The Impact of Equivalent, Redundant and Quasi Mutants on Database - - PowerPoint PPT Presentation

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The Impact of Equivalent, Redundant and Quasi Mutants on Database - - PowerPoint PPT Presentation

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The Impact of Equivalent, Redundant and Quasi Mutants on Database Schema Mutation Analysis Chris J. Wright Gregory M. Kapfhammer Phil McMinn The Impact of Equivalent, Redundant and Quasi Mutants on Database Schema Mutation Analysis Chris J.

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SLIDE 1

The Impact of Equivalent, Redundant and Quasi Mutants on Database Schema Mutation Analysis

Chris J. Wright Gregory M. Kapfhammer Phil McMinn

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SLIDE 2

The Impact of Equivalent, Redundant and Quasi Mutants on Database Schema Mutation Analysis

Chris J. Wright Gregory M. Kapfhammer Phil McMinn

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SLIDE 3

The Impact of Equivalent, Redundant and Quasi Mutants on Database Schema Mutation Analysis

Chris J. Wright Gregory M. Kapfhammer Phil McMinn

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SLIDE 4

The Impact of Equivalent, Redundant and Quasi Mutants on Database Schema Mutation Analysis

Chris J. Wright Gregory M. Kapfhammer Phil McMinn

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SLIDE 5

The Impact of Equivalent, Redundant and Quasi Mutants on Database Schema Mutation Analysis

Chris J. Wright Gregory M. Kapfhammer Phil McMinn

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SLIDE 6

The Impact of Equivalent, Redundant and Quasi Mutants on Database Schema Mutation Analysis

Chris J. Wright Gregory M. Kapfhammer Phil McMinn

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SLIDE 7
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SLIDE 8

Database Schema

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SLIDE 9

Database Schema

1 CREATE TABLE T ( 2 A CHAR, B CHAR, C CHAR, 3 CONSTRAINT UniqueOnColsAandB UNIQUE (A, B) 4 ); 5 6 CREATE TABLE S ( 7 X CHAR, Y CHAR, Z CHAR, 8 CONSTRAINT RefToColsAandB FOREIGN KEY (X, Y) 9 REFERENCES T (A, B) 10 );

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SLIDE 10

Database Schema

1 CREATE TABLE T ( 2 A CHAR, B CHAR, C CHAR, 3 CONSTRAINT UniqueOnColsAandB UNIQUE (A, B) 4 ); 5 6 CREATE TABLE S ( 7 X CHAR, Y CHAR, Z CHAR, 8 CONSTRAINT RefToColsAandB FOREIGN KEY (X, Y) 9 REFERENCES T (A, B) 10 );

Tables

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SLIDE 11

Database Schema

1 CREATE TABLE T ( 2 A CHAR, B CHAR, C CHAR, 3 CONSTRAINT UniqueOnColsAandB UNIQUE (A, B) 4 ); 5 6 CREATE TABLE S ( 7 X CHAR, Y CHAR, Z CHAR, 8 CONSTRAINT RefToColsAandB FOREIGN KEY (X, Y) 9 REFERENCES T (A, B) 10 );

Columns

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SLIDE 12

Database Schema

1 CREATE TABLE T ( 2 A CHAR, B CHAR, C CHAR, 3 CONSTRAINT UniqueOnColsAandB UNIQUE (A, B) 4 ); 5 6 CREATE TABLE S ( 7 X CHAR, Y CHAR, Z CHAR, 8 CONSTRAINT RefToColsAandB FOREIGN KEY (X, Y) 9 REFERENCES T (A, B) 10 );

Constraints

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SLIDE 13

Why Test a Database Schema?

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SLIDE 14

Why Test a Database Schema?

Database Schema

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SLIDE 15

Why Test a Database Schema?

DBMS Database Schema

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SLIDE 16

Why Test a Database Schema?

DBMS

Database Schema

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SLIDE 17

Why Test a Database Schema?

DBMS Application Web Server Third Party

Database Schema

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SLIDE 18

Why Test a Database Schema?

