Code Coverage Test Case Prioritization for Regression Testing Using Non-Deterministic Approach

Inhaltsverzeichnis (Table of Contents)

• 1. INTRODUCTION
  • 1.1 MOTIVATION
    • 1.1.1 What Is Software Testing and Why Is It Important?
    • 1.1.2 The Need for Regression Testing
  • 1.2 PROBLEM STATEMENT
  • 1.3 RESEARCH CONTRIBUTIONS
  • 1.4 SUMMARY
• 2. LITERATURE REVIEW
  • 2.1 OVERVIEW
  • 2.2 SOFTWARE REGRESSION TESTING
  • 2.3 RESEARCH AIMS AND OBJECTIVES
  • 2.4 LITERATURE SURVEY
  • 2.5 TYPES OF TESTING
  • 2.6 TEST CASES OR TEST SUITE
  • 2.7 OPTIMIZATION METHODS
    • 2.7.1 Random Generation
    • 2.7.2 Symbolic Execution
    • 2.7.3 Search-Based Generation
    • 2.7.4 Model-Based Generation
    • 2.7.5 Heuristic-Based Techniques
  • 2.8 SUMMARY
• 3. REGRESSION TESTING
  • 3.1 SOFTWARE DEVELOPMENT PROCESS
  • 3.2 STRATEGIES OF REGRESSION TESTING

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This monograph aims to address the challenges of efficient test case selection for regression testing, focusing on maximizing code coverage while minimizing execution time. It proposes a novel methodology incorporating a hybrid algorithm for optimal test case generation and selection, and a Non-Deterministic Finite Automata (NFA) approach for testing un-invoked methods.

• Efficient test case selection for regression testing
• Maximizing code coverage
• Minimizing test execution time
• Handling un-invoked methods in software testing
• Application of a hybrid algorithm and NFA for improved testing

Zusammenfassung der Kapitel (Chapter Summaries)

1. INTRODUCTION: This introductory chapter establishes the context for the research by highlighting the importance of efficient test case selection in software regression testing. It underscores the need to maximize code coverage and minimize testing time, emphasizing the challenges posed by traditional approaches. The chapter introduces the problem of insufficient code coverage and lengthy execution times in regression testing, motivating the need for a new methodology. It also lays out the research contributions and provides a concise summary of the entire work.

2. LITERATURE REVIEW: This chapter provides a comprehensive overview of existing literature on software regression testing and optimization methods. It examines various approaches to test case generation and selection, including random generation, symbolic execution, search-based generation, model-based generation, and heuristic-based techniques. The chapter critically analyzes the strengths and limitations of these methods, setting the stage for the proposed new methodology. It also delves into different types of testing and the concept of test suites, laying the groundwork for a deeper understanding of the challenges addressed in the research.

3. REGRESSION TESTING: This chapter delves into the specifics of regression testing within the software development lifecycle. It explores different strategies for conducting effective regression testing and their implications for code coverage and efficiency. This chapter likely details the practical applications and considerations related to implementing the strategies discussed within the scope of the software development process. It might include discussions on various testing approaches and their effectiveness in diverse software development settings.

Schlüsselwörter (Keywords)

Regression testing, code coverage, test case prioritization, hybrid algorithm, Non-Deterministic Finite Automata (NFA), software testing, optimization, test case generation, un-invoked methods, fault detection.

Frequently asked questions

What is the purpose of this document?

This document provides a language preview of a monograph, including the table of contents, objectives, key themes, chapter summaries, and keywords. It is intended for academic use in analyzing themes related to software regression testing.

What is the main topic of this monograph?

The monograph focuses on addressing the challenges of efficient test case selection for regression testing, aiming to maximize code coverage while minimizing execution time.

What key areas does the monograph cover?

The monograph covers topics such as: efficient test case selection for regression testing, maximizing code coverage, minimizing test execution time, handling un-invoked methods, and the application of a hybrid algorithm and Non-Deterministic Finite Automata (NFA) for improved testing.

What does the introduction chapter cover?

The introduction establishes the context for the research by highlighting the importance of efficient test case selection in software regression testing. It underscores the need to maximize code coverage and minimize testing time, emphasizing the challenges posed by traditional approaches and introducing the research contributions.

What is discussed in the literature review chapter?

The literature review provides a comprehensive overview of existing literature on software regression testing and optimization methods. It examines various approaches to test case generation and selection, including random generation, symbolic execution, search-based generation, model-based generation, and heuristic-based techniques.

What is discussed in the regression testing chapter?

The regression testing chapter delves into the specifics of regression testing within the software development lifecycle. It explores different strategies for conducting effective regression testing and their implications for code coverage and efficiency.

What are some of the keywords associated with this research?

Some of the keywords include: Regression testing, code coverage, test case prioritization, hybrid algorithm, Non-Deterministic Finite Automata (NFA), software testing, optimization, test case generation, un-invoked methods, fault detection.

What are un-invoked methods and why are they mentioned?

Un-invoked methods refer to software functions or procedures that are never called during normal program execution. The monograph aims to address testing these un-invoked methods using a Non-Deterministic Finite Automata (NFA) approach.

What is the significance of the hybrid algorithm mentioned in the objectives?

The hybrid algorithm is a core component of the proposed methodology, aimed at generating and selecting optimal test cases for regression testing. Its goal is to improve both code coverage and execution time efficiency compared to traditional methods.