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Logging Analysis and Prediction in Open Source Java Project

Logging Analysis and Prediction in Open Source Java Project

Sangeeta Lal, Neetu Sardana, Ashish Sureka
Copyright: © 2021 |Pages: 29
ISBN13: 9781799891581|ISBN10: 1799891585|EISBN13: 9781799891598
DOI: 10.4018/978-1-7998-9158-1.ch038
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MLA

Lal, Sangeeta, et al. "Logging Analysis and Prediction in Open Source Java Project." Research Anthology on Usage and Development of Open Source Software, edited by Information Resources Management Association, IGI Global, 2021, pp. 733-761. https://doi.org/10.4018/978-1-7998-9158-1.ch038

APA

Lal, S., Sardana, N., & Sureka, A. (2021). Logging Analysis and Prediction in Open Source Java Project. In I. Management Association (Ed.), Research Anthology on Usage and Development of Open Source Software (pp. 733-761). IGI Global. https://doi.org/10.4018/978-1-7998-9158-1.ch038

Chicago

Lal, Sangeeta, Neetu Sardana, and Ashish Sureka. "Logging Analysis and Prediction in Open Source Java Project." In Research Anthology on Usage and Development of Open Source Software, edited by Information Resources Management Association, 733-761. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-9158-1.ch038

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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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