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Improving Logging Prediction on Imbalanced Datasets: A Case Study on Open Source Java Projects
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Author(s): Sangeeta Lal (Jaypee Institute of Information Technology Noida, Department of CSE & IT, Noida, Uttar-Pradesh, India), Neetu Sardana (Jaypee Institute of Information Technology Noida, Department of CSE & IT, Noida, Uttar-Pradesh, India)and Ashish Sureka (ABB Corporate Research Center, Bangalore, India)
Copyright: 2020
Pages: 33
Source title:
Cognitive Analytics: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-2460-2.ch039
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Abstract
Logging is an important yet tough decision for OSS developers. Machine-learning models are useful in improving several steps of OSS development, including logging. Several recent studies propose machine-learning models to predict logged code construct. The prediction performances of these models are limited due to the class-imbalance problem since the number of logged code constructs is small as compared to non-logged code constructs. No previous study analyzes the class-imbalance problem for logged code construct prediction. The authors first analyze the performances of J48, RF, and SVM classifiers for catch-blocks and if-blocks logged code constructs prediction on imbalanced datasets. Second, the authors propose LogIm, an ensemble and threshold-based machine-learning model. Third, the authors evaluate the performance of LogIm on three open-source projects. On average, LogIm model improves the performance of baseline classifiers, J48, RF, and SVM, by 7.38%, 9.24%, and 4.6% for catch-blocks, and 12.11%, 14.95%, and 19.13% for if-blocks logging prediction.
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