DBMS Application Web Server Third Party

Database Schema

✗ ✗ ✗ ✗

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SLIDE 19

How to Test a Database Schema

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SLIDE 20

How to Test a Database Schema

  • Generate test data – SQL INSERT statements
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SLIDE 21

How to Test a Database Schema

  • Generate test data – SQL INSERT statements

INSERT INTO T(a, b) VALUES('a', 'b');

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SLIDE 22

How to Test a Database Schema

  • Generate test data – SQL INSERT statements

INSERT INTO T(a, b) VALUES('a', 'b');

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SLIDE 23

How to Test a Database Schema

  • Generate test data – SQL
  • Execute the data against the database

INSERT INTO T(a, b) VALUES('a', 'b');

DBMS

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SLIDE 24

How to Test a Database Schema

  • Generate test data – SQL
  • Execute the data against the database
  • Examine the acceptance of statements

INSERT INTO T(a, b) VALUES('a', 'b');

DBMS

✗

✓/

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SLIDE 25

Mutation Analysis

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SLIDE 26

Mutation Analysis

Application

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SLIDE 27

Mutation Analysis

Application

Mutation Operators

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SLIDE 28

Mutation Analysis

Application Mutants

Mutation Operators

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SLIDE 29

Mutation Analysis

Application Mutants

Mutation Operators Test suite

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SLIDE 30

Mutation Analysis

Application Mutants

Mutation Operators Test suite

Test results

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SLIDE 31

Mutation Analysis

Application Mutants

Mutation Operators Test suite Test suite

Test results

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SLIDE 32

Mutation Analysis

Application Mutants

Mutation Operators Test suite Test suite

Test results Test results

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SLIDE 33

Mutation Analysis

Application Mutants

Mutation Operators Test suite Test suite

Test results Test results

Comparison

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SLIDE 34

Mutation Analysis

Application Mutants

Mutation Operators Test suite Test suite

Test results Test results

Comparison

Mutation Score

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SLIDE 35

Mutation Analysis

Application Mutants

Mutation Operators Test suite Test suite

Test results Test results

Comparison

Mutation Score

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SLIDE 36

Database Schema Mutation Operators

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SLIDE 37

Database Schema Mutation Operators

Primary Key Foreign Key Unique Not Null Check

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SLIDE 38

Database Schema Mutation Operators

Primary Key Foreign Key Unique Not Null Check

×

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SLIDE 39

Database Schema Mutation Operators

Primary Key Foreign Key Unique Not Null Check Column Addition

×

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SLIDE 40

Database Schema Mutation Operators

Primary Key Foreign Key Unique Not Null Check Column Addition Column Removal

×

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SLIDE 41

Database Schema Mutation Operators

Primary Key Foreign Key Unique Not Null Check Column Addition Column Removal Column Exchange

×

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SLIDE 42

Database Schema Mutation Operators

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SLIDE 43

Database Schema Mutation Operators

Primary Key Column Addition

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SLIDE 44

Database Schema Mutation Operators

Primary Key Column Addition

1 CREATE TABLE T ( 2 A CHAR, B CHAR, 3 PRIMARY KEY (A) 4 );

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SLIDE 45

Database Schema Mutation Operators

Primary Key Column Addition

1 CREATE TABLE T ( 2 A CHAR, B CHAR, 3 PRIMARY KEY (A) 4 ); 1 CREATE TABLE T ( 2 A CHAR, B CHAR, 3 PRIMARY KEY (A, B) 4 );

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SLIDE 46

Database Schema Mutation Operators

Primary Key Column Exchange

1 CREATE TABLE T ( 2 A CHAR, B CHAR, 3 PRIMARY KEY (A) 4 ); 1 CREATE TABLE T ( 2 A CHAR, B CHAR, 3 PRIMARY KEY (B) 4 );

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SLIDE 47

Database Schema Mutation Operators

Primary Key Column Removal

1 CREATE TABLE T ( 2 A CHAR, B CHAR, 3 PRIMARY KEY (A) 4 ); 1 CREATE TABLE T ( 2 A CHAR, B CHAR, 3 4 );

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SLIDE 48

Mutation Analysis – Challenges

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SLIDE 49

Mutation Analysis – Challenges

  • Special classes of mutants
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SLIDE 50

Mutation Analysis – Challenges

  • Special classes of mutants
  • Equivalent
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SLIDE 51

Mutation Analysis – Challenges

  • Special classes of mutants
  • Equivalent
  • Redundant
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SLIDE 52

Mutation Analysis – Challenges

  • Special classes of mutants
  • Equivalent
  • Redundant
  • Quasi-mutants
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SLIDE 53

