Course syllabus
Course-PM
DAT521 / DIT849 Behavioral software engineering lp4 VT26 (7.5 hp)
Course is offered by the department of Computer Science and Engineering
Contact details
Examiner: Robert Feldt, robert.feldt@chalmers.se
Course responsible: Robert Feldt, robert.feldt@chalmers.se
Teachers/Lecturers: Robert Feldt, Lucas Gren, Lekshmi Murali Rani, and Per Lenberg
Teaching assistant and main student contact: Lekshmi Murali Rani, lekshmi@chalmers.se
Course purpose
Much of software engineering research, as well as practice, focuses on technical or process aspects of software development. In contrast, the Behavioral Software Engineering (BSE) course gives knowledge about how the humans that participate in and drive software engineering and development processes and organizations are key in making software projects successful.
Humans are not always rational, but commonly irrational, and act in groups and organizational settings where politics, group norms, personal agendas, as well as unconscious biases and preconceptions govern and affect them. A deeper understanding of human nature helps software organizations better cater to the needs of their employees, build on their strengths as well as overcome their weaknesses, and, overall, increase the chance that software development work succeeds.
BSE is a relatively new area within Software Engineering that complements the technology and process focus that dominates the area today. It also introduces the research methods that are needed for BSE studies and discusses how they differ from many of the more traditionally and commonly used research methods.
For 2026, the course also has a clear overarching theme: how BSE and SE are changing in response to recent developments in AI, GenAI, LLMs, and agentic systems for developing software. Throughout the course we will discuss both the opportunities and risks of this transformation, and in particular how higher-level and more human-centered issues may become even more important.
Schedule
The course starts on Monday, March 23rd, 2026 in room Jupiter 122, Chalmers Lindholmen. Lectures/seminars are normally given on Mondays and Wednesdays 10:15-12:00. The week starting Monday, April 6th, 2026 has no scheduled lectures or course moments. For rooms used in the course, please see the schedule in TimeEdit:
The Wednesday afternoon lab times 13:15-17:00 are used flexibly during the course and we will not use the full time every Wednesday. They are used for individual assignment presentations, discussion of the research papers studied in the course, industry interviews, and support for the group assignment. Once the individual presentations start, the first slot on Wednesdays is reserved for them, and additional Wednesday-afternoon time will be used if needed to fit all students. The final course week starting Monday, May 25th, 2026 uses both the Monday and Wednesday course slots for group assignment presentations.
The final written examination is on Wednesday, June 3rd, 2026, 08:30-12:30.
Course literature
The course is fully based on research papers as well as written material provided by the course teachers. A meta-goal of the course is that you also learn both where to find and access new research, directly from research papers. Each major theme of the course will have one research paper but also additional, introductory text. Students need not buy any book to participate in the course; all material will be provided via Canvas for the course.
Course design
The course is provided in the form of modules, which combine lectures, discussions, and supervised practical work with exercises in small groups and individually. The exercises are both theoretical and practical in nature. A final, individual written hall examination is the final element of the course. For some exercises, we will have industry visitors discuss the themes in the course and how they connect to software development work in the industry.
Modules are organized around themes that together span several units of analysis, from the individual software developer/engineer, over teams and groups, and on to the organizational level. This is complemented by an introduction to the field as well as an outlook on the future. Outline of themes:
- Introduction to BSE
- Individuals:
- Experience and Emotion
- Personality and Cognitive Biases
- Motivation and Attitudes
- Personal sustainability
- Groups:
- Norms and Creativity
- Social factors on SW teams,
- Group dynamics, and maturity
- Organizations:
- Organizational change
- Culture
- Politics, happiness & freedom
- Gender, ethics, and sustainability
- Research methods (Meta):
- Ethnography,
- Interview studies,
- Reflexivity and Validity Threats
- Course summary and outlook:
- BSE implications and effects,
- Human-AI collaboration in (B)SE
- Future of BSE in research & practice
The first module on introduction to Behavioral Software Engineering requires no preparation from students, but for the other modules, students need to read the material before coming to the associated course seminar/event. These seminars are organized as partly traditional lectures, with an introduction and overview from the teacher(s), and partly as discussion/reflection in groups and the whole class. It is important that students prepare for seminars by reading the assigned material but also taking an active part in discussions during the course events. Students should reflect on their own experience with software development both individually and in groups/projects and share it with the class.
