Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with ...
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In critical softwareengineering, structured assurance cases (ACs) are used to demonstrate how key properties (e.g., safety, security) are supported by evidence artifacts (e.g., test results, proofs). ACs can also be ...
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ISBN:
(纸本)9783031765537;9783031765544
In critical softwareengineering, structured assurance cases (ACs) are used to demonstrate how key properties (e.g., safety, security) are supported by evidence artifacts (e.g., test results, proofs). ACs can also be studied as formal objects in themselves, such that formal methods can be used to establish their correctness. Creating rigorous ACs is particularly challenging in the context of software product lines (SPLs), wherein a family of related software products is engineered simultaneously. Since creating individual ACs for each product is infeasible, AC development must be lifted to the level of product lines. In this work, we propose PLACIDUS, a methodology for integrating formal methods and software product line engineering to develop provably correct ACs for SPLs. To provide rigorous foundations for PLACIDUS, we define a variability-aware AC language and formalize its semantics using the proof assistant Lean. We provide tool support for PLACIDUS as part of an Eclipse-based model management framework. Finally, we demonstrate the feasibility of PLACIDUS by developing an AC for a product line of medical devices.
Requirements engineers have the responsibility for classifying software requirements into functional and nonfunctional variants. As software architects need quality requirements to be known to get their job done, mach...
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ISBN:
(纸本)9783031752001;9783031752018
Requirements engineers have the responsibility for classifying software requirements into functional and nonfunctional variants. As software architects need quality requirements to be known to get their job done, machine learning is employed to speed up and add consistency to the process of identifying and categorizing requirements so that effort may be spent more effectively. We experimented with the effects of different machine learning algorithms, as well as different pre-processing and feature selection techniques. It was determined that, for this application, stop words should not be removed and that performing lemmatization on words provides the most effective features for classification. Furthermore, after finalizing our choices of pre-processing techniques and algorithm to use, we proposed a modification to the Extensive Feature Selector by gathering the most distinctive words in each category and using a list of those as our main features. By using a threshold of 0.013, we obtained an F1 score of 0.787, which is an improvement on the base Enhanced Feature Selector's F1 score of 0.761 with the same number of word features.
The complex nature of compliance with industry regulations, along with the increasing requirements for Product Change Notifications (PCNs) and managing product complexity, poses challenges for component manufacturers ...
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ISBN:
(纸本)9783031720406;9783031720413
The complex nature of compliance with industry regulations, along with the increasing requirements for Product Change Notifications (PCNs) and managing product complexity, poses challenges for component manufacturers and machine builders in the engineering phase of product development working with up-to-date Digital Twin (DT) data. This situation leads to delays, potential compliance issues, and inefficiencies. Main reasons are the lack of interoperability PCNs, poor data quality, and the absence of direct traceability and tracking of product changes. To address these challenges, this paper proposes a solution approach that involves utilizing a Product Lifecycle Management (PLM) system and the Asset Administration Shell (AAS). Their design and mutual adjustment enhance collaboration, improve data quality, enable traceability, and finally, increase efficiency within engineering processes. The paper presents first results of a prototype implementation and outlines the next steps of the overarching research project.
Drones are emerging technologies in agriculture, replacing manual pesticide spraying due to harmful side effects on personnel. It is designed to reduce time and labor costs for farmers. This paper aims to develop a so...
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To mitigate the challenges posed by data uncertainty in Full-Self Driving (FSD) systems. This paper proposes a novel feature extraction learning model called Adaptive Region of Interest Optimized Pyramid Network (ARO)...
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The rapid expansion of Artificial Intelligence has driven a surge in the development of Machine Learning (ML) pipelines, essential for constructing and maintaining ML models. Despite the growing recognition of the imp...
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ISBN:
(纸本)9783031783852;9783031783869
The rapid expansion of Artificial Intelligence has driven a surge in the development of Machine Learning (ML) pipelines, essential for constructing and maintaining ML models. Despite the growing recognition of the importance of high-quality ML pipelines, there is limited actionable guidance on improving pipelines quality at the implementation level. The Object-Oriented (OO) paradigm and software design principles have been used in softwareengineering to enhance software system quality. In this context, this work explores the coverage of existing ML pipelines in relation to OO concepts and software design principles. Our findings reveal a limited use of OO mechanisms and minimal adherence to software design principles. Specifically, issues such as poor code organization and deficient levels of coupling and cohesion were observed. Our results can shed some light on the development of strategies to overcome these deficiencies for improving ML pipelines quality.
Semiotics is the discipline that studies the signs and the cognitive process of meaning-making. As part of semiotics studies, the idea of semiospheres has been formulated, representing spheres of meaning that do not e...
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An artificial intelligence (AI) system works by combining a computer program and algorithms to make a device more efficient and intelligent for tasks that are typically performed by humans. Deep learning, machine lear...
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The software industry is characterized by an environment of uncertainty, high volatility, and constant change. This context has shaped the industry, its components, and actors, generating methodologies capable of meet...
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