Course Overview

This course is an exploration of topics in empirical software engineering. It covers software metrics, experiment design, and empirical topics for software engineering. The course is designed to give the student an appreciation of the importance of measurement in software engineering and how to develop and apply metrics to software projects. It also provides the mathematical background needed for performing measurements and empirical studies and conducting experiments.

Upon completion of the course, you will be able to:

• Use metrics to help control, track, and improve software engineering projects and processes.
• Develop and analyze measures and metrics.
• Design and execute experiments.
• Apply various statistical analyses to the data collected from the experiments in order to determine the validity of the results and develop conclusions on process performance or technique comparison.

Textbook and Readings

The textbook is Software Metrics A Rigorous & Practical Approach 2e (revised printing), by Fenton and Pfleeger, ISBN 053495425-1. In addition, there will be several articles and papers from various sources that the student will be responsible for. See the Schedule & Assignments page for the list of readings. Other books and readings are on the Resource page. You may wish to consult them if you need additional information on basic statistics, or other knowledge that you need to know for this course.

Projects and Assignments

Besides the readings, students will be expected to complete one or more projects or research papers. The projects will be discussed in early classes and then assigned.

List of Topics

The following is a list of topics that will be included. I reserve the right to alter the set of topics based upon the progress and interests of the class.

• Measurement, theory and practice
• Empirical methods
• Data analysis
• Experiment design
• Statistical analysis of data

Prerequisites

You should enter this course with the following prerequisite knowledge:

• Knowledge of general software engineering topics, especially the software development life-cycle.
• The ability to program in at least one object-oriented language. Java is the preferred language for the course, but students may use C++, C#, or some other object-oriented language.
• Basic mathematical maturity. Specifically, you should be able to learn the statistics necessary to perform the data analysis on the experimental data.

Time Commitment

You should expect to spend 7-10 hours outside of class per week on this course.

Modified: 14-Jan-2005
Gary Pollice