Mutation Analysis – Challenges

  • Special classes of mutants
  • Equivalent
  • Redundant
  • Quasi-mutants
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SLIDE 54

Equivalent Mutants

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SLIDE 55

Equivalent Mutants

  • Functionally identical to non-mutant
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SLIDE 56

Equivalent Mutants

  • Functionally identical to non-mutant
  • …but syntactically different
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SLIDE 57

Equivalent Mutants

  • Functionally identical to non-mutant
  • …but syntactically different
  • Cannot be ‘killed’
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SLIDE 58

Equivalent Mutants

  • Functionally identical to non-mutant
  • …but syntactically different
  • Cannot be ‘killed’
  • Artificially decrease mutation score
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SLIDE 59

Equivalent Mutants

  • Functionally identical to non-mutant
  • …but syntactically different
  • Cannot be ‘killed’
  • Artificially decrease mutation score
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SLIDE 60

Equivalent Mutants

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SLIDE 61

Equivalent Mutants

1 CREATE TABLE T ( 2 A CHAR, 3 PRIMARY KEY (A) 4 );

Original:

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SLIDE 62

Equivalent Mutants

1 CREATE TABLE T ( 2 A CHAR, 3 PRIMARY KEY (A) 4 ); 1 CREATE TABLE T ( 2 A CHAR NOT NULL, 3 PRIMARY KEY (A) 4 );

Original: Mutant:

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SLIDE 63

Redundant Mutants

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SLIDE 64

Redundant Mutants

  • Functionally identical to another mutant
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SLIDE 65

Redundant Mutants

  • Functionally identical to
  • …but syntactically different
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SLIDE 66

Redundant Mutants

  • Functionally identical to
  • …but syntactically different
  • May be ‘killed’
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SLIDE 67

Redundant Mutants

  • Functionally identical to
  • …but syntactically different
  • May be ‘killed’
  • Artificially alters mutation score
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SLIDE 68

Redundant Mutants

  • Functionally identical to
  • …but syntactically different
  • May be ‘killed’
  • Artificially alters mutation score
  • Reduces efficiency
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SLIDE 69

Redundant Mutants

  • Functionally identical to
  • …but syntactically different
  • May be ‘killed’
  • Artificially alters mutation score
  • Reduces efficiency
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SLIDE 70

Types of Equivalence

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SLIDE 71

Types of Equivalence

  • Structural
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SLIDE 72

Types of Equivalence

  • Structural
  • Functionally irrelevant syntactic differences
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SLIDE 73

Types of Equivalence

  • Structural
  • Functionally irrelevant syntactic differences
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SLIDE 74

Types of Equivalence

  • Structural
  • Functionally irrelevant syntactic differences
  • Behavioural
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SLIDE 75

Types of Equivalence

  • Structural
  • Functionally irrelevant syntactic differences
  • Behavioural
  • Overlap within SQL features
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SLIDE 76

Types of Equivalence

  • Structural
  • Functionally irrelevant syntactic differences
  • Behavioural
  • Overlap within SQL features
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SLIDE 77

Behavioural Equivalence Patterns

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SLIDE 78

Behavioural Equivalence Patterns

  • NOT NULL in CHECK constraints
  • NOT NULL ≅ CHECK(… IS NOT NULL)
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SLIDE 79

Behavioural Equivalence Patterns

  • NOT NULL in CHECK constraints
  • NOT NULL ≅ CHECK(… IS NOT NULL)

1 CREATE TABLE T ( 2 A CHAR NOT NULL, 3 ); 1 CREATE TABLE T ( 2 A CHAR, 3 CHECK(A IS NOT NULL) 4 );

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SLIDE 80

Behavioural Equivalence Patterns

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SLIDE 81

Behavioural Equivalence Patterns

  • NOT NULL on PRIMARY KEY columns
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SLIDE 82

Behavioural Equivalence Patterns

  • NOT NULL on PRIMARY KEY columns
  • Implicit NOT NULL on PRIMARY KEY
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SLIDE 83

Behavioural Equivalence Patterns

  • NOT NULL on PRIMARY KEY columns
  • Implicit NOT NULL on PRIMARY KEY
  • (Only PostgreSQL and HyperSQL)
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SLIDE 84

