The printable version is no longer supported and may have rendering errors. Please update your browser bookmarks and please use the default browser print function instead.

Metrics and Empirical Methods for Software Engineers and Data Scientists_old

Course name: Metrics and Empirical Methods for Software Engineers and Data Scientists
Course number: SE-06
Area of instruction: Computer Science and Engineering

Administrative details

Faculty: Computer Science and Engineering
Year of instruction: 1st year of MSc
Semester of instruction: 1st semester
No. of Credits: 5 ECTS
Total workload on average: 180 hours overall
Frontal lecture hours: 2 hours per week.
Frontal tutorial hours: 0 hours per week.
Lab hours: 2 hours per week.
Individual lab hours: 2 hours per week.
Frequency: weekly throughout the semester.
Grading mode: letters: A, B, C, D.

Course outline

The course aims at presenting the fundamentals of metrics and empirical methods to the future software engineers and data scientists, on one side providing the scientific fundamentals of the disciplines, and on the other anchoring the theoretical concepts on practices coming from the world of software development and engineering. As a side product, the course also refreshes the fundamentals of statistics, providing the basis for more advanced statistical courses in the following semester(s) of study.

Expected learning outcomes

To master the fundamentals of statistics and empirical sciences
To understand how to perform an experiment using measures of different kinds and on different scales
To know the fundamental measures in software development
To appreciate the value of different kinds of measures, also coming from biometrics sensors
To be able to develop a comprehensive metrics plan for software organizations linking business goals to actual practices

Programming related learning outcomes

None.

Required background knowledge

Fundamentals of statistics and of software development.

Prerequisite courses

None

Detailed topics covered in the course

Review of the fundamentals of descriptive and hypothesis testing
Inferential statistics, convolution, correlation, linear and logistic regression
Experimentation and quasi experimentation
Suitable parametric and non parametric statistical tests
Fundamental of measurements, measurement scales, kinds of measurements, the representational theory of measurement
Fundamental of software measures, axiomatic approaches, GQM(+)
Object oriented metrics and other advanced metrics
Biometrics in software development
Non invasive measurements
Statistical meta-analysis

Textbook

Handouts supplied by the instructor

Reference material

Required computer resources

A laptop.

Evaluation

Weekly quizzes (20%)
Project (40%)
Final oral (40%)

MSc:MetricsAndEmpiricalMethods old

Contents