The course consists of two parts: For the regression test selection part, the purpose is to enable participants get an in-depth understanding of techniques for selecting test cases that should be executed following changes to the software under test. For the software fault prediction part, the purpose is to use software fault prediction models as a way to provide quality estimates using measurements from design and testing processes.
The course will further discuss methodology of building simple software fault prediction models and highlight its use.
- Introduction to regression testing and regression test selection
- Regression test selection techniques
- Basis of regression test selection
- Regression test selection for different applications
- Introduction to software fault prediction and benefits
- Classes of predictor variables to use for software fault prediction
- Techniques for software fault prediction
- Software fault prediction methodology
Learning outcomes: On completion of the course, students will be able to:
- Know different regression test selection techniques and the basis of their selection mechanisms.
- Understand the context in which to use different regression test selection techniques.
- Understand the limitations and advantages of different regression test selection techniques.
- Understand the use of software fault prediction to assist software testing.
- Understand the underlying methodological issues in regression test selection and building of software fault prediction models.
Related industrial challenges addressed in the course:
- Minimize test effort and increase test effectiveness in regression testing
- How to know which parts of the software under test to focus on during testing.
Upcoming instances: September 2017, and ’18
- Course title in Swedish: Regressionstestning och felprediktering
- Course code: DVA448 (at MdH), MDH-24132 (at antagning.se)
- Course syllabus at Mälardalen University
- September 2017 instance at Mälardalen University
- Apply at antagning.se
- Admission requirements: 120 credits of which at least 80 credits in Computer Science and / or equivalent. In addition, at least 18 months of documented work experience in software development.