The Wednesday afternoons are adapted to the current phase of the course. There are no scheduled course moments in the week starting April 6th. The early industry interview is planned for Wednesday, April 15th, 2026, and the later interview for Wednesday, May 13th, 2026. Individual presentations start on Wednesday, April 22nd, 2026. The normal plan is to use the first Wednesday slot for individual presentations and then continue with paper discussion and group-assignment support, but later Wednesday-afternoon time may also be used for more individual presentations when needed.
Canvas is the main source of information for the course. Students should check the Canvas page several times each week to stay on top of the latest information. Changes will be posted as announcements in Canvas. Questions should primarily be asked either through comments on Canvas announcements or directly to Lekshmi by email.
All deadlines are firm on the date and time stated. No exceptions.
Changes made since the last occasion
The course was given the first time in VT2022 so this is the 4th instance of the course.
Based on the experiences and feedback from 2022 these changes were made for 2023: one less group assignment, so there is now only one individual and one group assignment, home exam is complemented by an oral discussion with a teacher, and pre-reading and discussion of papers in Perusall now gives further bonus points for the home exam.
For 2024 we changed the group assignment to also include interviewing an industry practitioner or a researcher in relation to the group's chosen topic. We also changed the order of the assignments so that the individual, reflective assignment comes later in the course when student's have more experience and knowledge about the course topics.
For 2025 we have added more material on BSE research methods turning that into it's own lecture. We also have a clearer focus on Human-AI collaboration and it's effect on SE and BSE. The individual assignments were changed to provide additional material for the laborations / afternoon sessions.
For 2026 the individual assignment is now based on an approved research-paper presentation rather than a reflective report, and attendance at classmates' presentations is now mandatory for passing. The group assignment has a stronger and explicit focus on how AI changes SE and BSE, while grading emphasizes the presentation, slides, and discussion rather than extensive report grading. The final examination is now a traditional written hall examination instead of a take-home exam.
Learning objectives and syllabus
Learning objectives:
- Explain why human and social factors are critical in (successful) SE, - Describe the risks of focusing mainly on technology in SE,
- Describe and explain what Behavioral Software Engineering (BSE) is and how it relates to socio-technical systems analysis, human factors studies, and Human-Computer Interaction,
- Describe important units of analysis in BSE: individual, group, organisational levels as well as how they interact,
- Describe key cognitive biases and how they affect software developers,
- Explain models of team development and maturity and how they relate to BSE,
- Give an overview of recent, empirical research on BSE
- Analyse why an SE intervention, like a process improvement or the introduction of a new tool, failed or succeeded from a BSE perspective,
- Diagnose software teams based on their developmental maturity,
- Propose interventions to improve a software development team based on a BSE analysis,
- Identify cognitive biases that affect a particular developer or team,
- Design a SE study using research methods suited to BSE
- Analyse and hypothesize about sources of software project failures, and reflect on whether they are primarily because of technical or behavioral/human factors,
- Assess and discuss ethical aspects and concerns as well as sustainability in software development on an individual and societal level
Link to the syllabus on Studieportalen (Chalmers):
https://www.chalmers.se/en/education/your-studies/find-course-and-programme-syllabi/course-syllabus/DAT521/
and for GU:
Examination form
Individual- and group-based exercises and assignments are the basis for the examination (4.5 credits of the total 7.5 credits) of the course. A final, individual written hall examination is also part of the examination (3.0 credits of the total 7.5 credits). Both the assignments and the written examination are graded U/3/4/5 for Chalmers and for GU.
