- Page 1 and 2: Search-Based Testing Phil McMinn Un
- Page 3 and 4: Acknowledgements The material in so
- Page 5 and 6: Conventional Testing manual design
- Page 7 and 8: Conventional Testing manual design
- Page 9 and 10: Random Test Data Generation Input
- Page 11 and 12: Random Test Data Generation Input
- Page 13 and 14: Search-Based Testing is an automate
- Page 15 and 16: Fitness Function The fitness functi
- Page 17 and 18: Fitness-guided search Fitness Input
- Page 19 and 20: Fitness-guided search Fitness Input
- Page 21 and 22: Publications since 1976 source: SEB
- Page 23 and 24: International Symposium on Search-B
- Page 25 and 26: Fitness Functions Often easy We oft
- Page 27 and 28: J. Wegener and M. Grochtmann. Verif
- Page 29 and 30: Conventional testing manual design
- Page 31 and 32: Generating vs Checking Conventional
- Page 33: Daimler Autonomous Parking System I
- Page 37 and 38: Generation 0 Generation 10 critical
- Page 39 and 40: Test setup Usual approach to testin
- Page 41 and 42: O Bühler and J Wegener: Evolutiona
- Page 43 and 44: Structural testing fitness function
- Page 45 and 46: Assertion testing
- Page 47 and 48: Assertion testing assertion conditi
- Page 49 and 50: Bogdan Korel, Ali M. Al-Yami: Asser
- Page 51 and 52: Hill Climbing Fitness Input
- Page 53 and 54: Hill Climbing Fitness Input
- Page 55 and 56: Hill Climbing Fitness No better sol
- Page 57 and 58: Hill Climbing - Restarts Fitness In
- Page 59 and 60: Hill Climbing - Restarts Fitness In
- Page 61 and 62: Hill Climbing - Restarts Fitness In
- Page 63 and 64: Hill Climbing - Restarts Fitness In
- Page 65 and 66: Simulated Annealing Fitness Input
- Page 67 and 68: Simulated Annealing Fitness Input
- Page 69 and 70: Simulated Annealing Fitness Input
- Page 71 and 72: Evolutionary Algorithm Fitness Inpu
- Page 73 and 74: Evolutionary Algorithm Fitness Inpu
- Page 75 and 76: Crossover a b c d 10 10 20 40 a b c
- Page 77 and 78: Crossover a b c d 10 10 20 40 a b c
- Page 79 and 80: Crossover a b c d 10 10 20 40 a 10
- Page 81 and 82: Mutation a b c d d 10 10 20 40 20
- Page 83 and 84: Mutation a b c d d 20 10 10 20 40 2
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Evolutionary Testing Insertion Muta
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Which search method Depends on char
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Which search method Depends on char
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Which search method Depends on char
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Which search method Some landscapes
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Which search method Some landscapes
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Ingredients for Search-Based Testin
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Ingredients for Search-Based Testin
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More search algorithms
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SBST Surveys & Reviews Phil McMinn:
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More applications of Search-Based T
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Mutation Testing mutant original mu
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Mutation Testing mutant higher orde
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Finding Good HOMs Due to the large
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Finding Good HOMs Due to the large
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Time aware test suite prioritisatio
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Time aware test suite prioritisatio
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Time aware test suite prioritisatio
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Time aware test suite prioritisatio
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Results Mutations used to seed faul
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Multi-objective Search Fitness Func
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Multi-objective Search Fitness Func
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Multi-objective Search Fitness Func
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Multi-objective Search Fitness Func
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Multi-objective Search Fitness Func
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Multi-objective Search Fitness Func
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Multi-objective Search Fitness Func
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Multi-objective Search Fitness Func
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Some Results Pareto Efficient Multi
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Other applications of Search-Based
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Search-Based Structural Test Data G
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Fitness evaluation TARGET
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Analysing control flow TARGET
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Approach Level TARGET
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Approach Level = 2 = 1 TARGET
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Approach Level = 2 = 1 minimisation
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Analysing predicates Approach level
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Branch distance Associate a distanc
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Putting it all together Fitness = a
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Putting it all together Fitness = a
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Normalisation Functions Since the
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Alternating Variable Method ‘Prob
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Alternating Variable Method ‘Prob
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Alternating Variable Method Acceler
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Alternating Variable Method Acceler
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Alternating Variable Method Acceler
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Alternating Variable Method Acceler
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Alternating Variable Method 1. Rand
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Alternating Variable Method 1. Rand
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A search-based test data generator
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IGUANA (Java) Test object (C code c
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IGUANA (Java) inputs Test object fi
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IGUANA (Java) search algorithm inpu
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Test Object Preparation 1. Parse th
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Test Object Preparation 3. Map inpu
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Test Object Preparation 3. Map inpu
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Instrumentation
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The first parameter is the control
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The instrumentation tells us: Which
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Testability Transformation
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Program Transformation
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Programs will inevitably have featu
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Programs will inevitably have featu
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Testability Transformation
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Testability Transformation Note tha
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Nesting & Local Optima
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Nesting & Local Optima
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Dependent & Independent Predicates
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100 Change in success rate after ap
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When not preserving program equival
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we are testing to cover structure .
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Input Domain Reduction
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Mark Harman, Youssef Hassoun, Kiran
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Effect of Reduction -100,000 ... 10
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Effect of Reduction -100,000 ... 10
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Variable Dependency Analysis
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Variable Dependency Analysis
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Empirical Study Studied the effects
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Effect on Random Testing -49 ... 50
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Results with AVM
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Effect on AVM Saves probe moves (an
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Results with ET
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Which search algorithm
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Interesting branches 8 20 9 Alterna
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Wins for the AVM 100,000 Avergage n
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Wins for Evolutionary Testing 100%
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When does ET win Evolutionary algor
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Schemata Subsets of useful genes e.
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Royal Roads landscape structures wh
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When Crossover Helps
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When Crossover Helps
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Headless Chicken Test T. Jones, “
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Evolutionary Testing Schemata {(a,
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Crossover of good schemata subschem
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Crossover of good schemata {(a, b,
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What types of program and program s
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Progressive Landscape fitness input
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Crossover - Conclusions 1. Large nu
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Other Theoretical Work A. Arcuri, P
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The Oracle Problem Determining the
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Test data generation and human orac
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Test data generation and human orac
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Test data generation and human orac
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Calendar program Some example dates
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In what ways can operational profil
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In what ways can operational profil
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Sanitisation Routines Defensive pro
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Test Data Re-use
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Will these techniques work Will the
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Questions & Discussion