software sustainability has been a trending topic in the last decade in academia. Studies related to software sustainability propose models, frameworks, or practices that can be applied in the industry. But most of th...
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ISBN:
(纸本)9789897586477
software sustainability has been a trending topic in the last decade in academia. Studies related to software sustainability propose models, frameworks, or practices that can be applied in the industry. But most of these proposals are still not systematically adopted in the industry. therefore, there is an opportunity to create a structured meeting to support the concrete adoption of sustainability practices in software development. this paper aims to provide an overview of these frameworks and how they can help facilitate sustainability-driven meetings (SusDM). Seeking this, we present practical examples and a workflow to prepare the meeting by applying the existing sustainability frameworks in SusDM. As a position paper, our hypothesis is that the contributions of this meeting may be related to improving the knowledge of software developers on sustainable softwareengineering, discovering new sustainability requirements, prioritization, and implementing software sustainability practices.
Withthe widespread deployment of deep neural networks (DNNs), ensuring the reliability of DNN-based systems is of great importance. Serious reliability issues such as system failures can be caused by numerical defect...
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ISBN:
(纸本)9781665457019
Withthe widespread deployment of deep neural networks (DNNs), ensuring the reliability of DNN-based systems is of great importance. Serious reliability issues such as system failures can be caused by numerical defects, one of the most frequent defects in DNNs. To assure high reliability against numerical defects, in this paper, we propose the RANUM approach including novel techniques for three reliability assurance tasks: detection of potential numerical defects, confirmation of potential-defect feasibility, and suggestion of defect fixes. To the best of our knowledge, RANUM is the first approach that confirms potential-defect feasibility with failure-exhibiting tests and suggests fixes automatically. Extensive experiments on the benchmarks of 63 real-world DNN architectures show that RANUM outperforms state-of-the-art approaches across the three reliability assurance tasks. In addition, when the RANUM-generated fixes are compared with developers' fixes on opensource projects, in 37 out of 40 cases, RANUM-generated fixes are equivalent to or even better than human fixes.
this paper discusses a practical approach to learning the basic principles of control engineering using DSP-based motor control. Laboratory work utilising microcontrollers for signal processing usually requires studen...
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Research on system and software product line engineering (SPLE) and the community around it have been inspired by industrial applications. However, despite decades of research, industry is still struggling with adopti...
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ISBN:
(纸本)9798400705939
Research on system and software product line engineering (SPLE) and the community around it have been inspired by industrial applications. However, despite decades of research, industry is still struggling with adopting product line approaches and more generally with managing system variability. We argue that it is essential to better understand why this is the case. Particularly, we need to understand the current challenges industry is facing wrt. adopting SPLE practices, how far existing research helps industry practitioners to cope withtheir challenges, and where additional research would be required. We conducted a hybrid workshop at the 2023 Systems and software Product Line conference (SPLC) with over 30 participants from industry and academia. 9 companies from diverse domains and in different phases of SPLE adoption presented their context and perceived challenges. We grouped, discussed, and rated the relevance of the articulated challenges. We then formed clusters of relevant research topics to discuss existing literature as well as research opportunities. In this paper, we report the industry cases, the identified challenges and clusters of research topics, provide pointers to existing work, and discuss research opportunities. Withthis, we want to enable industry practitioners to become aware of typical challenges and find their way into the existing body of knowledge and to relevant fields of research.
the reuse and distribution of open-source software must be in compliance with its accompanying open-source license. In modern packaging ecosystems, maintaining such compliance is challenging because a package may have...
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ISBN:
(纸本)9798350329964
the reuse and distribution of open-source software must be in compliance with its accompanying open-source license. In modern packaging ecosystems, maintaining such compliance is challenging because a package may have a complex multi-layered dependency graph with many packages, any of which may have an incompatible license. Although prior research finds that license incompatibilities are prevalent, empirical evidence is still scarce in some modern packaging ecosystems (e.g., PyPI). It also remains unclear how developers remediate the license incompatibilities in the dependency graphs of their packages (including direct and transitive dependencies), let alone any automated approaches. To bridge this gap, we conduct a large-scale empirical study of license incompatibilities and their remediation practices in the PyPI ecosystem. We find that 7.27% of the PyPI package releases have license incompatibilities and 61.3% of them are caused by transitive dependencies, causing challenges in their remediation;for remediation, developers can apply one of the five strategies: migration, removal, pinning versions, changing their own licenses, and negotiation. Inspired by our findings, we propose SILENCE, an SMT-solver-based approach to recommend license incompatibility remediations with minimal costs in package dependency graph. Our evaluation shows that the remediations proposed by SILENCE can match 19 historical real-world cases (except for migrations not covered by an existing knowledge base) and have been accepted by five popular PyPI packages whose developers were previously unaware of their license incompatibilities.
