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Foundations of Software Testing (Fundamental Algorithms and Techniques) by Pearson

Name: Foundations of Software Testing (Fundamental Algorithms and Techniques) by Pearson
Author: Aditya P. Mathur
ISBN: 9781299486720

by Aditya P. Mathur

May 2024

Intermediate to advanced

765 pages

21h 57m

English

Pearson India

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Cover (1/2)
Cover (2/2)
Contents (1/2)
Contents (2/2)
Preface (1/2)
Preface (2/2)
Acknowledgments
Part I: Preliminaries
Chapter 1: Basics of Software Testing
1.1.Humans, Errors, and Testing
1.1.1. Errors, faults, and failures1.1.2. Test automation1.1.3. Developer and tester as two roles

1.2 Software Quality
1.2.1. Quality attributes1.2.2. Reliability
1.3 Requirements, Behavior, and Correctness
1.3.1. Input domain and program correctness1.3.2. Valid and invalid inputs
1.4 Correctness Versus Reliability
1.4.1. Correctness1.4.2. Reliability1.4.3. Program use and the operational profile
1.5 Testing and Debugging (1/2)
1.5.1. Preparing a test plan1.5.2. Constructing test data1.5.3. Executing the program1.5.4. Specifying program behavior
1.5 Testing and Debugging (2/2)
1.5.5. Assessing the correctness of program behavior1.5.6. Construction of oracles
1.6 Test Metrics (1/2)
1.6.1. Organizational metrics1.6.2. Project metrics1.6.3. Process metrics1.6.4. Product metrics: Generic1.6.5. Product metrics: OO software
1.6 Test Metrics (2/2)
1.6.6. Progress monitoring and trends1.6.7. Static and dynamic metrics1.6.8. Testability
1.7 Software and Hardware Testing
1.8 Testing and Verification
1.9 Defect Management
1.10 Execution History
1.11 Test-Generation Strategies
1.12 Static Testing
1.12.1. Walkthroughs1.12.2. Inspections1.12.3. Use of static code analysis tools in static testing1.12.4. Software complexity and static testing
1.13 Model-Based Testing and Model Checking
1.14 Control-Flow Graph (1/2)
1.14.1. Basic block1.14.2. Flow graph: Definition and pictorial representation1.14.3. Path
1.14 Control-Flow Graph (2/2)
1.15 Dominators and Postdominators
1.16 Program-Dependence Graph
1.16.1. Data dependence1.16.2. Control dependence
1.17 Strings, Languages, and Regular Expressions
1.18 Types of Testing (1/3)
1.18.1. Classifier C1: Source of test generation1.18.2. Classifier C2: Life cycle phase
1.18 Types of Testing (2/3)
1.18.3. Classifier C3: Goal-directed testing1.18.4. Classifier C4: Artifact under test
1.18 Types of Testing (3/3)
1.18.5. Classifier C5: Test process models
1.19 The Saturation Effect
1.19.1. Confidence and true reliability1.19.2. Saturation region1.19.3. False sense of confidence1.19.4. Reducing Δ1.19.5. Impact on test process
Summary
Bibliographic Notes
Exercises
Part II: Test Generation
Chapter 2: Test Generation from Requirements
2.1 Introduction
2.2 The Test-Selection Problem
2.3 Equivalence Partitioning (1/5)
2.3.1. Faults targeted2.3.2. Relations and equivalence partitioning
2.3 Equivalence Partitioning (2/5)
2.3.3. Equivalence classes for variables
2.3 Equivalence Partitioning (3/5)
2.3.4. Unidimensional versus multidimensional partitioning2.3.5. A systematic procedure for equivalence partitioning
2.3 Equivalence Partitioning (4/5)
2.3.6. Test selection based on equivalence classes
2.3 Equivalence Partitioning (5/5)
2.3.7. GUI design and equivalence classes
2.4 Boundary-Value Analysis (1/2)
2.4 Boundary-Value Analysis (2/2)
2.5 Category-Partition Method (1/2)
2.5.1 Steps in the category-partition method
2.5 Category-Partition Method (2/2)
2.6 Cause–Effect Graphing (1/4)
2.6.1. Notation used in cause–effect graphing2.6.2. Creating cause–effect graphs
2.6 Cause–Effect Graphing (2/4)
2.6.3. Decision table from cause–effect graph
2.6 Cause–Effect Graphing (3/4)
2.6.4. Heuristics to avoid combinatorial explosion
2.6 Cause–Effect Graphing (4/4)
2.6.5. Test generation from a decision table
2.7 Test Generation from Predicates (1/7)
