The proceedings contain 39 papers. The special focus in this conference is on Computer Safety, Reliability, and Security. The topics include: Making the case for safety of machine learning in highly automated driving;...
ISBN:
(纸本)9783319662831
The proceedings contain 39 papers. The special focus in this conference is on Computer Safety, Reliability, and Security. The topics include: Making the case for safety of machine learning in highly automated driving;a thought experiment on evolution of assurance cases —from a logical aspect;a thought experiment on evolution of assurance cases from a logical aspect;uniform model interface for assurance case integration with system models;integrated model-based development of system and safety cases;a web based GSN editor for multiple stakeholders;towards combined safety and security constraints analysis;reconciling systems-theoretic and component-centric methods for safety and security co-analysis;reconciling systems-theoretic and component-centric methods for safety and security co-analysis;increasing dependability in safety critical CPSs using reflective statecharts;a survey of hardware technologies for mixed-critical integration explored in the project EMC2;a model-based design approach;GSN support of mixed-criticality systems certification;concepts for reliable communication in a software-defined network architecture and combining safety and security analysis for industrial collaborative automation systems.
softwareengineering in the era of generative AI, large data sets and superfast pace of software development often tends to focus on technology, tools and methods, putting aside us, software engineers. In this column,...
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softwareengineering in the era of generative AI, large data sets and superfast pace of software development often tends to focus on technology, tools and methods, putting aside us, software engineers. In this column, we focus on softer aspects of softwareengineering and report from two conferences: 28th internationalconference on Evaluation and Assessment in softwareengineering (EASE 2024) and 18th ACM/IEEE international Symposium on Empirical softwareengineering and Measurement (ESEM 2024). The selection of papers provides a glimpse on handling privacy, documenting ethical considerations in AI models and trustworthy AI.
software fault prediction (SFP) is becoming increasingly important in softwareengineering, especially in service-oriented systems (SOS). This study investigates the effectiveness of using source code for fault predic...
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software fault prediction (SFP) is becoming increasingly important in softwareengineering, especially in service-oriented systems (SOS). This study investigates the effectiveness of using source code for fault prediction in SOS. It uses supervised machine learning algorithms such as random forest, decision tree, and support vector machine to improve error prediction accuracy. Feature extraction is used for more accurate analysis. The study highlights the strengths and weaknesses of these algorithms, providing insights into the prediction of malicious software in SOS. It aims to provide high-performance and reliable software architecture, and advance fault prediction models in SFP.
With the rapid development of cloud computing technology, softwareengineering and mobile application development have ushered in new opportunities and challenges. This paper analyzes the application of cloud computin...
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In order to create software that is reliable, efficient, and of the highest quality, it is imperative to predict and address bugs during the development stage. Early detection of faults is crucial;yet developing a cos...
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In order to create software that is reliable, efficient, and of the highest quality, it is imperative to predict and address bugs during the development stage. Early detection of faults is crucial;yet developing a cost-effective and successful advanced bug prediction model presents challenges. This research endeavor aims to achieve precise bug identification by exploring the utilization of various machine learning techniques on training and testing datasets. Multiple machine learning methods have been devised to identify and learn from software defects. This study employs machine learning techniques to conduct a comprehensive examination of software bug detection, offering valuable insights to the software industry. It synthesizes existing research on bug prediction, detailing different methods and highlighting their effectiveness, advantages, and limitations. This comprehensive analysis offers valuable guidance to researchers and software developers seeking to enhance bug detection methods for the creation of higher-quality software.
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