With medical technological advances being developed in a rapid pace, the need for effective Scientific software Development (SSD), that can process, store and visualize medical data is ever growing. Particularly durin...
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Large Language Models (LLMs) like ChatGPT have gained significant attention because of their impressive capabilities, leading to a dramatic increase in their integration into intelligent softwareengineering. However,...
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ISBN:
(纸本)9798350395693;9798350395686
Large Language Models (LLMs) like ChatGPT have gained significant attention because of their impressive capabilities, leading to a dramatic increase in their integration into intelligent softwareengineering. However, their usage as a service with varying performance and price options presents a challenging trade-off between desired performance and the associated cost. To address this challenge, we propose CPLS, a framework that utilizes transfer learning and local search techniques for assigning intelligent softwareengineering jobs to LLM-based services. CPLS aims to minimize the total cost of LLM invocations while maximizing the overall accuracy. the framework first leverages knowledge from historical data across different projects to predict the probability of an LLM processing a query correctly. then, CPLS incorporates problem-specific rules into a local search algorithm to effectively generate Pareto optimal solutions based on the predicted accuracy and cost. To evaluate the proposed approach, we conduct extensive experiments on LLM-based log parsing, a typical software maintenance task. Our experimental results demonstrate that CPLS outperforms the baseline methods, providing solutions withthe highest accuracy in 14 out of 16 instances. Compared to the baselines, CPLS achieves an accuracy improvement ranging from 1.24% to 485.54%, or reduces costs by 15.21% to 89.09% while maintaining the highest accuracy achieved by the baselines.
Nowadays, the construction industry's digital transformation processes are challenging civil engineering education. In general, students and society underestimate the civil engineering field as an outdated and arc...
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Nowadays, the construction industry's digital transformation processes are challenging civil engineering education. In general, students and society underestimate the civil engineering field as an outdated and archaic knowledge area. Despite the defragmentation and heterogeneity of the sector, advances have been made on the path for Construction 4.0 and 5.0 scenarios. From this perspective, the CONSTRUCT-Gequaltec group of the Faculty of engineering of the University of Porto have been sponsoring research and technological development using hardware and software targeting construction management solutions. this paper presents the work in progress mainly connected to the curricular units regarding project management in the Construction Section of the Civil engineering Department. It also targets assumptions of some expected results across the research and innovation priorities from ECTP Innovative Built Environment and United Nations Sustainable Development Goals (SDGs). Two fundamental elements of the teaching practices are presented as the Construction Sites of the Future (Laboratory -Based Education initiative) and Digital products catalogue targeting Digital Twins in Construction (Project Based Learning initiative). the learning outcomes address increasing knowledge of innovative solutions, delivering skills to deploy 5.0 actions, and targeting humancentred and sustainable attitudes and values.
the measurement of software complexity is a critical component in the development of modern software systems, directly influencing maintainability, scalability, and reliability. As software projects grow in complexity...
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In modern software development projects, developer teams usually adopt an issue-driven approach to increase their productivity. the component of an issue report implicitly organize issues in a software project (e.g. d...
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ISBN:
(数字)9781665485104
ISBN:
(纸本)9781665485104
In modern software development projects, developer teams usually adopt an issue-driven approach to increase their productivity. the component of an issue report implicitly organize issues in a software project (e.g. defects, new feature requests, and tasks) into a group of issues that have similar characteristics. A component of an issue report is an important attribute needed to be identified in an issue triaging process. thus, assigning the correct component(s) to an issue is crucial in issue resolution. However, it is a challenging task since largescale projects contain a considerable amount of components (e.g. almost one-hundred components in the Bamboo project) and it can increase significantly as the project evolves over time. In this paper, we propose an approach that uses textual feature extraction and machine learning techniques with Binary Relevance (BR) to develop a component recommendation model to support the task of assigning component(s) to an issue. the empirical evaluation over 60,000 issue reports shows that our proposed models outperform the baseline benchmarks and other techniques by achieving on average 0.480 Precision@1, 0.616 Recall@3, 0.432 MAP, and 0.596 MRR.
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