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Graduate courses

Departments' graduate courses for PhD-students.

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Syllabus for

Academic year
DAT246 - Empirical software engineering
Empirisk programvaruteknik
 
Syllabus adopted 2019-02-21 by Head of Programme (or corresponding)
Owner: MPSOF
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: Second-cycle
Major subject: Computer Science and Engineering, Information Technology
Department: 37 - COMPUTER SCIENCE AND ENGINEERING

The course is full. For waiting list, please contact the director of studies: mylana@chalmers.se
Teaching language: English
Application code: 24114
Open for exchange students: Yes
Maximum participants: 80

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0114 Written and oral assignments 2,5c Grading: UG   2,5c    
0214 Examination 5,0c Grading: TH   5,0c   13 Jan 2020 pm L,  07 Apr 2020 am DIST   25 Aug 2020 am L

In programs

MPIDE INTERACTION DESIGN AND TECHNOLOGIES, MSC PROGR, Year 2 (elective)
MPSOF SOFTWARE ENGINEERING AND TECHNOLOGY, MSC PROGR, Year 1 (compulsory)
MPHPC HIGH-PERFORMANCE COMPUTER SYSTEMS, MSC PROGR, Year 1 (elective)
MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (elective)

Examiner:

Richard Torkar

  Go to Course Homepage

Replaces

DAT245   Empirical software engineering


Eligibility:


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

To be eligible for the course Empirical Software Engineering the student should have a bachelor degree in Software Engineering, Computer Science or equivalent.

Aim

Software development organizations need to constantly improve to become faster, better, and more efficient. This course aims to learn scientific approaches, in particular experiments and statistics, for data collection e.g. as a basis for analysis and decision support in initiatives to improve performances in software development organizations. The course prepares students for the master thesis project and improves the student’s ability to conduct PhD studies.

Learning outcomes (after completion of the course the student should be able to)

1. Knowledge and understanding:
a. Describe the principles of empiricism in software engineering.
b. Describe the principles of case study research/experiments/surveys.
c. Describe the principles of meta-analytical studies.
d. Explain the importance of research ethics.
e. Recognise and define code of ethics for when conducting research in software
engineering.
f. Discuss and explain the most common ethical models in research.
g. State and explain the importance of threats to validity and how to control
said threats.


2. Skills and abilities:

a. Design an empirical study.

b. Analyse descriptive statistics and decide on appropriate analysis methods.

c. Use and interpret code of ethics for software engineering research.



3. Judgement and approach:

a. State and discuss the tools used for data analysis and, in particular, judge
their output.

b. Judge the appropriateness of particular empirical methods and their
applicability to solve various and disparate software engineering problems.

c. Question and assess common ethical issues in software engineering research.


Content

This course is for students who are interested in the empirical methods applied to the field of software engineering. The course introduces quantitative and qualitative methods in software engineering with accompanying statistical methods used for analysis.

The course contains:
1. Descriptive and inferential statistical methods applied to software engineering.
2. Conducting qualitative and quantitative methods in software engineering.
3. Methods for analysing quantitative and qualitative data in software engineering.
4. Usage of statistical tools.

Organisation

The course introduces quantitative and qualitative methods in software engineering research with accompanying statistical methods used for analysis.

The course contains: Descriptive and inferential statistical methods applied to software engineering. Conducting qualitative and quantitative methods in software engineering. Methods for analysing quantitative and qualitative data in software engineering. Usage of statistical tools.

Literature

We will use different textbooks and research articles for different parts. More information will be given before the course starts.

Examination including compulsory elements

The course is examined by three written lab assignments carried out in groups of normally 3-4 students. The course is also examined by an individual written hall-exam. The assignments are both theoretical and practical in nature.


Page manager Published: Thu 04 Feb 2021.