Product-Focused Software Process Improvement

24th International Conference, PROFES 2023, Dornbirn, Austria, December 10–13, 2023, Proceedings, Part II

AvRegine Kadgien,Andreas Jedlitschka

Häftad, Engelska, 2023

Del 14484 i serien Lecture Notes in Computer Science

588 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2023-12-02
Mått:155 x 235 x 10 mm
Vikt:277 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:155
Förlag:Springer International Publishing AG
ISBN:9783031492686

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Innehållsförteckning

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.