SEAA 2022
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SEAA 2022 Presentation Slides
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seaa22 software ml mde technical debt ai keynote knowledge representation risk modeling cpps industrial automation industry 4.0 process automation digital transformation testing microservices autonomous driving cps maintainability uml software architecture code analysis android empirical software engineering mapping study software api software testing java services cloud
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Risk and Engineering Knowledge Integration in Cyber-physical Production Systems Engineering

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Bad Smells in Industrial Automation: Sniffing out Feature Envy

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Software Architecture Challenges in Process Automation - From Code Generation to Cloud-native Service Orchestration

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From Traditional to Digital: How software, data and AI are transforming the embedded systems industry

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Exploiting dynamic analysis for architectural smell detection: a preliminary study

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On the Role of Personality Traits in Implementation Tasks: A Preliminary Investigation with Students

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An Empirical Analysis of Microservices Systems Using Consumer-Driven Contract Testing

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Have Java Production Methods Co-Evolved With Test Methods Properly?: A Fine-Grained Repository- Based Co-Evolution Analysis

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A Preliminary Conceptualization and Analysis on Automated Static Analysis Tools for Vulnerability Detection in Android Apps

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An Evaluation of Effort-Aware Fine-Grained Just-in-Time Defect Prediction Methods

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The Impact of Forced Working-From-Home on Code Technical Debt: An Industrial Case Study

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Service Classification through Machine Learning: Aiding in the Efficient Identification of Reusable Assets in Cloud Application Development

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Maintainability Challenges inML:ASLR

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Model-Driven Optimization: Generating Smart Mutation Operators for Multi-Objective Problems

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An Industrial Experience Report about Challenges from Continuous Monitoring, Improvement, and Deployment for Autonomous Driving Features

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API Deprecation: A Systematic Mapping Study

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MDEML_UMLsec4Edge Extending UMLsec to model data-protection-compliant edge computing systems.pdf

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EMMM: A Unified Meta-Model for Tracking Machine Learning Experiments

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Easing the Reuse of ML Solutions by Interactive Clustering-based Autotuning in Scientific Applications

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