Empirical research in software engineering : concepts, analysis, and applications

Content: Introduction  What Is Empirical Software Engineering?  Overview of Empirical Studies  Types of Empirical Studies  Empirical Study Process  Ethics of Empirical Research  Importance of Empirical Research  Basic Elements of Empirical Research  Some Terminologies  Concluding Remarks         Systematic Literature Reviews  Basic Concepts  Case Study  Planning the Review  Methods for Presenting Results  Conducting the Review  Reporting the Review  SRs in Software Engineering         Software Metrics Introduction  Measurement Basics  Measuring Size  Measuring Software Quality  OO Metrics  Dynamic Software Metrics  System Evolution and Evolutionary Metrics  Validation of Metrics  Practical Relevance         Experimental Design  Overview of Experimental Design  Case Study: Fault Prediction Systems  Research Questions Reviewing the Literature  Research Variables  Terminology Used in Study Types  Hypothesis Formulation  Data Collection  Selection of Data Analysis Methods         Mining Data from Software Repositories  Configuration Management Systems  Importance of Mining Software Repositories  Common Types of Software Repositories  Understanding Systems  Version Control Systems  Bug Tracking Systems Extracting Data from Software Repositories  Static Source Code Analysis  Software Historical Analysis  Software Engineering Repositories and Open Research Data Sets  Case Study: Defect Collection and Reporting System for Git Repository         Data Analysis and Statistical Testing  Analyzing the Metric Data  Attribute Reduction Methods  Hypothesis Testing  Statistical Testing  Example-Univariate Analysis Results for Fault Prediction System         Model Development and Interpretation  Model Development  Statistical Multiple Regression Techniques  ML Techniques Concerns in Model Prediction  Performance Measures for Categorical Dependent Variable  Performance Measures for Continuous Dependent Variable  Cross-Validation  Model Comparison Tests  Interpreting the Results  Example-Comparing ML Techniques for Fault Prediction         Validity Threats  Categories of Threats to Validity  Example-Threats to Validity in Fault Prediction System  Threats and Their Countermeasures         Reporting Results  Reporting and Presenting Results Guidelines for Masters and Doctoral Students  Research Ethics and Misconduct         Mining Unstructured Data  Introduction  Steps in Text Mining  Applications of Text Mining in Software Engineering  Example-Automated Severity Assessment of Software Defect Reports         Demonstrating Empirical Procedures  Abstract  Introduction  Related Work  Experimental Design  Research Methodology  Analysis Results Discussion and Interpretation of Results  Validity Evaluation  Conclusions and Future Work  Appendix        Tools for Analyzing Data  WEKA  KEEL  SPSS  MATLAB  R  Comparison of Tools         Appendix     References     Index        Exercises and Further Reading appear at the end of most chapters.