Product-Focused Software Process Improvement 24th International Conference, PROFES 2023, Dornbirn, Austria, December 10–13, 2023, Proceedings, Part II /
This book constitutes the refereed proceedings of the 24th International Conference on Product-Focused Software Process Improvement, PROFES 2023, which took place in Dornbirn, Austria, in December 2023. The 21 full technical papers, 6 industrial papers, 8 short papers and 1 poster paper were careful...
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| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
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Springer Nature Switzerland : Imprint: Springer,
2024.
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| Ausgabe: | 1st ed. 2024. |
| Schriftenreihe: | Lecture Notes in Computer Science,
14484 |
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| Online-Zugang: | https://doi.org/10.1007/978-3-031-49269-3 |
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Inhaltsangabe:
- Poster
- Metrics for Code Smells of ML Pipelines
- Tutorial
- Declarative Process Mining for Software Processes: the RuM toolkit and the Declare4Py Python library
- 2nd Workshop on Computational Intelligence and Software Engineering (CISE 2023)
- MaREA: Multi-class Random Forest for Automotive Intrusion Detection
- Forecasting The Developer's Impact in Managing the Technical Debt
- Development of a Desktop Application to Enable Doctors to Remotely Monitor Patients’ Hematological Parameters
- The significance of classical simulations in the adoption of quantum technologies for software development
- Enhancing Bug-Fixing Time Prediction with LSTM-Based Approach
- Enhancing Code Obfuscation Techniques: Exploring the Impact of Artificial Intelligence on Malware Detection
- A perspective on the interplay between 5G and quantum computing for secure algorithm
- Speech Therapy supported by AI and smart assistants
- 2nd Workshop on Engineering Processes and Practices for Quantum Software (PPQS’ 23)
- Integration of Classical and Quantum Services Using an Enterprise Service Bus
- Problem decomposition to leverage quantum computing for optimization problems
- Quantum Algorithm Cards: Streamlining the development of hybrid classical-quantum applications
- Doctoral Symposium
- Simulation-Based Safety Testing of Automated Driving Systems
- Utilization of Machine Learning for the detection of self-admitted vulnerabilities
- Closing the Loop: Towards a Complete Metamorphic Testing Pipeline.