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Automatisk testgenerering

  • Högskolepoäng 2.5 hp
  • Studieort Ortsoberoende
  • $stringTranslations.StartDate 2020-11-09 - 2021-01-17 (deltid 17%)
  • Utbildningsnivå Avancerad nivå
  • Kurskod DVA481
  • Huvudområde Datavetenskap

In contrast to learning how to do manual testing, in this course you will learn how to generate tests automatically in the sense that test creation satisfying a given test goal or given requirement is performed automatically.

About this course

The increasing competition pressure for rapid introduction of new or modified system versions is posing problems to properly testing software. These pressures have led many organizations to begin transitioning their development processes to agile development and continuous integration, greatly shortening the time available to conduct comprehensive testing.

In contrast to learning how to do manual testing, in this course you will learn how to generate tests automatically in the sense that test creation satisfying a given test goal or given requirement is performed automatically.

This course provides an understanding of automating software testing using program analysis with the goal of intelligently and algorithmically creating tests. The course covers search-based test generation, combinatorial and random testing while highlighting the challenges associated with the use of automatic test generation.

You will learn

  • Understand algorithmic test generation techniques and their use in developer testing and continuous integration.
  • Understand how to automatically generate test cases with assertions.
  • Have a working knowledge and experience in static and dynamic generation of tests.
  • Have an overview knowledge in search-based testing and the use of machine learning for test generation.

Entry requirements

  • 120 credits, of which 80 credits in Computer Science and/or Computer Engineering.
  • 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

Automatisk testgenerering

Language

English

Teacher

Universitetslektor

Eduard Paul Enoiu

+4621101624

eduard.paul.enoiu@mdh.se

Kursplan

Du kan läsa i detalj om utbildningen, dess innehåll och litteratur m.m. i kursplanen

Se kursplan

Apply to the course

Automated test generation

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Application information

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.

Entry requirements

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.

FutureE

The courses are part of the FutureE project where MDH offers online courses in the areas of AI, Environmental and Energy Engineering, Software and Computer Systems Engineering.

For companies that want to collaborate on competence development