Malin Rosqvist

New video: Challenges and Recommendations of Data Visualization

Challenges and Recommendations of Data Visualization in Continuous Integration and Delivery Link to the recorded seminar on YouTube: Speaker: Azeem Ahmad, LiU Several operations, ranging from regular code updates to compiling, building, testing, and distribution to customers are consolidated in continuous integration and delivery (CI/CD). Professionals seek additional information to complete the mission at hand during these tasks. Developers who devote a large amount of time and effort to

New video: Data management and Data Pipelines

Data management and Data Pipelines: An empirical investigation in the embedded systems domain Candidate: Aiswarya Raj Munappy Link to the recorded presentation on YouTube: Context: Companies are increasingly collecting data from all possible sources to extract insights that help in data-driven decision-making. Increased data volume, variety, and velocity and the impact of poor quality data on the development of data products are leading companies to look for an improved

New video: Are you using the MNIST dataset to compare the algorithms?

Presenter: David Issa Mattos (PhD student, Chalmers) One of the most common tasks when developing a new tool is to benchmark it against competing tools. Both researchers and practitioners often look at the results of these benchmarks before selecting the appropriate tool. However, the quality of the benchmark greatly influences the results. In this presentation, we discuss how to evaluate if your benchmark has the appropriate difficulty level and is

New video: Collaborative Traceability

’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: Abstract: Traceability information connects the artifacts created in a development process and allows, i.a., analysing the impact of changes,

New video: Understanding Metrics Team-Stakeholder Communication

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: Analyzing software code using artificial neural networks

Title: Analyzing software code using artificial neural networks Speaker: Abu Naser Masud, Senior Lecturer, MDH Analyzing software code reveals many important aspects of the software such as bugs, security vulnerabilities, API recommendation, code similarity, code quality, etc. The dominant code analysis techniques include static and dynamic program analyses. However, artificial neural network (ANN) based source code analysis is a rising trend for the analysis of software. In this talk, I

New video: How to Fail at Continuous Practices

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

New video: Data Labeling

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

New video: Test Case Selection In The Presence of Class Noise

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