Empirical research has now become an essential component of software engineering yet software practitioners and researchers often lack an understanding of how the empirical procedures and practices are applied in the field. Empirical Research in Software Engineering: Concepts, Analysis, and Applications shows how to implement empirical research processes, procedures, and practices in software engineering. Written by a leading researcher in empirical software engineering, the book describes the necessary steps to perform replicated and empirical research. It explains how to plan and design experiments, conduct systematic reviews and case studies, and analyze the results produced by the empirical studies. The book balances empirical research concepts with exercises, examples, and real-life case studies, making it suitable for a course on empirical software engineering. The author discusses the process of developing predictive models, such as defect prediction and change prediction, on data collected from source code repositories. She also covers the application of machine learning techniques in empirical software engineering, includes guidelines for publishing and reporting results, and presents popular software tools for carrying out empirical studies -- Publisher's description

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nexusstc/Empirical research in software engineering : concepts, analysis, and applications/bdfe8ad96c9052bc282ec610cc935fa0.pdf

zlib/Computers/Programming/Malhotra, Ruchika/Empirical research in software engineering : concepts, analysis, and applications_2633316.pdf

Ruchika Malhotra

Chapman and Hall/CRC

CRC Press LLC

Routledge

1st edition, Erscheinungsort nicht ermittelbar, 2016

CRC Press (Unlimited), Boca Raton, 2016

United States, United States of America

Boca Raton, FL, 2016

1, FR, 2015

lg1423367

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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.

2015-12-23