software logs are essential records generated during the functioning of softwaresystems, aiding in the identification of irregularities and prevention of system failures. Recently, deep learning models have garnered ...
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ISBN:
(纸本)9798350376975;9798350376968
software logs are essential records generated during the functioning of softwaresystems, aiding in the identification of irregularities and prevention of system failures. Recently, deep learning models have garnered significant interest among researchers due to their efficacy in detecting anomalies within software logs. This research paper constructs a novel dataset, consisting of three parts: two datasets derived from our software system, along with a publicly available dataset obtained from the LogHub platform. The extensive logs within the dataset undergo preprocessing to extract meaningful features. Furthermore, this study introduces a novel model named SMAC-LSTM, designed specifically for detecting anomalies in software logs. Sequential Model-based Algorithm Configuration (SMAC) is a suitable method for hyperparameter optimization and automated deep learning. SMAC-LSTM involves determining the optimal hyperparameter values for the LSTM model using the SMAC. Additionally, SMAC-LSTM combines the temporal dependency capturing ability of Long Short-Term Memory (LSTM) with a context-dependent mechanism achieved through a Bayesian optimization algorithm based on random forests. This fusion enhances the model's ability to detect subtle anomalies in time series data, which are frequently disregarded by conventional LSTM models. The thorough evaluation demonstrates the superior performance of SMAC-LSTM models compared to traditional deep learning models, showcasing significant enhancements in precision (98.63%), and recall (92.31%), with an F1-Score of 95.36%, outperforming all other models. These results underscore the potential of SMAC-LSTM in the realm of software log anomaly detection.
Feature models are the de-facto standard in product line engineering to capture the commonalities and variability of systems. However, feature models provide little user guidance during configuration and are unable to...
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The rapid evolution of autonomous and connected vehicles has led to their integration with numerous technologies and software, rendering them vulnerable targets for cybersecurity attacks. While efforts have traditiona...
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In today's fast-paced corporate environment, real-time data was essential for making educated decisions, but it was challenging to create dynamic dashboards with drill-down capabilities. This study investigated ho...
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Rich web-based applications are complex systems with multiple application elements running on diverse platforms distributed over different tiers. There are no UML-based modelling languages or tools catering for the sp...
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ML systems have become an essential tool for experts of many domains, data scientists and researchers, allowing them to find answers to many complex business questions starting from raw datasets. Nevertheless, the dev...
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ISBN:
(数字)9783031308260
ISBN:
(纸本)9783031308253;9783031308260
ML systems have become an essential tool for experts of many domains, data scientists and researchers, allowing them to find answers to many complex business questions starting from raw datasets. Nevertheless, the development of ML systems able to satisfy the stake-holders' needs requires an appropriate amount of knowledge about the ML domain. Over the years, several solutions have been proposed to automate the development of ML systems. However, an approach taking into account the new quality concerns needed by ML systems (like fairness, interpretability, privacy, and others) is still missing. In this paper, we propose a new engineering approach for the quality-based development of ML systems by realizing a workflow formalized as a software Product Line through Extended Feature Models to generate an ML System satisfying the required quality constraints. The proposed approach leverages an experimental environment that applies all the settings to enhance a given Quality Attribute, and selects the best one. The experimental environment is general and can be used for future quality methods' evaluations. Finally, we demonstrate the usefulness of our approach in the context of multi-class classification problem and fairness quality attribute.
While functionality and correctness of code has traditionally been the main focus of computing educators, quality aspects of code are getting increasingly more attention. High-quality code contributes to the maintaina...
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ISBN:
(纸本)9798400701382
While functionality and correctness of code has traditionally been the main focus of computing educators, quality aspects of code are getting increasingly more attention. High-quality code contributes to the maintainability of softwaresystems, and should therefore be a central aspect of computing education. We have conducted a systematic mapping study to give a broad overview of the research conducted in the field of code quality in an educational context. The study investigates paper characteristics, topics, research methods, and the targeted programming languages. We found 195 publications (1976-2022) on the topic in multiple databases, which we systematically coded to answer the research questions. This paper reports on the results and identifies developments, trends, and new opportunities for research in the field of code quality in computing education.
The research focuses on the contribution to wireless communications equipment for the development of practices. In this sense, the need arises from the lack of a logarithmic antenna prototype for academic practices;as...
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Based on the EU AI Act draft from November 2022 a team of data scientists, quality managers and legal experts set out to instantiate the AI Act for their project domain. To focus on the product and service relevant pa...
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ISBN:
(数字)9783031423079
ISBN:
(纸本)9783031423062;9783031423079
Based on the EU AI Act draft from November 2022 a team of data scientists, quality managers and legal experts set out to instantiate the AI Act for their project domain. To focus on the product and service relevant parts of the extensive EU Act, the Level of Done (LoD) layer approach was applied. Based on this LoD layer for the AI Act an evaluation was initiated with ongoing Machine Learning (ML) projects. This case study describes the method and approach on how the instantiation was done and provides a first insight into the LoD-application from an engineering perspective.
The proceedings contain 55 papers. The special focus in this conference is on systems, software and Services Process Improvement. The topics include: Sleepless in the Code: Exploring the Relationship Between Occupatio...
ISBN:
(纸本)9783031711411
The proceedings contain 55 papers. The special focus in this conference is on systems, software and Services Process Improvement. The topics include: Sleepless in the Code: Exploring the Relationship Between Occupational Anxiety and Sleep Patterns in the software Industry;how to Explain Artificial Intelligence to Humans: Learning from Quality Function Deployment;large Language Models for softwareengineering: A Systematic Mapping Study;REFIoT: A Framework to Combat Requirements engineering in IoT Applications and systems;Using Data Augmentation to Support AI-Based Requirements Evaluation in Large-Scale Projects;artificial Intelligence-Enabled Medical Device Standards: A Multidisciplinary Literature Review;toward the Development of a Method for Identifying Problems and Providing Strategies to Reduce Them in software Development Teams;investigating systems Modernisation: Approaches, Challenges and Risks;Analysing the Role of Generative AI in softwareengineering - Results from an MLR;Virtual Emergency Warnings via C-ITS – An Interdisciplinary Approach;AI-Driven Test Flow Generation from Semi-formal Functional Safety Requirements;Understanding the Implications: Critical Path Analysis vs Dependent Failure Analysis in ISO 26262 Safety Methodology;Towards the Development of a Data Security Risk Management Framework for Medical Device software AI Models;towards an Integrated Cybersecurity Framework for Small and Medium Enterprises;Situation Analysis for Railway Safety: Adapting SOTIF for Passive Crossings;leveraging Digital Twins for Smart Hydropower: A Pathway to Industry 4.0;A Proposal for ISO24089 Audit Methodology Before Type Approvals: Interface with Automotive SPICE® PAM4.0;A Proposal for Enhancing IEC 61508 Methodology for the β-Factor Estimation;Consistency for More Than One TARA and Security Element Out of Context Experiences.
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