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Paper on PyBugHive bug database published in IEEE Access

September 13, 2024

The article titled “PyBugHive: A Comprehensive Database of Manually Validated, Reproducible Python Bugs” written by Gábor Antal, Norbert Vándor, István Kolláth, Balázs Mosolygó, Péter Hegedűs, and Rudolf Ferenc (Department of Software Engineering) has been accepted into the Q1 journal IEEE Access.

Page last modified: September 13, 2024

Paper accepted into the Q1 Journal “Quantum Machine Intelligence”

July 26, 2024

The article titled “SoK: quantum computing methods for machine learning optimization” authored by Dr. Hamza Baniata (IoT-Cloud Group, Department of Software Engineering) has been accepted into Springer Nature’s Q1 journal Quantum Machine Intelligence.

Page last modified: July 26, 2024

Beszédes Á, Horváth F, Di Penta M, Gyimóthy T. 2020. Leveraging Contextual Information from Function Call Chains to Improve Fault Localization. 2020 IEEE 27th International Conference on Software Analysis, Evolution and Reengineering (SANER). :468-479.

Page last modified: July 8, 2024

Paper on PyBugHive bug database published in IEEE Access

Paper accepted into the Q1 Journal “Quantum Machine Intelligence”

Code Coverage Differences of Java Bytecode and Source Code Instrumentation Tools

A Mobile IoT Device Simulator for IoT-Fog-Cloud Systems

Deep learning in static, metric-based bug prediction

Leveraging Contextual Information from Function Call Chains to Improve Fault Localization

Elírások automatikus detektálása és javítása radiológiai leletek szövegében

An automatically created novel bug dataset and its validation in bug prediction

A public unified bug dataset for java and its assessment regarding metrics and bug prediction

Differences between a static and a dynamic test-to-code traceability recovery method

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