Course syllabus - Regression Test Selection and Software Fault Prediction

Objectives

The focus of this course is on two distinct activities in software testing that impacts test effectiveness and efficiency: regression test selection (RTS) and software fault prediction (SFP). Regression testing is done to ensure that recent changes to software (e.g., as a result of bug fixes, implementation of new functionality and change requests) do not impact its quality. Regression test selection (RTS) deals with the mechanisms used to select a subset of test cases to test changed parts of the software. RTS, although distinct, shares some of its purpose with test suite minimization and test case prioritization approaches. Software fault prediction (SFP), on the other hand, is a way to provide quality estimates using measurements from design and testing processes. For example, SFP helps a testing team focus their testing efforts on fault­prone files and components in a coming release of a project. The overall purpose of this course is, therefore, to provide participants with an understanding of variety of mechanisms for RTS and SFP and to appreciate its usefulness in improving test efficiency and effectiveness.

Learning outcomes

After completing the course, the student shall be able to:

1. differentiate between mechanisms for regression test selection (RTS) techniques
2. argue in favor of or against certain RTS techniques based on variery of criteria
3. be able to apply RTS techniques in industrial projects
4. comprehend main ideas for using software fault prediction (SFP) techniques
5. reflect on possible difficulties in using SFP techniques in practice

Course content

  The course covers the following topics:

- Introduction to regression testing and regression test selection (RTS)
- Techniques for RTS
- Basis for RTS
- RTS for different applications
- Introduction to software fault prediction (SFP) and its benefits
- Classes of predictor variables to use for SFP
- Techniques for SFP
- SFP methodology
 

Tuition

Video lectures.

Specific 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. Swedish B/Swedish 3 and English A/English 6 are required. For courses given entirely in English exemption is made from the requirement in Swedish. B/Swedish 3.

Examination

Written assignment (INL1), 0,5 hp, examines the learning objective 1, marks Fail (U) or Pass (G)
Written assignment (INL2), 1 hp, examines the learning objectives 2 and 3, marks Fail (U) or Pass (G)
Written assignment (INL3), 1 hp, examines the learning objectives 4 and 5, marks Fail (U) or Pass (G)

A student who has a certificate from MDU regarding a disability has the opportunity to submit a request for supportive measures during written examinations or other forms of examination, in accordance with the Rules and Regulations for Examinations at First-cycle and Second-cycle Level at Mälardalen University (2020/1655). It is the examiner who takes decisions on any supportive measures, based on what kind of certificate is issued, and in that case which measures are to be applied.

Suspicions of attempting to deceive in examinations (cheating) are reported to the Vice-Chancellor, in accordance with the Higher Education Ordinance, and are examined by the University’s Disciplinary Board. If the Disciplinary Board considers the student to be guilty of a disciplinary offence, the Board will take a decision on disciplinary action, which will be a warning or suspension.

Grade

Pass, Fail

Interim Regulations and Other Regulations

The course overlaps with 2 credits towards Software Verification and Validation.