Quality assurance - Regression testing and fault prediction
Please note that applications are closed for this course for the duration of autumn semester 2020.
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.
About this course
- 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
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 limitations and advantages of different regression test selection techniques.
- Understand the underlying methodological issues in regression test selection and building of software fault prediction models.
- Understand the use of software fault prediction to assist software testing.
- Understand the context in which to use different regression test selection techniques.
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.
- 100 credits, out of which 70 credits are within technology or information technology, with at least 15 credits in programming or software development.
- In addition, English course A/English course 6 is required
You can also apply for the course and get your eligibility evaluated based on knowledge acquired in other ways, such as work experience, other studies etc.
Course title in Swedish
Kvalitetssäkring - Regressionstestning och felprediktering
After submitting your electronic application, the next step is to submit documentation to demonstrate your eligibility for the course you have applied for. In order to document your eligibility, you must provide your high school diploma and university transcript and proof of your English language proficiency.
To meet the entry requirements for this course you need to have previous academic qualifications (university studies). You will find the specific entry requirements above.
No academic qualifications?
If you do not have the formal academic qualifications needed for a specific course, you can apply for the course and get your eligibility evaluated based on knowledge acquired in other ways, such as work experience, other studies etc. This is also known as a validation of prior learning.
Recognition of prior learning means the mapping out and assessment of an individual's competence and qualifications, regardless how, where or when they were acquired – in the formal education system or in some other way in Sweden or abroad, just recently or a long time ago.
If you think your knowledge and competences will qualify you for this course, you will need to upload th following with your application:
- CV with description of your educational and professional background. Your CV must describe your knowledge and competences in relation to the entry requirements.
- If you refer to work experience, you need to upload an Employers certificate.
If we need more information, we will contact you.
The courses are part of the Prompt project where MDH offers courses at master's level. The courses are given online without physical meetings and are flexible in time and space so that they can be combined with professional life.For companies that want to collaborate on competence development