Behavioural Equivalence Patterns

  • NOT NULL on PRIMARY KEY columns
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SLIDE 85

Behavioural Equivalence Patterns

  • NOT NULL on PRIMARY KEY columns

1 CREATE TABLE T ( 2 A CHAR, 3 PRIMARY KEY (A) 4 ); 1 CREATE TABLE T ( 2 A CHAR NOT NULL, 3 PRIMARY KEY (A) 4 );

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SLIDE 86

Behavioural Equivalence Patterns

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SLIDE 87

Behavioural Equivalence Patterns

  • UNIQUE and PRIMARY KEY with shared

columns

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SLIDE 88

Behavioural Equivalence Patterns

  • UNIQUE and PRIMARY KEY with shared

columns

1 CREATE TABLE T ( 2 A CHAR, 3 PRIMARY KEY (A) 4 ); 1 CREATE TABLE T ( 2 A CHAR, 3 PRIMARY KEY (A), 4 UNIQUE (A) 5 );

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SLIDE 89

Quasi-mutants

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SLIDE 90

Quasi-mutants

  • Operators produce DBMS-agnostic mutants
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SLIDE 91

Quasi-mutants

  • Operators produce DBMS-agnostic mutants
  • Some DBMSs have implicit constraints
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SLIDE 92

Quasi-mutants

  • Operators produce DBMS-agnostic mutants
  • Some DBMSs have implicit constraints
  • Valid for some DBMSs, invalid for others

HyperSQL PostgreSQL SQLite✓

✗ ✗

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SLIDE 93

Quasi-mutants

  • Operators produce DBMS-agnostic mutants
  • Some DBMSs have implicit constraints
  • Valid for some DBMSs, invalid for others
  • HyperSQL

PostgreSQL SQLite✓

✗ ✗

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SLIDE 94

Quasi-mutants

SQLite✓ PostgreSQL

✗

HyperSQL✗

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SLIDE 95

Quasi-mutants

  • Cannot adversely affect mutation score

SQLite✓ PostgreSQL

✗

HyperSQL✗

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SLIDE 96

Quasi-mutants

  • Cannot adversely affect mutation score
  • …but may preclude some optimisations

SQLite✓ PostgreSQL

✗

HyperSQL✗

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SLIDE 97

Quasi-mutants

  • Cannot adversely affect mutation score
  • …but may preclude some optimisations
  • Remove when DBMS will ‘reject’ them

SQLite✓ PostgreSQL

✗

HyperSQL✗

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SLIDE 98

Types of Quasi-mutants

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SLIDE 99

Types of Quasi-mutants

  • Representative example
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SLIDE 100

Types of Quasi-mutants

  • Representative example
  • DBMS: PostgreSQL, HyperSQL
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SLIDE 101

Types of Quasi-mutants

  • Representative example
  • DBMS: PostgreSQL, HyperSQL
  • ∀ FK(reference columns) ∃ 


(PK(reference columns) ∨ 
 Unique(reference columns))

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SLIDE 102

Types of Quasi-mutants

  • Representative example
  • DBMS: PostgreSQL, HyperSQL
  • ∀ FK(reference columns) ∃ 


(PK(reference columns) ∨ 
 Unique(reference columns))

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SLIDE 103

Types of Quasi-mutants

  • Representative example
  • DBMS: PostgreSQL, HyperSQL
  • ∀ FK(reference columns) ∃ 


(PK(reference columns) ∨ 
 Unique(reference columns))

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SLIDE 104

Types of Quasi-mutants

  • Representative example
  • DBMS: PostgreSQL, HyperSQL
  • ∀ FK(reference columns) ∃ 


(PK(reference columns) ∨ 
 Unique(reference columns))

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SLIDE 105

Types of Quasi-mutants

  • Representative example
  • DBMS: PostgreSQL, HyperSQL
  • ∀ FK(reference columns) ∃ 


(PK(reference columns) ∨ 
 Unique(reference columns))

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SLIDE 106

Types of Quasi-mutants

  • Representative example
  • DBMS: PostgreSQL, HyperSQL
  • ∀

(PK(reference columns) Unique(reference

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SLIDE 107

Detecting Quasi-mutants

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SLIDE 108

Detecting Quasi-mutants

  • Submit to DBMS
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SLIDE 109

Detecting Quasi-mutants

  • Submit to DBMS
  • 100% accurate
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SLIDE 110

