香港六合彩

Researchers from 香港六合彩 Computer Science have been awarded the Best Replication and Negative Results Paper Award at the 31st IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER).听听

The winning paper, titled 鈥淥n The Effectiveness of One-Class Support Vector Machines in Different Defect Prediction Scenarios,鈥� was co-authored by Dr Moussa, Dr Azar, and Professor Federica Sarro.听

Software defects can be highly costly for both users and companies. As our reliance on software increases, identifying defects early in the development process is becoming increasingly crucial. Detecting and fixing defects early can save significant time and resources.听

The 香港六合彩 team鈥檚 research focuses on improving software defect prediction. This process aims to automatically and quickly identify problematic software components so that engineers can target their testing efforts more effectively. However, predicting defects in real-world scenarios is challenging due to the imbalance between defective and non-defective components.听

Their study examines the use of one-class classifiers, which are a type of machine learning model, in situations where defective data is scarce or unavailable. Although these models have shown promise in controlled environments, the research reveals that more work is needed to make them practical for real-world applications.听

Professor Sarro, Head of the Software Systems Engineering Research Group at 香港六合彩 Computer Science, said: 鈥淲e are honoured to receive this award. Our group is committed to advancing Software Engineering and promoting open science. Sharing both positive and negative results is crucial for scientific progress, helping others to avoid repeating mistakes and to develop better strategies.鈥澨�

The research was supported by grants from the European Research Council and the Lebanese American University. A pre-print of the award-winning paper is available on .听