2.7.1. Predicates and boolean expressions2.7.2. Fault model for predicate testing
2.7 Test Generation from Predicates (2/7)
2.7.2.1 Missing or Extra Boolean Variable Faults2.7.3. Predicate constraints2.7.4. Predicate-testing criteria2.7.5. Generating BOR-, BRO-, and BRE-adequate tests
2.7 Test Generation from Predicates (3/7)
2.7.5 Generating the BOR-constraint setGenerating the BRO-constraint set
2.7 Test Generation from Predicates (4/7)
2.7.5.3 Generating the BRE-constraint set2.7.5.4 Generating BOR Constraints for Nonsingular Expressions
2.7 Test Generation from Predicates (5/7)
2.7.6 Cause–effect Graphs and Predicate Testing
2.7 Test Generation from Predicates (6/7)
2.7.7 Fault Propagation2.7.8 Predicate Testing in Practice2.7.8.1 Specification-based Predicate Test Generation2.7.8.2 Program-based Predicate Test Generation
2.7 Test Generation from Predicates (7/7)
Summary
Bibliographic Notes
Exercises (1/2)
Exercises (2/2)
Chapter 3: Test Generation from Finite-State Models
3.1 Software Design and Testing
3.2 Finite-state Machines (1/2)
3.2.1. Excitation using an input sequence
3.2 Finite-state Machines (2/2)
3.2.2. Tabular representation3.2.3. Properties of FSM
3.3 Conformance Testing
3.3.1. Reset inputs3.3.2. The testing problem
3.4 A Fault Model (1/2)
3.4.1. Mutants of FSMs
3.4 A Fault Model (2/2)
3.4.2. Fault coverage
3.5 Characterization Set (1/2)
3.5.1. Construction of the k-equivalence partitions3.5.2. Deriving the characterization set
3.5 Characterization Set (2/2)
3.5.3. Identification sets
3.6 The W-method (1/2)3.6.1. Assumptions3.6.2. Maximum number of states3.6.3. Computation of the transition cover set3.6.4. Constructing Z3.6.5. Deriving a test set
3.6 The W-method (2/2)
3.6.6. Testing using the W-method3.6.7. The error-detection process
3.7 The Partial W-method (1/2)
3.7.1. Testing using the Wp-method for m = n
3.7 The Partial W-method (2/2)
3.7.2. Testing using the Wp-method for m > n
3.8 The UIO-sequence Method (1/6)
3.8.1. Assumptions3.8.2. UIO sequences3.8.3. Core and noncore behavior
3.8 The UIO-sequence Method (2/6)
3.8.4. Generation of UIO sequences3.8.4.1 Explanation of gen-UIO
3.8 The UIO-sequence Method (3/6)
3.8 The UIO-sequence Method (4/6)
3.8.5. Distinguishing signatures
3.8 The UIO-sequence Method (5/6)
3.8.6. Test generation3.8.7. Test optimization3.8.8. Fault detection
3.8 The UIO-sequence Method (6/6)
3.9 Automata Theoretic Versuscontrol-flow-based Techniques (1/2)
3.9.1. n-switch-cover
3.9 Automata Theoretic Versuscontrol-flow-based Techniques (2/2)
3.9.2. Comparing automata-theoretic methods
Summary
Bibliographic Notes
Exercises (1/2)
Exercises (2/2)
Chapter 4: Test Generation from Combinatorial Designs
4.1 Combinatorial Designs (1/2)
4.1.1. Test configuration and test set4.1.2. Modeling the input and configuration spaces
4.1 Combinatorial Designs (2/2)
4.2 A Combinatorial Test-Design Process
4.3 Fault Model
4.3.1. Fault vectors
4.4 Latin Squares
4.5 Mutually Orthogonal Latin Squares
4.6 Pairwise Design: Binary Factors
4.7 Pairwise Design: Multivalued Factors (1/2)
4.7 Pairwise Design: Multivalued Factors (2/2)
4.7.1. Shortcomings of using MOLS for test design
4.8 Orthogonal Arrays4.8.1. Mixed-level orthogonal arrays
4.9 Covering and Mixed-Level Covering Arrays
4.9.1. Covering arrays4.9.2. Mixed-level covering arrays
4.10 Arrays Of Strength >2
4.11 Generating Covering Arrays (1/2)
4.11 Generating Covering Arrays (2/2)
Summary
Bibliographic Notes
Exercises
Chapter 5: Test Selection, Minimization, and Prioritization for Regression Testing
5.1 What is Regression Testing?
5.2 Regression-Test Process (1/2)
5.2.1. Test revalidation, selection, minimization, and prioritization5.2.2. Test setup5.2.3. Test sequencing
5.2 Regression-Test Process (2/2)
5.2.4. Test execution
5.2.5. Output comparison
5.3 RTS: The Problem
5.4 Selecting Regression Tests
5.4.1. Test all5.4.2. Random selection5.4.3. Selecting modification-traversing tests5.4.4. Test minimization
