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Abstract: As industrial PLC programs become more complex, automated testing and verification methods are needed to ensure their reliability and correctness. This paper presents PyLC+, a modular framework that translates PLC programs into Python, allowing for automated AI-driven test generation. PyLC+ builds upon our previous work, addressing limitations by adopting a class-based modular architecture that im...
Abstract: The increasing demand for autonomous machines in construction environments necessitates the development of robust object detection algorithms that can perform effectively across various weather and environmental conditions. However, challenging conditions at construction sites, such as mud splashes and vibrations, can degrade object detection performance by causing sensor occlusions and image blur...
Abstract: DevOps has significantly improved the software development life-cycle by enabling fast, automated, and continuous integration and deployment (CI/CD). However, its application to Cyber-Physical Systems (CPS) presents unique challenges that require rethinking of traditional practices. DevOps in CPS becomes even more challenging when we consider sustainability, which is becoming increasingly importan...
Abstract: The deployment of automated functions that can operate without direct human supervision has changed safety evaluation in domains seeking higher levels of automation. Unlike conventional systems that rely on human operators, these functions require new assessment frameworks to demonstrate that they do not introduce unacceptable risks under real-world conditions. To make a convincing safety claim, t...
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...
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