SC Harvester Papers Database Interface

Rethinking MMLA: Design Considerations for Multimodal Learning Analytics Systems

Hamza Ouhaichi, Daniel Spikol, Bahtijar Vogel. In: Proceedings of the Tenth ACM Conference on Learning @ Scale. 2023

Abstract: Designing MMLA systems is a complex task requiring a wide range of considerations. In this paper, we identify key considerations that are essential for designing MMLA systems. These considerations include data management, human factors, sensors and modalities, learning scenarios, privacy and ethics, interpretation and feedback, and data collection. The implications of these considerations are twof...

Multi-Objective Optimization on Autoencoder for Feature Encoding and Attack Detection on Network Data

M. Leon, Tijana Markovic, S. Punnekkat. In: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. 2023

Abstract: There is a growing number of network attacks and the data on the network is more exposed than ever with the increased activity on the Internet. Applying Machine Learning (ML) techniques for cyber-security is a popular and effective approach to address this problem. However, the data which is used by ML algorithms have to be protected. In this paper, we present a framework that combines autoencoder...

Investigating ChatGPT’s Potential to Assist in Requirements Elicitation Processes

Krishna Ronanki, Christian Berger, J. Horkoff. In: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). 2023

Abstract: Natural Language Processing (NLP) for Requirements Engineering (RE) (NLP4RE) seeks to apply NLP tools, techniques, and resources to the RE process to increase the quality of the requirements. There is little research involving the utilization of Generative AI-based NLP tools and techniques for requirements elicitation. In recent times, Large Language Models (LLM) like ChatGPT have gained significa...

Search-Based Test Generation Targeting Non-Functional Quality Attributes of Android Apps

Teklit Gereziher, Selam Gebrekrstos, Gregory Gay. In: Proceedings of the Genetic and Evolutionary Computation Conference. 2023

Abstract: Mobile apps form a major proportion of the software marketplace and it is crucial to ensure that they meet both functional and nonfunctional quality thresholds. Automated test input generation can reduce the cost of the testing process. However, existing Android test generation approaches are focused on code coverage and cannot be customized to a tester's diverse goals---in particular, quality att...

Composite Hazard Analysis of System of Systems for Mixed-traffic Automation in Underground Mine

Nazakat Ali, S. Punnekkat. In: 2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN). 2023

Abstract: Hazard analysis for a single system focuses on identifying and evaluating potential hazards associated with the individual system, its components, and their interactions. There are well-established hazard analysis techniques that are widely used to identify hazards for single systems. However, unlike single systems, hazard analysis in a System of Systems (SoS) must focus on analyzing the potential...

Change-Point and Model Estimation with Heteroskedastic Noise and Unknown Model Structure

Anas Alhashimi, Thomas Nolte, A. Papadopoulos. In: 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT). 2023

Abstract: In this paper, we investigate the problem of modeling time-series as a process generated through (i) switching between several independent sub-models; (ii) where each sub-model has heteroskedastic noise, and (iii) a polynomial bias, describing nonlinear dependency on system input. First, we propose a generic nonlinear and heteroskedastic statistical model for the process. Then, we design Maximum L...

Human factors in developing automated vehicles: A requirements engineering perspective

Amna Pir Muhammad, E. Knauss, Jonas Bärgman. In: J. Syst. Softw.. 2023

Beyond Procurement: How Entur Navigated the Open Source Journey to Advance Public Transport

Daniel Rudmark, Juho Lindman, Andreas Tryti, Brede Dammen. In: IEEE Software. 2023

Abstract: This report describes how software professionals at the Norwegian public transport organization Entur use open source processes and tools to leverage digital transformation. Moving software acquisition from procurement to open source and in-house development can deliver value but also entails challenges....

Editorial

M. Staron. In: Inf. Softw. Technol.. 2023

Open Source Software: Communities and Quality

S. Abrahão, M. Staron, A. Serebrenik, B. Penzenstadler, Rafael Capilla. In: IEEE Softw.. 2023

A Case Study of Introducing Security Risk Assessment in Requirements Engineering in a Large Organization

Shanai Ardi, K. Sandahl, Mats Gustafsson. In: SN Computer Science. 2023

Abstract: Software products are increasingly used in critical infrastructures, and verifying the security of these products has become a necessary part of every software development project. Effective and practical methods and processes are needed by software vendors and infrastructure operators to meet the existing extensive demand for security. This article describes a lightweight security risk assessment...

Report on the Blended Modeling for Software Architectures Tutorial at ICSA 2023

Malvina Latifaj, Federico Ciccozzi, Muhammad Waseem Anwar, K. Aslam, I. Malavolta. In: ACM SIGSOFT Software Engineering Notes. 2023

Abstract: In this note, we report on a half-day tutorial designed to introduce software architecture practitioners and researchers to the concepts and open-source implementations of blended modeling for software architectures. The tutorial covered blended modeling motivation and principles, generation of editors, generation of the synchronization infrastructure, and collaborative modeling techniques. Throug...

Tiny Federated Learning with Bayesian Classifiers

N. Xiong, S. Punnekkat. In: 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE). 2023

Abstract: Tiny machine learning (TinyML) represents an emerging research direction that aims to realize machine learning on Internet of Things (IoT) devices. The current TinyML research seems to focus on supporting the deployment of deep learning models on microprocessors, while the models themselves are trained on high performance computers or clouds. However, in the resource/time constrained IoT contexts,...

The dynamic versus the stable team: The unspoken question in large‐scale agile development

Daniel Ståhl. In: Journal of Software: Evolution and Process. 2023

Abstract: The importance of the team, its internal dynamics, and its performance are widely recognized within the software engineering community. While popular frameworks identify wholeness, stability over time, and smallness as important factors, they offer little guidance on how to form teams that achieve these three characteristics. The objective of this study is to investigate how these team characteris...

Arguing Operational Safety for Mixed Traffic in Underground Mining

Julieth Patricia Castellanos Ardila, S. Punnekkat, H. Hansson, C. Grante. In: 2023 18th Annual System of Systems Engineering Conference (SoSe). 2023

Abstract: Practitioners report improved productivity as one of the main benefits of using autonomous dump trucks in underground mining. However, manned vehicles are still needed to transport materials and personnel in the tunnels, which requires practices that may diminish autonomy benefits. Thus, both fleets shall be efficiently mixed to maximize the autonomy potential. In addition, sufficient safety shall...