5.4.5. Test prioritization
5.5 Test Selection Using Execution Trace (1/3)5.5.1. Obtaining the execution trace5.5.2. Selecting regression tests
5.5 Test Selection Using Execution Trace (2/3)
5.5.3. Handling function calls5.5.4. Handling changes in declarations
5.5 Test Selection Using Execution Trace (3/3)
5.6 Test Selection Using Dynamic Slicing (1/3)
5.6.1. Dynamic slicing5.6.2. Computation of dynamic slices5.6.3. Selecting tests
5.6 Test Selection Using Dynamic Slicing (2/3)
5.6.4. Potential dependence5.6.5. Computing the relevant slice5.6.6. Addition and deletion of statements
5.6 Test Selection Using Dynamic Slicing (3/3)
5.6.7. Identifying variables for slicing5.6.8. Reduced dynamic-dependence graph
5.7 Scalability of Test-Selection Algorithms
5.8 Test Minimization
5.8.1. The set-cover problem5.8.2. A procedure for test minimization
5.9 Test Prioritization
5.10 Tools For Regression Testing
Summary
Bibliographic Notes (1/2)
Bibliographic Notes (2/2)
Exercises (1/2)
Exercises (2/2)
Part III: Test Adequacy Assessment and Enhancement
Chapter 6: Test-Adequacy Assessment Using Control Flow and Data Flow
6.1 Test Adequacy: Basics (1/3)
6.1.1. What is test adequacy?6.1.2. Measurement of test adequacy6.1.3. Test enhancement using measurements of adequacy
6.1 Test Adequacy: Basics (2/3)
6.1.4. Infeasibility and test adequacy6.1.5. Error detection and test enhancement
6.1 Test Adequacy: Basics (3/3)
6.1.6. Single and multiple executions
6.2 Adequacy Criteria Based on Control Flow (1/8)
6.2.1. Statement and block coverage6.2.2. Conditions and decisions
6.2 Adequacy Criteria Based on Control Flow (2/8)
6.2.3. Decision coverage6.2.4. Condition coverage6.2.5. Condition/decision coverage
6.2 Adequacy Criteria Based on Control Flow (3/8)
6.2.6. Multiple condition coverage6.2.7. Linear code sequence and jump (LCSAJ) coverage
6.2 Adequacy Criteria Based on Control Flow (4/8)
6.2.8. Modified condition/decision coverage6.2.9. MC/DC-adequate tests for compound conditions
6.2 Adequacy Criteria Based on Control Flow (5/8)
6.2 Adequacy Criteria Based on Control Flow (6/8)
6.2.10. Definition of MC/DC coverage
6.2 Adequacy Criteria Based on Control Flow (7/8)
6.2.11. Minimal MC/DC tests6.2.12. Error detection and MC/DC adequacy
6.2 Adequacy Criteria Based on Control Flow (8/8)
6.2.13. Short-circuit evaluation and infeasibility6.2.14. Tracing test cases to requirements
6.3 Data-Flow Concepts (1/3)
6.3.1. Definitions and uses6.3.2. c-use and p-use6.3.3. Global and local definitions and uses6.3.4. Data-flow graph
6.3 Data-Flow Concepts (2/3)
6.3.5. Def–clear paths6.3.6. Def–use pairs6.3.7. Def–use chains6.3.8. A little optimization
6.3 Data-Flow Concepts (3/3)
6.3.9. Data contexts and ordered data contexts
6.4 Adequacy Criteria Based on Data Flow (1/3)
6.4.1. c-use coverage6.4.2. p-use coverage6.4.3. all-uses coverage6.4.4. k–dr chain coverage
6.4 Adequacy Criteria Based on Data Flow (2/3)
6.4.5. Using the k–dr chain coverage6.4.6. Infeasible c-uses and p-uses6.4.7. Context coverage
6.4 Adequacy Criteria Based on Data Flow (3/3)
6.5 Control Flow Versus Data Flow
6.6 The Subsumes Relation
6.7 Structural and Functional Testing
6.8 Scalability of Coverage Measurement
Summary
Bibliographic Notes (1/2)
Bibliographic Notes (2/2)
Exercises (1/2)
Exercises (2/2)
Chapter 7: Test-Adequacy Assessment Using Program Mutation
7.1 Introduction
7.2 Mutation and Mutants (1/2)
7.2.1. First-order and higher-order mutants7.2.2. Syntax and semantics of mutants
7.2 Mutation and Mutants (2/2)
7.2.3. Strong and weak mutations7.2.4. Why mutate?
7.3 Test Assessment Using Mutation (1/3)
7.3.1. A procedure for test-adequacy assessment
7.3 Test Assessment Using Mutation (2/3)
7.3.2. Alternate procedures for test-adequacy assessment7.3.3. Distinguished versus killed mutants
7.3 Test Assessment Using Mutation (3/3)
7.3.4. Conditions for distinguishing a mutant
7.4 Mutation Operators
7.4.1. Operator types7.4.2. Language dependence of mutation operators
