Course syllabus for Empirical software engineering

The course syllabus contains changes
See changes

Course syllabus adopted 2020-02-20 by Head of Programme (or corresponding).

Overview

  • Swedish nameEmpirisk programvaruteknik
  • CodeDAT246
  • Credits7.5 Credits
  • OwnerMPSOF
  • Education cycleSecond-cycle
  • Main field of studyComputer Science and Engineering, Software Engineering
  • DepartmentCOMPUTER SCIENCE AND ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 24120
  • Maximum participants80
  • Open for exchange studentsYes

Credit distribution

0114 Written and oral assignments 2.5 c
Grading: UG		 2.5 c
0214 Examination 5 c
Grading: TH		 5 c
  • 11 Jan 2021 pm L
  • 08 Apr 2021 am L
  • 24 Aug 2021 pm L

In programmes

Examiner

Go to coursepage (Opens in new tab)

Course round 2

  • Teaching language English
  • Application code 99233
  • Maximum participants20
  • Open for exchange studentsNo
  • Only students with the course round in the programme overview.

Credit distribution

0114 Written and oral assignments 2.5 c
Grading: UG		 2.5 c
0214 Examination 5 c
Grading: TH		 5 c

    Examiner

    Go to coursepage (Opens in new tab)

    Eligibility

    General entry requirements for Master's level (second cycle)
    Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

    Specific entry requirements

    English 6 (or by other approved means with the equivalent proficiency level)
    Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

    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)

    • Knowledge and understanding:
      • Describe, understand, and apply empiricism in software engineering
      • Describe, understand, and partly apply the principles of case study research/experiments/surveys.
      • Describe and understand the underlying principles of meta-analytical studies.
      • Explain the importance of research ethics. 
      • Recognise and define code of ethics for when conducting research in software engineering.  
      • Discuss and explain the most common ethical models in research.
      • State and explain the importance of threats to validity and how to control said threats.
    •   Skills and abilities:
      • Design and implement an empirical study
      • Assess suitability of and apply methods of analysis on data
      • Analyse descriptive statistics and decide on appropriate analysis methods.
      • Use and interpret code of ethics for software engineering research.
    • Judgement and approach:
      • State and discuss the tools used for data analysis and, in particular, judge their output.
      • Judge the appropriateness of particular empirical methods and their applicability to attack various and disparate software engineering problems.
      • 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:
    • 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.

    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

    Examination forms: The course is examined by three written lab assignments carried out in groups of normally 3-4 students. The assignments are graded individually, taking into account the group work as well as the student's individual contribution to the group work.
    The course is also examined by an individual written hall examination. The assignments are both theoretical and practical in nature.

    The course syllabus contains changes

    • Changes to examination:
      • 2020-09-30: Grade raising No longer grade raising by GRULG