Logging Analysis and Prediction in Open Source Java Project

Logging Analysis and Prediction in Open Source Java Project

Sangeeta Lal (Jaypee Institute of Information Technology, India), Neetu Sardana (Jaypee Institute of Information Technology, India) and Ashish Sureka (Ashoka University, India)
DOI: 10.4018/978-1-5225-5314-4.ch003

Abstract

Log statements present in source code provide important information to the software developers because they are useful in various software development activities such as debugging, anomaly detection, and remote issue resolution. Most of the previous studies on logging analysis and prediction provide insights and results after analyzing only a few code constructs. In this chapter, the authors perform an in-depth, focused, and large-scale analysis of logging code constructs at two levels: the file level and catch-blocks level. They answer several research questions related to statistical and content analysis. Statistical and content analysis reveals the presence of differentiating properties among logged and nonlogged code constructs. Based on these findings, the authors propose a machine-learning-based model for catch-blocks logging prediction. The machine-learning-based model is found to be effective in catch-blocks logging prediction.
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This section presents the closely related work and the novel research contributions of the study presented in this chapter in context to existing work. The authors categorize the related work in three dimensions: 1) improving source code logging, 2) uses of logging statements in other applications, and 3) applications of LDA in topic identification.

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