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Conventional testing manual design of test cases / scenarios laborious, time-consuming tedious difficult! (where are the faults ) Search-Based Testing: Search-Based Testing: automatic - may sometimes be time consuming, but it is not a human’s time being consumed a good fitness function will lead the search to the faults
Generating vs Checking Conventional Software Testing Research Write a method to construct test cases Search-Based Testing Write a method to determine how good a test case is
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: Conventional testing manual design
Page 33 and 34: Daimler Autonomous Parking System I
Page 36 and 37: Generation 0
Page 38 and 39: Generation 0 Generation 10 critical
Page 40 and 41: Test setup Search-Based Testing app
Page 42 and 43: Structural testing
Page 44 and 45: Structural testing fitness function
Page 46 and 47: Assertion testing assertion conditi
Page 48 and 49: Assertion testing assertion conditi
Page 50 and 51: Search Techniques
Page 52 and 53: Hill Climbing Fitness Input
Page 54 and 55: Hill Climbing Fitness Input
Page 56 and 57: Hill Climbing - Restarts Fitness In
Page 64 and 65: Simulated Annealing Fitness Input
Page 70 and 71: Simulated Annealing Fitness Worse s
Page 72 and 73: Evolutionary Algorithm Fitness Inpu
Page 74 and 75: Evolutionary Algorithms inspired by
Page 76 and 77: Crossover a b c d 10 10 20 40 a b c
Page 78 and 79: Crossover a b c d 10 10 20 40 a 10
Page 80 and 81:
Mutation a b c 10 10 20 d 40
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Mutation a b c d d 10 10 20 40 20
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Evolutionary Testing
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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 Some landscapes
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Which search method Some landscapes
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Ingredients for an optimising searc
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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 Tabu Search
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Getting started in SBSE M. Harman a
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Mutation Testing original
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Mutation Testing mutant mutant
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Mutation Testing mutant higher orde
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Finding Good HOMs Due to the large
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Subtle Mutants - Fitness Function r
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Time aware test suite prioritisatio
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Fitness Function The tester is unli
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Multi-objective Search Instead of c
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Multi-objective Search Fitness Func
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Three objectives
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Other applications of Search-Based
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Covering a structure TARGET
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Fitness evaluation The test data ex
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Analysing control flow The outcomes
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Approach Level = 2 TARGET
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Approach Level = 2 = 1 TARGET = 0
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Approach Level Roy P. Pargas, Mary
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Analysing predicates Approach level
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Branch distances for relational pre
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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 1. Rand
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Alternating Variable Method 1. Rand
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Key Publications Alternating variab
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Input Generation Using Automated No
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IGUANA (Java) inputs Test object (C
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IGUANA (Java) search algorithm inpu
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A function for testing
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Test Object Preparation 2. Instrume
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Test Object Preparation 3. Map inpu
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Instrumentation
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Each branching condition is replace
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Relational predicates are replaced
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1nput: NODE T F 1 0 1 2 10 0 4 20
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The ‘Flag’ Problem
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Program Transformation
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Programs will inevitably have featu
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Nesting Phil McMinn, David Binkley
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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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Results - Industrial & Open source
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Dependent & Independent Predicates
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Independent and some dependent pred
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we are testing to cover structure .
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we are testing to cover structure .
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Mark Harman, Youssef Hassoun, Kiran
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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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Effect on Random Testing -49 ... 50
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Results with Random Testing
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Effect on AVM Saves probe moves (an
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Effect on ET Saves mutations on irr
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Conclusions for Input Domain Reduct
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Empirical Study Bibclean Defroster
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Wins for the AVM 100 90 80 Success
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When does the AVM win fitness input
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When does ET win The branches in qu
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Schemata Subsets of useful genes e.
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Schemata 1**1 *11* 1111 crossover o
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The Genetic Algorithm Royal Road S1
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When Crossover Helps Executes the t
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When Crossover Helps Executes the t
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Royal Roads Investigations into Cro
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Crossover of good schemata {(a, b,
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Crossover of good schemata building
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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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Crossover - Conclusions 1. Large nu
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Crossover - Conclusions 1. Large nu
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Future directions...
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Human Oracle Cost Typically the res
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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 Takes two dates (r
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Seeding knowledge Programmer test c
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In what ways can operational profil
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Identifier names Give clues to the
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Test Data Re-use
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Program similarity and test data re
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web: http://www.dcs.shef.ac.uk/~phi
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