7.5 Design of Mutation Operators
7.5.1. Goodness criteria for mutation operators7.5.2. Guidelines
7.6 Founding Principles of Mutation Testing
7.6.1. The competent programmer hypothesis
7.6.2. The coupling effect
7.7 Equivalent Mutants
7.8 Fault Detection Using Mutation
7.9 Types of Mutants
7.10 Mutation Operators For C (1/8)
7.10.1. What is not mutated?7.10.2. Linearization7.10.3. Execution sequence
7.10 Mutation Operators For C (2/8)
7.10.4. Effect of an execution sequence7.10.5. Global and local identifier sets7.10.6. Global and local reference sets
7.10 Mutation Operators For C (3/8)
7.10.7. Mutating program constants7.10.7.1 Required Constant Replacement7.10.7.2 Constant for Constant Replacement7.10.7.3 Constant for Scalar Replacement7.10.8. Mutating operators7.10.8.1 Binary Operator Mutations7.10.8.2 Unary Operator Mutations
7.10 Mutation Operators For C (4/8)
7.10.9. Mutating statements7.10.9.1 Trap on Statement Execution
7.10 Mutation Operators For C (5/8)
7.10.9.2 Trap on if Condition7.10.9.3 Statement Deletion7.10.9.4 return Statement Replacement7.10.9.5 goto Label Replacement7.10.9.6 continue Replacement by break7.10.9.7 break Replacement by continue
7.10 Mutation Operators For C (6/8)
7.10.9.8 Break Out to nth Enclosing Level7.10.9.9 Continue Out to nth Enclosing Level7.10.9.10 Multiple Trip Trap7.10.9.11 Multiple Trip Continue7.10.9.12 Sequence Operator Mutation7.10.9.13 Move Brace Up or Down
7.10 Mutation Operators For C (7/8)
7.10.9.14 Switch Statement Mutation7.10.10. Mutating program variables7.10.10.1 Scalar Variable Reference Replacement
7.10 Mutation Operators For C (8/8)
7.10.10.2 Array Reference Replacement7.10.10.3 Structure Reference Replacement7.10.10.4 Pointer Reference Replacement7.10.10.5 Structure Component Replacement7.10.10.6 Array Reference Subscript Mutation7.10.10.7 Domain Traps
7.10.10.8 Twiddle Mutations
7.11 Mutation Operators for Java (1/2)7.11.1. Traditional mutation operators7.11.2. Inheritence
7.11 Mutation Operators for Java (2/2)
7.11.3. Polymorphism and dynamic binding7.11.4. Method overloading7.11.5. Java-specific mutation operators
7.12 Mutation Operators for Fortran 77, C, and Java: A Comparison
7.13 Tools for Mutation Testing
7.14 Mutation Testing within Budget
7.14.1. Prioritizing functions to be mutated7.14.2. Selecting a subset of mutation operators
Summary
Bibliographic Notes (1/3)
Bibliographic Notes (2/3)
Bibliographic Notes (3/3)
Exercises (1/2)
Exercises (2/2)
References (1/8)
References (2/8)
References (3/8)
References (4/8)
References (5/8)
References (6/8)
References (7/8)
References (8/8)
Subject Index (1/7)
Subject Index (2/7)
Subject Index (3/7)
Subject Index (4/7)
Subject Index (5/7)
Subject Index (6/7)
Subject Index (7/7)
Name Index (1/12)
Name Index (2/12)
Name Index (3/12)
Name Index (4/12)
Name Index (5/12)
Name Index (6/12)
Name Index (7/12)
Name Index (8/12)
Name Index (9/12)
Name Index (10/12)
Name Index (11/12)
Name Index (12/12)

Content preview from Foundations of Software Testing (Fundamental Algorithms and Techniques) by Pearson

Test Generation from Finite-State Models

Any nonempty test starting with symbol B distinguishes M from

. Mutant M

is distinguished from M by the input sequence BC.

Note that when excited with BC, M outputs the string 11 whereas

outputs the string 10. Input BC also distinguishes M

from M.

The complete set of distinguishing inputs can be found using the W-

method described in Section 3.6.

3.4.2 FAULT COVERAGE

One way to determine the goodness of a test set is to compute how many

errors it reveals in a given implementation M

. A test set that can reveal

all errors in M

is considered superior to one that fails to reveal one or

more errors. Methods for ...

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Foundations of Software Testing: Fundamental Algorithms and Techniques

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