A total of 2 assignments are included in the course and they are all compulsory for passing the course:
- Assignment 1, individual (2 credits): Approved BSE paper presentation
- You will select one research paper that is clearly relevant to BSE and present it to the class.
- The paper must be approved by Lekshmi by email at least 10 days before your assigned presentation slot.
- Presentation slots are randomized by the teaching team and announced in Canvas.
- The presentation is given during the Wednesday afternoon slot, normally in the first hour.
- You will need to find one other student presenting in your slot that you will be the "opponent" on and pose questions to them about their paper and presentation.
- Examination: 8-10 minute presentation, followed by joint discussion with the class and teachers, plus submitted slides as PDF within 24 hours before the presentation.
- What is judged:
- The relevance and quality of the selected paper
- Your understanding of the paper and its main ideas
- How clearly you present the paper
- How well you connect it to the AI transformation of software engineering
- How well you answer questions and take part in the post-presentation discussion
- The submitted slides
- Assignment 2, group (2.5 credits): BSE, AI, and the future of software engineering
- Groups investigate a BSE-relevant topic that must explicitly discuss how recent AI developments affect SE and/or BSE.
- Groups may self-select, but the teaching team will help if needed. Groups must be formed by Thursday, April 2nd, 2026.
- The group topic must be approved by the teaching team by Thursday, April 9th, 2026 at 17:00.
- The written report is submitted once, with no resubmission after the presentation.
- Examination: 6-page report due Thursday, May 21st, 2026 at 17:00, a 15-minute presentation (20 minutes hard maximum), joint discussion and questions for approximately 15-20 minutes, and submitted slides as PDF within 24 hours before the presentation. What is judged:
- The report as a concise and credible basis for the work
- The use of relevant literature
- The quality of the presentation
- The slides
- How well the group answers questions and takes part in the discussion
- How well the report and presentation analyze the AI-related transformation angle
- How well interview results are used in the analysis and discussion
Mandatory course elements
The following elements are mandatory and must be completed in addition to obtaining sufficient scores on the judged parts:
- Assignment 1 presentation
- Attendance at at least 80% of classmates' individual presentations
- Attendance at both industry interviews
- Assignment 2 report submission
- Assignment 2 presentation
- Attendance at all group-presentation sessions
Assignment-specific rubrics are available for how each assignment is judged and graded. The rubric details 4 levels for each criterion which corresponds to 0, 1, 2, and 3 points. The points for each criterion are summed together to give the final score. For all judged output the points are then mapped to grades according to:
Chalmers and GU:
0 - 49% => Fail
50%-64% => 3
65%-79% => 4
80-100% => 5
The course is examined by a written hall examination and by multiple assignments and presentations during the course. Some of the assignments are carried out individually, and some are in small groups of normally 3-5 students.
The final, individual examination (3 credits) is a traditional written hall examination. The exam is closed-book. The only permitted aid is an English dictionary. The examination is on Wednesday, June 3rd, 2026, 08:30-12:30. Since the examination is closed-book, students need to prepare by reading the research papers that are the course material, the lecture slides, and the material and discussions from seminars and course meetings throughout the course.
Text or ideas cannot be plagiarised and the student must be clear and explicit on which material and sources their answers are based. The written exam is complemented with oral discussion with a teacher. Clear plagiarism which cannot be backed up by argumentation and answers in the oral discussion will lead to failing the course. This also goes for other written material and assignments produced by students during the course.
We allow the use of AI-based tools such as ChatGPT and similar in the assignment work. However, all submitted and presented material must be understood by the student(s), be checked by the student(s), and be possible to defend in questions and discussion afterwards. If a student or group cannot explain and stand by their submitted or presented material, that assignment can be failed. For Assignment 2, the report must explicitly disclose how AI-based tools were used, in a dedicated appendix. AI-based tools or other online resources are not permitted during the final written hall exam; the only permitted aid there is an English dictionary.