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Abstract: A key challenge in security analysis is the manual evaluation of potential security weaknesses generated by static application security testing (SAST) tools. Numerous false positives (FPs) in these reports reduce the effectiveness of security analysis. We propose using Large Language Models (LLMs) to improve the assessment of SAST findings. We investigate the ability of LLMs to reduce FPs while tr...
Abstract: Software development Effort Estimation (SEE) comprises predicting the most realistic amount of effort (e.g., in work hours) required to develop or maintain software based on incomplete, uncertain, and noisy input. Expert judgment is the dominant SEE strategy used in the industry. Yet, expert-based judgment can provide inaccurate effort estimates, leading to projects’ poor budget planning and cost ...
Abstract: Cyber-physical production systems increasingly involve collaborative robotic missions, which come with a higher demand for robustness and safety. Practitioners rely on risk assessments to identify potential failures and implement measures to mitigate their risks. Ensuring that mitigation strategies derived from risk assessments are adequately considered in the software implementation can be challe...
Abstract: Systems of Systems (SoS) face challenges related to coordinated management of the various tasks performed by constituent systems (CS), resource allocation, and SoS-level decision-making to achieve optimal performance related to costs and energy consumption. Addressing these challenges requires rigorous modeling and verification methods that accurately represent CS, capturing their interactions and...
Abstract: In many areas, independent and heterogeneous systems collaborate towards a common goal, and their assembly is referred to as a System of Systems (SoS). The mediating actors that orchestrate the SoS are frequently used to enhance collaboration. They support onboarding new constituent systems, monitoring and capability identification, goal transformation, workflow composition and execution, world mo...
Abstract: Explainable AI (XAI) is a promising route to comply with the EU AI Act, the first multinational AI regulation. XAI enhances transparency and human oversight of AI systems, especially''black-box`` models criticized as incomprehensible. Yet discourse about the AI Act's stakeholders and XAI remains disconnected: XAI increasingly prioritizes end users'needs, while the AI Act focuses on providers'and d...
Abstract: Software engineering in low-resourced countries is just gaining momentum. A considerable number of students enroll for the masters program at Makerere University in hope of achieving the successful promises that come with undertaking studies in software engineering. However, many find themselves not completing studies, especially during the research year. Based on interviews with 10 of the 24 stud...
Abstract: Artificial Intelligence (AI) is gradually transforming the landscape and operations of software startups by enabling innovation, improving decision making, and automating their business processes. However, software startups in the least developed countries (LDCs) like Uganda face a number of challenges that have hindered their abilities to adopt AI in their processes, products and services. In thi...
Abstract: A cross-sectional, questionnaire-based survey of software testing courses offered at Swedish universities was undertaken in the final quarter of 2023. With a return rate of 44%, the survey delved into the contents of these software testing courses to gain an understanding of how the courses differ in terms of depth and breadth of content. Information was also sought about administrative and course...
Abstract: The automation of Unified Modeling Language (UML) sequence diagram generation has posed a persistent challenge in software engineering, with existing approaches relying heavily on manual processes. Recent advancements in natural language processing (NLP), particularly through large language models (LLMs), offer promising solutions for automating this task. This paper investigates the use of LLMs i...
Abstract: Large Language Models (LLM) have shown stunning abilities to carry out tasks that were previously conducted by humans. The future role of humans and the responsibilities assigned to non-human LLMs affect society fundamentally. In that context, LLMs have often been compared to humans. However, it is surprisingly difficult to make a fair empirical comparison between humans and LLMs. To address those...
Abstract: A number of challenging aspects have to be considered, when the Internet of Things (IoT) and Artificial Intelligence (AI) are combined into intelligent IoT systems. A key aspect that demands high attention is trustworthiness. As part of the investigations we conduct in this area in collaboration with partner companies, the need of a holistic view for trustworthiness in Intelligent IoT systems has ...
Abstract: In our AI projects with machinery and plant engineering customers, we encounter recurring challenges beyond data processing, such as data availability, integration, human involvement, operations, and business considerations. Addressing these challenges is crucial for progress in this domain, yet research support is lacking. We present these challenges, discuss our current solutions, and call on re...
Abstract: The global environmental crises continue to get worse, fast approaching various irreversible thresholds. While a vast array of approaches to solving sustainability problems are found under the umbrella of Sustainable HCI, their contributions are sometimes hard to compare. In this essay, we describe a set of assumptions that influence what is considered meaningful and important areas of sustainabil...
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