SE /
Seminar
SE Seminar Schedule
This is the schedule for the SE division seminar in the academic year 2020 / 2021. Common types of seminars are trial talks (for upcoming conferences, defenses, etc.), presentations of ongoing research, lectures by academic guests, and other research talks.
| Date & Time | Presenter | Title | Talk Type | Talk Abstract | Location |
| 05.11.2021 (10:00) | Khan Mohammad Habibullah | Non-functional Requirements for Machine Learning: Understanding Current Use and Challenges in Industry | Post conference, and current research on NFRs for ML | Machine Learning (ML) is an application of Artificial Intelligence (AI) that uses big data to produce complex predictions and decision-making systems, which would be challenging to obtain otherwise. To ensure the success of ML-enabled systems, it is essential to be aware of certain qualities of ML solutions (performance, transparency, fairness), known from a Requirement Engineering (RE) perspective as non-functional requirements (NFRs). However, when systems involve ML, NFRs for traditional software may not apply in the same ways; some NFRs may become more prominent or less important; NFRs may be defined over the ML model, data, or the entire system; and NFRs for ML may be measured differently. In this work, we aim to understand the state-of-the-art and challenges of dealing with NFRs for ML in industry. We interviewed ten engineering practitioners working with NFRs and ML. We find examples of (1) the identification and measurement of NFRs for ML, (2) identification of more and less important NFRs for ML, and (3) the challenges associated with NFRs and ML in the industry. This knowledge paints a picture of how ML-related NFRs are treated in practice and helps to guide future RE for ML efforts. | Hybrid: Jupiter 473 and https://chalmers.zoom.us/j/68106313512 Password: 067177 |
| 05.11.2021 (10:45) | Christoph Laaber (Simula, Norway) | Variability of Microbenchmark Results and How to Deal with It | External Visitor | Performance variability is a well-known challenge in software systems research and in performance engineering practice. Microbenchmarks, a form of performance testing technique, is not immune to performance variability, which can lead to unreliable results from which one can not draw reliable conclusions. The reasons for performance variability are many-fold, e.g., the environment the benchmark is executed in, the way the benchmark is written, or the measurement methodology that is applied. In this talk, I will show empirical evidence on the extent of benchmark result variability and elaborate on two techniques that can help dealing with it. | Hybrid: Jupiter 473 and https://chalmers.zoom.us/j/68106313512 Password: 067177 |
| 19.11.2021 (10:00) | Hamdy Michael Ayas | The journey of migrating towards microservices | Conference presentation practice | We will showcase the evolutionary and iterative nature of the migration journey towards microservices at an architectural-level and system-implementation level. Also, we identify 18 detailed activities that take place in these levels, categorized in the four phases of 1) designing the architecture, 2) altering the system, 3) setting up supporting artifacts, and 4) implementing additional technical artifacts. | Hybrid: Jupiter 473 and https://chalmers.zoom.us/j/65639661065 Password: 701684 |
| 14.01.2022 (10:00) | |||||
| 28.01.2022 (10:00) | Peter Samoaa | ||||
| 11.02.2022 (10:00) | Weixing Zhang |
Registered students in 21 / 22:
- Hazem Samoaa
- Aiswarya Raj Munappy
- Weixing Zhang
- Hamdy Ayas
- Khan Mohammad Habibullah
- Linda Erlenhov