigger

311 views • 15 slides
About the Stephanie Alexander Kitchen Garden Foundation (Who we are) Stephanie Alexander piloted

About the Stephanie Alexander Kitchen Garden Foundation (Who we are) Stephanie Alexander piloted

About the Stephanie Alexander Kitchen Garden Foundation (Who we are) Stephanie Alexander piloted the first Kitchen Garden Program at Collingwood College in 2001. The Foundation was established in 2004 to engage more schools and support them to

255 views • 3 slides
Palau Palau Dwight G. Alexander Dwight G. Alexander Pacific World Heritage Workshop Apia,

Palau Palau Dwight G. Alexander Dwight G. Alexander Pacific World Heritage Workshop Apia,

Palau Palau Dwight G. Alexander Dwight G. Alexander Pacific World Heritage Workshop Apia, Samoa 5 -9 September 2011 GOVERNMENT OF SAMOA General Information 1. National Policy &amp; Legislation a. Title 19, Historic Preservation Act b.

795 views • 7 slides
maze Alexander Brown (aab2212) Alexander Freemantle (asf2161) Michelle Navarro (mn2614) Lindsay

maze Alexander Brown (aab2212) Alexander Freemantle (asf2161) Michelle Navarro (mn2614) Lindsay

maze Alexander Brown (aab2212) Alexander Freemantle (asf2161) Michelle Navarro (mn2614) Lindsay Schiminske (ls3245) Introduction What is maze? Language Evolution Plan: Proposal: Actual: Object-Oriented Java-like language Game design

488 views • 20 slides
Microkernel virtualization under one roof - dare the impossible - Alexander Bttcher &lt;

Microkernel virtualization under one roof - dare the impossible - Alexander Bttcher &lt;

Microkernel virtualization under one roof - dare the impossible - Alexander Bttcher &lt; alexander.boettcher@genode-labs.com &gt; Outline 1. Introduction 2. Kernel interfaces 3. VM interface harmonization 4. VMMs harmonized 5. Conclusion

941 views • 55 slides
inverse MEG-problems Authors: Galchenkova Marina, Demidov Alexander, Kochurov Alexander Plan

inverse MEG-problems Authors: Galchenkova Marina, Demidov Alexander, Kochurov Alexander Plan

A flat approximation of inverse MEG-problems Authors: Galchenkova Marina, Demidov Alexander, Kochurov Alexander Plan What is magnetoencephalography (MEG)? Inverse problem The reason of our interest in this problem Steps of

720 views • 19 slides
Weighted Gate Elimination Alexander Golovnev New York University Alexander S. Kulikov Steklov

Weighted Gate Elimination Alexander Golovnev New York University Alexander S. Kulikov Steklov

Weighted Gate Elimination Alexander Golovnev New York University Alexander S. Kulikov Steklov Institute of Mathematics ITCS 2016 Gate Elimination Lower Bounds for Affine Dispersers Lower Bound for Quadratic Dispersers Open Problems

594 views • 44 slides
Navigating and Editing Prototxts Alexander Radovic College of William and Mary Alexander

Navigating and Editing Prototxts Alexander Radovic College of William and Mary Alexander

Navigating and Editing Prototxts Alexander Radovic College of William and Mary Alexander Radovic Editing Prototxts 1 What are prototxts? A file format a little like an xml file: https://developers.google.com/protocol-buffers/docs/overview

371 views • 24 slides
The Gain of Resource Delegation in Distributed Computing Environments Alexander Flling,

The Gain of Resource Delegation in Distributed Computing Environments Alexander Flling,

technische universitt Robotics Research Institute dortmund The Gain of Resource Delegation in Distributed Computing Environments Alexander Flling, Christian Grimme, Joachim Lepping, and Alexander Papaspyrou 15th Workshop on Job Scheduling

444 views • 22 slides

More recommend