Detecting Quasi-mutants

  • Submit to DBMS
  • 100% accurate
  • Convert representation to SQL, submit to database,

inspect response

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SLIDE 111

Detecting Quasi-mutants

  • Submit to DBMS
  • 100% accurate
  • Convert representation to SQL, submit to database,

inspect response

  • Analyse statically
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SLIDE 112

Detecting Quasi-mutants

  • Submit to DBMS
  • 100% accurate
  • Convert representation to SQL, submit to database,

inspect response

  • Analyse statically
  • Operates directly on representation
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SLIDE 113

Detecting Quasi-mutants

  • Submit to DBMS
  • 100% accurate
  • Convert representation to SQL, submit to database,

inspect response

  • Analyse statically
  • Operates directly on representation
  • DBMS-specific implementation
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SLIDE 114

Empirical Study

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SLIDE 115

Empirical Study

  • 1. Quasi-mutant detection – DBMS v Static Analysis
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SLIDE 116

Empirical Study

  • 1. Quasi-mutant detection – DBMS v Static Analysis
  • 2. Equivalent, Redundant and Quasi-mutant

removal – Efficiency?

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SLIDE 117

Empirical Study

  • 1. Quasi-mutant detection – DBMS v Static Analysis
  • 2. Equivalent, Redundant and Quasi-mutant

removal – Efficiency?

  • 3. Equivalent, Redundant and Quasi-mutant

removal – Effectiveness?

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SLIDE 118

Empirical Study

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SLIDE 119

Empirical Study

  • 16 schemas
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SLIDE 120

Empirical Study

  • 16 schemas
  • 2 DBMSs – PostgreSQL, HyperSQL
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SLIDE 121

Empirical Study

  • 16 schemas
  • 2 DBMSs – PostgreSQL, HyperSQL
  • 15 repeat trials
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SLIDE 122

Empirical Study

  • 16 schemas
  • 2 DBMSs – PostgreSQL, HyperSQL
  • 15 repeat trials
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SLIDE 123

Empirical Study – Quasi-mutants

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SLIDE 124

Empirical Study – Quasi-mutants

  • 5 conditions:
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SLIDE 125

Empirical Study – Quasi-mutants

  • 5 conditions:
  • Postgres (with/without transactions)
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SLIDE 126

Empirical Study – Quasi-mutants

  • 5 conditions:
  • Postgres (with/without transactions)
  • HyperSQL (with/without transactions)
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SLIDE 127

Empirical Study – Quasi-mutants

  • 5 conditions:
  • Postgres (with/without transactions)
  • HyperSQL (with/without transactions)
  • Static analysis
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SLIDE 128

Empirical Study – Quasi-mutants

  • 5 conditions:
  • Postgres (with/without transactions)
  • HyperSQL (with/without transactions)
  • Static analysis
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SLIDE 129

Empirical Study – Quasi-mutants

  • 0.00

0.25 0.50 0.75 1.00 1.25 HyperSQL HyperSQL Trans. Postgres Postgres Trans. Static

Approach Scaled Time Taken

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SLIDE 130

Empirical Study – Mutant Removal

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SLIDE 131

Empirical Study – Mutant Removal

  • 2 conditions – with and without removal
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SLIDE 132

Empirical Study – Mutant Removal

  • 2 conditions – with and without removal
  • 2 metrics –
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SLIDE 133

Empirical Study – Mutant Removal

  • 2 conditions – with and without removal
  • 2 metrics –
  • Time taken for mutation analysis
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SLIDE 134

Empirical Study – Mutant Removal

  • 2 conditions – with and without removal
  • 2 metrics –
  • Time taken for mutation analysis
  • Mutation score
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SLIDE 135

Empirical Study – Mutant Removal

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SLIDE 136
  • HyperSQL – Time saved
  • Best case: 718ms (23.05%)
  • Worst case: -824ms (-9.71%)

Empirical Study – Mutant Removal

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SLIDE 137

Empirical Study – Mutant Removal

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SLIDE 138
  • HyperSQL – Time saved

Empirical Study – Mutant Removal

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SLIDE 139
  • HyperSQL – Time saved
  • 9/16 mean time decrease (p < 0.05)

