News

’Collaborative Traceability — Nine practices and why they (don’t) work’ New recording from the Software Center Brown Bag seminar series,  May 17th. It is hosted by theme 4 (‘Customer Data and Ecosystem Driven Development’) and our speaker is Jan-Philipp Steghöfer, Chalmers/University of Gothenburg. Link to the recorded presentation on YouTube: https://youtu.be/AjfsRo8plvA Abstract: Traceability information connects the artifacts created in a development process and allows, i.a., analysing the impact of changes,

Software Center lunch seminar organized by Theme 3, Metrics: Title: Understanding Metrics Team-Stakeholder Communication Abstract: In our study, we explore challenges in communication between metrics teams and stakeholders in agile metrics service delivery. Drawing on interviews and interactive workshops with team members and stakeholders at two different Swedish software development organizations, we identify interrelated challenges such as aligning expectations, prioritizing demands, providing regular feedback, and maintaining continuous dialogue, which impede

New video from the Software Center Brown Bag Seminar series, hosted by Theme 1 (“Continuous Delivery”): Speaker: Daniel Ståhl We have all read the books, watched the movies and listened to the talks telling us how to succeed at continuous integration, continuous delivery and all things continuous. By all rights, this would seem to be a solved problem – and yet… In this brown bag seminar we turn the tables

Data Labeling: Industrial Challenges and Mitigation Strategies Labels are a prerequisite to perform supervised machine learning. However, in industrial contexts, data is often incomplete because labels are missing partially or entirely. Even if there exist manual, semi-automatic, and automatic techniques, such as crowdsourcing, active-learning (AL), and semi-supervised learning (SSL), we have seen that AL and SSL are rarely implemented due to lack of knowledge of their existence. Furthermore, labeling instances

Speaker: Khaled Al-Sabbagh Abstract: Machine learning models have been increasingly used to support decision making in software engineering tasks. One example of its application is the optimization of test case selection in continuous integration. Among the challenges that hinders the application of machine learning is the amount of noise that comes in the data, which often leads to a decrease in classification performances. For this reason, we examine the impact

March 8 brownbag seminar organized by the AI engineering theme in Software Center. This time our speaker is Hugo Sica de Andrade. Abstract: The requirements for performance continue to increase in computer systems across several domains. Particularly in artificial intelligence applications, several workloads require large amounts of memory, parallel computing, and low-precision computation. One of the most prominent ways of fulfilling these performance requirements is through heterogeneous computing, i.e., using