research-article

LogOpt: Static Feature Extraction from Source Code for Automated Catch Block Logging Prediction

Authors:

Sangeeta Lal,

Ashish SurekaAuthors Info & Claims

ISEC '16: Proceedings of the 9th India Software Engineering Conference

Pages 151 - 155

https://doi.org/10.1145/2856636.2856637

Published: 18 February 2016 Publication History

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Abstract

Software logging is an important software development practice which is used to trace important software execution points. This execution information can provide important insight to developer while software debugging. Inspite of many benefits logging is often done in an ad-hoc manner based only on knowledge and experience of software developer because of lack of formal guidelines and training required for making strategic logging decision. It is known that appropriate logging is beneficial for developers but inappropriate logging can have adverse effect on the system. Excessive logging can not only cause performance and cost overhead, it can also lessen the benefit of logging by producing tons of useless logs. Sparse logging can make logging ineffective by leaving out important information. In order to lessen the load of software developers and to improve the quality of software logging, in this work we propose 'LogOpt' tool to help developers in making informed logging decision. LogOpt uses static features from source code to make catch block logging decision. LogOpt is a machine learning based framework which learns the characteristics of logged and unlogged training instance to make informed logging decision. We manually analyze snippets of logged and unlogged source code and extracted 46 distinguishing features important in making logging decision. We evaluated LogOpt on two large open source projects Apache Tomcat and CloudStack (nearly 1.41M LOC). Results show that LogOpt is effective for automated logging task.

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Cited By

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Batoun MSayagh MAghili ROuni ALi H(2024)A literature review and existing challenges on software logging practicesEmpirical Software Engineering10.1007/s10664-024-10452-w29:4Online publication date: 18-Jun-2024
https://doi.org/10.1007/s10664-024-10452-w
Lal SSardana NSureka A(2021)Logging Analysis and Prediction in Open Source Java ProjectResearch Anthology on Usage and Development of Open Source Software10.4018/978-1-7998-9158-1.ch038(733-761)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-9158-1.ch038
Chen BJiang Z(2021)A Survey of Software Log InstrumentationACM Computing Surveys10.1145/344897654:4(1-34)Online publication date: 3-May-2021
https://dl.acm.org/doi/10.1145/3448976
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Index Terms

LogOpt: Static Feature Extraction from Source Code for Automated Catch Block Logging Prediction
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging

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Published In

ISEC '16: Proceedings of the 9th India Software Engineering Conference

February 2016

204 pages

ISBN:9781450340182

DOI:10.1145/2856636

General Chairs:
Santonu Sarkar
BITS Pilani, K.K. Birla Goa Campus
,
Ashish Sureka
ABB Corporate Research Center, India
,
Program Chairs:
Domenico Cotroneo
University of Naples
,
Nishant Sinha
IBM Research, India
,
Vibha S. Sinha
IBM Watson, Almaden Research Center, USA
,
Radhika Venkatasubramanyam
Siemens, Bangalore India
,
Padmaja Joshi
C-DAC, Mumbai
,
R. D. Naik
TCS
,
Pushpendra Singh
IIIT Delhi
,
Publications Chair:
Jayprakash Lalchandani
IIIT Bangalore

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ACM India: ACM India

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Association for Computing Machinery

New York, NY, United States

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Published: 18 February 2016

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ISEC '16

ISEC '16: 9th India Software Engineering Conference

February 18 - 20, 2016

Goa, India

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ISEC '16 Paper Acceptance Rate 25 of 127 submissions, 20%;

Overall Acceptance Rate 76 of 315 submissions, 24%

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Cited By

View all

Batoun MSayagh MAghili ROuni ALi H(2024)A literature review and existing challenges on software logging practicesEmpirical Software Engineering10.1007/s10664-024-10452-w29:4Online publication date: 18-Jun-2024
https://doi.org/10.1007/s10664-024-10452-w
Lal SSardana NSureka A(2021)Logging Analysis and Prediction in Open Source Java ProjectResearch Anthology on Usage and Development of Open Source Software10.4018/978-1-7998-9158-1.ch038(733-761)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-9158-1.ch038
Chen BJiang Z(2021)A Survey of Software Log InstrumentationACM Computing Surveys10.1145/344897654:4(1-34)Online publication date: 3-May-2021
https://dl.acm.org/doi/10.1145/3448976
Li HZhang HWang SHassan A(2021)Studying the Practices of Logging Exception Stack Traces in Open-Source Software ProjectsIEEE Transactions on Software Engineering10.1109/TSE.2021.3129688(1-1)Online publication date: 2021
https://doi.org/10.1109/TSE.2021.3129688
Gujral HLal SLi H(2021)An exploratory semantic analysis of logging questionsJournal of Software: Evolution and Process10.1002/smr.236133:7Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1002/smr.2361
Lal SSardana NSureka A(2020)Improving Logging Prediction on Imbalanced DatasetsCognitive Analytics10.4018/978-1-7998-2460-2.ch039(740-772)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-2460-2.ch039
Lal SSardana NSureka A(2018)Logging Analysis and Prediction in Open Source Java ProjectOptimizing Contemporary Application and Processes in Open Source Software10.4018/978-1-5225-5314-4.ch003(57-85)Online publication date: 2018
https://doi.org/10.4018/978-1-5225-5314-4.ch003
Li HChen TShang WHassan A(2018)Studying software logging using topic modelsEmpirical Software Engineering10.1007/s10664-018-9595-823:5(2655-2694)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s10664-018-9595-8
Kumar LRath SSureka AGorthi RSarkar SMedvidovic NKulkarni VKumar AJoshi PInverardi PSureka ASharma R(2017)Empirical Analysis on Effectiveness of Source Code Metrics for Predicting Change-PronenessProceedings of the 10th Innovations in Software Engineering Conference10.1145/3021460.3021461(4-14)Online publication date: 5-Feb-2017
https://dl.acm.org/doi/10.1145/3021460.3021461
Sureka ALal SSardana N(2016)Improving Logging Prediction on Imbalanced DatasetsInternational Journal of Open Source Software and Processes10.4018/IJOSSP.20160401037:2(43-71)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.4018/IJOSSP.2016040103
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A Survey on Automated Log Analysis for Reliability Engineering

Two Level Empirical Study of Logging Statements in Open Source Java Projects

Improving Logging Prediction on Imbalanced Datasets: A Case Study on Open Source Java Projects

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