Software testing automation : testability evaluation, refactoring, test data generation and fault localization / Saeed Parsa

By:

Parsa, Saeed [author]

Material type: Text

text

Media type:

unmediated

Carrier type:

colume

ISBN:

9783031220562

Subject(s):

Computer software -- Testing -- Automation

DDC classification:

005.1/4 PAR 2023 23/eng/20230330

LOC classification:

QA76.76.T48

Contents:

Software Testability -- Unit testing and Test-Driven Development -- Acceptance Testing and Behavior Driven Development

Summary: This book is about the design and development of tools for software testing. It intends to get the reader involved in software testing rather than simply memorizing the concepts. The source codes are downloadable from the book website. The book has three parts: software testability, fault localization, and test data generation. Part I describes unit and acceptance tests and proposes a new method called testability-driven development (TsDD) in support of TDD and BDD. TsDD uses a machine learning model to measure testability before and after refactoring. The reader will learn how to develop the testability prediction model and write software tools for automatic refactoring. Part II focuses on developing tools for automatic fault localization. This part shows the reader how to use a compiler generator to instrument source code, create control flow graphs, identify prime paths, and slice the source code. On top of these tools, a software tool, Diagnoser, is offered to facilitate experimenting with and developing new fault localization algorithms. Diagnoser takes a source code and its test suite as input and reports the coverage provided by the test cases and the suspiciousness score for each statement. Part III proposes using software testing as a prominent part of the cyber-physical system software to uncover and model unknown physical behaviors and the underlying physical rules. The reader will get insights into developing software tools to generate white box test data.

List(s) this item appears in: New Book 2025

Average rating: 0.0 (0 votes)

Holdings ( 1 )
Title notes ( 4 )

Holdings
Item type	Current library	Home library	Collection	Shelving location	Call number	Status	Date due	Barcode	Item holds	Course reserves
Reference	FIRST CITY UNIVERSITY COLLEGE	FIRST CITY UNIVERSITY COLLEGE	Reference	FCUC Library	005.14 PAR 2023 (Browse shelf(Opens below))	Not for loan		00025255		Master in Software Engineering

Total holds: 0

Includes bibliographical references

Software Testability -- Unit testing and Test-Driven Development -- Acceptance Testing and Behavior Driven Development

Available to OhioLINK libraries

This book is about the design and development of tools for software testing. It intends to get the reader involved in software testing rather than simply memorizing the concepts. The source codes are downloadable from the book website. The book has three parts: software testability, fault localization, and test data generation. Part I describes unit and acceptance tests and proposes a new method called testability-driven development (TsDD) in support of TDD and BDD. TsDD uses a machine learning model to measure testability before and after refactoring. The reader will learn how to develop the testability prediction model and write software tools for automatic refactoring. Part II focuses on developing tools for automatic fault localization. This part shows the reader how to use a compiler generator to instrument source code, create control flow graphs, identify prime paths, and slice the source code. On top of these tools, a software tool, Diagnoser, is offered to facilitate experimenting with and developing new fault localization algorithms. Diagnoser takes a source code and its test suite as input and reports the coverage provided by the test cases and the suspiciousness score for each statement. Part III proposes using software testing as a prominent part of the cyber-physical system software to uncover and model unknown physical behaviors and the underlying physical rules. The reader will get insights into developing software tools to generate white box test data.