Empirical Study – Mutant Removal

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SLIDE 140
  • HyperSQL – Time saved
  • 9/16 mean time decrease (
  • 7/16 mean time increase (p < 0.05)

Empirical Study – Mutant Removal

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SLIDE 141
  • HyperSQL – Time saved
  • 9/16 mean time decrease (
  • 7/16 mean time increase (
  • Overall, decrease (1.6% mean, 1.4% median)

Empirical Study – Mutant Removal

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SLIDE 142

Empirical Study – Mutant Removal

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SLIDE 143
  • PostgreSQL – Time saved
  • Best case: 317,208ms (33.71%)
  • Worst case: -3,086ms, (-0.33%)

Empirical Study – Mutant Removal

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SLIDE 144

Empirical Study – Mutant Removal

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SLIDE 145
  • PostgreSQL – Time saved

Empirical Study – Mutant Removal

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SLIDE 146
  • PostgreSQL – Time saved
  • 14/16 mean time decrease (p < 0.05)

Empirical Study – Mutant Removal

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SLIDE 147
  • PostgreSQL – Time saved
  • 14/16 mean time decrease (
  • 2/16 mean time increase (p < 0.05)

Empirical Study – Mutant Removal

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SLIDE 148
  • PostgreSQL – Time saved
  • 14/16 mean time decrease (
  • 2/16 mean time increase (
  • Overall, decrease (12.7% mean, 11.8% median)

Empirical Study – Mutant Removal

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SLIDE 149

Empirical Study – Mutant Removal

DBMS Time saved (ms) Median Mean

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SLIDE 150

Empirical Study – Mutant Removal

DBMS Time saved (ms) Median Mean HyperSQL 36.2 7.5

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SLIDE 151

Empirical Study – Mutant Removal

DBMS Time saved (ms) Median Mean HyperSQL 36.2 7.5 Postgres 8,071 50,880

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SLIDE 152

Empirical Study – Mutant Removal

DBMS Time saved (ms) Median Mean HyperSQL 36.2 7.5 Postgres 8,071 50,880 Both 229.9 25,450

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SLIDE 153

Empirical Study – Mutant Removal

DBMS Time saved (%) Median Mean HyperSQL 1.4 1.6 Postgres 12.7 11.8 Both 4.7 6.7

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SLIDE 154

Empirical Study – Mutant Removal

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SLIDE 155
  • HyperSQL – Mutation score
  • 75% Increased
  • 44% Adequate
  • 25% No change

Empirical Study – Mutant Removal

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SLIDE 156
  • PostgreSQL – Mutation score
  • 75% Increased
  • 44% Adequate
  • 25% No change

Empirical Study – Mutant Removal

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SLIDE 157

Empirical Study – Mutant Removal

DBMS Scores changed (%) Increased (adequate) No change HyperSQL 75 (44) 25 Postgres 75 (44) 25 Both 75 (44) 25

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SLIDE 158

Conclusion

  • 1. Quasi-mutant detection – DBMS v Static Analysis
  • 2. Equivalent, Redundant and Quasi-mutant

removal – Efficiency?

  • 3. Equivalent, Redundant and Quasi-mutant

removal – Effectiveness?

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SLIDE 159

Conclusion

  • 1. Quasi-mutant detection – DBMS v Static Analysis
  • 2. Equivalent, Redundant and Quasi-mutant

removal – Efficiency?

  • 3. Equivalent, Redundant and Quasi-mutant

removal – Effectiveness?

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SLIDE 160

Conclusion

  • 1. Quasi-mutant detection – DBMS v Static Analysis
  • 2. Equivalent, Redundant and Quasi-mutant

removal – Efficiency?

  • 3. Equivalent, Redundant and Quasi-mutant

removal – Effectiveness?

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SLIDE 161

Conclusion

  • 1. Quasi-mutant detection – Improved efficiency
  • 2. Equivalent, Redundant and Quasi-mutant

removal – Improved efficiency

  • 3. Equivalent, Redundant and Quasi-mutant

removal – Improved effectiveness

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SLIDE 162

Conclusion

  • 1. Quasi-mutant detection – Improved efficiency
  • 2. Equivalent, Redundant and Quasi-mutant

removal – Improved efficiency

  • 3. Equivalent, Redundant and Quasi-mutant

removal – Improved effectiveness

Chris J. Wright – aca08cw@sheffield.ac.uk