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Teaching Mining Software Repositories

Zadia Codabux, Fatemeh Fard, Roberto Verdecchia, Fabio Palomba, Dario Di Nucci, Gilberto Recupito

TL;DR

This chapter provides a comprehensive, educator-focused guide to Mining Software Repositories (MSR), detailing a structured study design using the Goal-Question-Metric (GQM) framework, data extraction and cleaning, static and dynamic code analysis, and diverse data analysis techniques for unstructured and structured data. It emphasizes data quality, replicability, and clear communication through descriptive metrics and visuals, while addressing threats to validity and ethical considerations in MSR research. The work also discusses complementary mixed-method approaches, ethical guidelines, and current trends such as AI-enabled MSR and software ecosystem analysis, offering practical teaching strategies, exercises, and a starter list of repositories to facilitate hands-on MSR education. Collectively, it equips MSc and PhD students and educators with methodological guidance, ethical awareness, and tools to conduct reproducible MSR studies that yield actionable insights for software engineering practice. The chapter closes by illustrating how MSR can be integrated with qualitative and quantitative methods to produce richer, human-centered understanding of software development processes.

Abstract

Mining Software Repositories (MSR) has become a popular research area recently. MSR analyzes different sources of data, such as version control systems, code repositories, defect tracking systems, archived communication, deployment logs, and so on, to uncover interesting and actionable insights from the data for improved software development, maintenance, and evolution. This chapter provides an overview of MSR and how to conduct an MSR study, including setting up a study, formulating research goals and questions, identifying repositories, extracting and cleaning the data, performing data analysis and synthesis, and discussing MSR study limitations. Furthermore, the chapter discusses MSR as part of a mixed method study, how to mine data ethically, and gives an overview of recent trends in MSR as well as reflects on the future. As a teaching aid, the chapter provides tips for educators, exercises for students at all levels, and a list of repositories that can be used as a starting point for an MSR study.

Teaching Mining Software Repositories

TL;DR

This chapter provides a comprehensive, educator-focused guide to Mining Software Repositories (MSR), detailing a structured study design using the Goal-Question-Metric (GQM) framework, data extraction and cleaning, static and dynamic code analysis, and diverse data analysis techniques for unstructured and structured data. It emphasizes data quality, replicability, and clear communication through descriptive metrics and visuals, while addressing threats to validity and ethical considerations in MSR research. The work also discusses complementary mixed-method approaches, ethical guidelines, and current trends such as AI-enabled MSR and software ecosystem analysis, offering practical teaching strategies, exercises, and a starter list of repositories to facilitate hands-on MSR education. Collectively, it equips MSc and PhD students and educators with methodological guidance, ethical awareness, and tools to conduct reproducible MSR studies that yield actionable insights for software engineering practice. The chapter closes by illustrating how MSR can be integrated with qualitative and quantitative methods to produce richer, human-centered understanding of software development processes.

Abstract

Mining Software Repositories (MSR) has become a popular research area recently. MSR analyzes different sources of data, such as version control systems, code repositories, defect tracking systems, archived communication, deployment logs, and so on, to uncover interesting and actionable insights from the data for improved software development, maintenance, and evolution. This chapter provides an overview of MSR and how to conduct an MSR study, including setting up a study, formulating research goals and questions, identifying repositories, extracting and cleaning the data, performing data analysis and synthesis, and discussing MSR study limitations. Furthermore, the chapter discusses MSR as part of a mixed method study, how to mine data ethically, and gives an overview of recent trends in MSR as well as reflects on the future. As a teaching aid, the chapter provides tips for educators, exercises for students at all levels, and a list of repositories that can be used as a starting point for an MSR study.
Paper Structure (38 sections, 1 figure)

This paper contains 38 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Setting up a Source Code Repository Mining Study
  3. MSR Study Design
  4. Research goal formulation
  5. Research Questions Definition
  6. Metrics identification
  7. Identifying High-Quality Source Code Repositories
  8. Data Sources
  9. Source Code Repositories Selection Criteria
  10. Considerations when Selecting Data Sources
  11. Data Extraction
  12. Data Cleaning
  13. Code Analysis
  14. Static Code Analysis
  15. Dynamic Program Analysis
  16. ...and 23 more sections

Figures (1)

  • Figure 1: Set of Activities Involved in the MSR Study Process.