research-article

Logtracker: learning log revision behaviors proactively from software evolution history

Authors:

Teng WangAuthors Info & Claims

ICPC '18: Proceedings of the 26th Conference on Program Comprehension

Pages 178 - 188

https://doi.org/10.1145/3196321.3196328

Published: 28 May 2018 Publication History

Abstract

Log statements are widely used for postmortem debugging. Despite the importance of log messages, it is difficult for developers to establish good logging practices. There are two main reasons for this. First, there are no rigorous specifications or systematic processes to guide the practices of software logging. Second, logging code co-evolves with bug fixes or feature updates. While previous works on log enhancement have successfully focused on the first problem, they are hard to solve the latter. For taking the first step towards solving the second problem, this paper is inspired by code clones and assumes that logging code with similar context is pervasive in software and deserves similar modifications. To verify our assumptions, we conduct an empirical study on eight open-source projects. Based on the observation, we design and implement LogTracker, an automatic tool that can predict log revisions by mining the correlation between logging context and modifications. With an enhanced modeling of logging context, LogTracker is able to guide more intricate log revisions that cannot be covered by existing tools. We evaluate the effectiveness of LogTracker by applying it to the latest version of subject projects. The results of our experiments show that LogTracker can detect 199 instances of log revisions. So far, we have reported 25 of them, and 6 have been accepted.

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Cited By

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://dl.acm.org/doi/10.1007/s10664-024-10452-w
Huo YLee CSu YShan SLiu JLyu M(2023)EvLog: Identifying Anomalous Logs over Software Evolution2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE59848.2023.00018(391-402)Online publication date: 9-Oct-2023
https://doi.org/10.1109/ISSRE59848.2023.00018
Zhang NChen QZheng ZZou YBissyandé TKlein JBird CSarro F(2023)iASTMapper: An Iterative Similarity-Based Abstract Syntax Tree Mapping AlgorithmProceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE56229.2023.00178(863-874)Online publication date: 11-Nov-2023
https://dl.acm.org/doi/10.1109/ASE56229.2023.00178
Show More Cited By

Index Terms

Logtracker: learning log revision behaviors proactively from software evolution history
1. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Maintaining software
      2. Software evolution
  2. Software organization and properties
    1. Extra-functional properties
      1. Software reliability

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Published In

cover image ACM Conferences

ICPC '18: Proceedings of the 26th Conference on Program Comprehension

May 2018

423 pages

ISBN:9781450357142

DOI:10.1145/3196321

General Chair:
Foutse Khomh
École Polytechnique de Montréal, Canada
,
Program Chairs:
Chanchal K. Roy
University of Saskatchewan, Canada
,
Janet Siegmund
University of Passau, Germany

Copyright © 2018 ACM.

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Published: 28 May 2018

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ICSE '18: 40th International Conference on Software Engineering

May 28 - 29, 2018

Gothenburg, Sweden

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Cited By

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://dl.acm.org/doi/10.1007/s10664-024-10452-w
Huo YLee CSu YShan SLiu JLyu M(2023)EvLog: Identifying Anomalous Logs over Software Evolution2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE59848.2023.00018(391-402)Online publication date: 9-Oct-2023
https://doi.org/10.1109/ISSRE59848.2023.00018
Zhang NChen QZheng ZZou YBissyandé TKlein JBird CSarro F(2023)iASTMapper: An Iterative Similarity-Based Abstract Syntax Tree Mapping AlgorithmProceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE56229.2023.00178(863-874)Online publication date: 11-Nov-2023
https://dl.acm.org/doi/10.1109/ASE56229.2023.00178
Dong JLou YZhu QSun ZLi ZZhang WHao DDwyer MDamian DZeller A(2022)FIRAProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510069(970-981)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510003.3510069
Tang YSpektor AKhatchadourian RBagherzadeh M(2022)Automated evolution of feature logging statement levels using Git histories and degree of interestScience of Computer Programming10.1016/j.scico.2021.102724214:COnline publication date: 1-Feb-2022
https://dl.acm.org/doi/10.1016/j.scico.2021.102724
Fan YXia XLo DHassan AWang YLi S(2021)A Differential Testing Approach for Evaluating Abstract Syntax Tree Mapping AlgorithmsProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00108(1174-1185)Online publication date: 22-May-2021
https://dl.acm.org/doi/10.1109/ICSE43902.2021.00108
KUBOTA TAOTA NKONO K(2020)Logging Inter-Thread Data Dependencies in Linux KernelIEICE Transactions on Information and Systems10.1587/transinf.2019EDP7255E103.D:7(1633-1646)Online publication date: 1-Jul-2020
https://doi.org/10.1587/transinf.2019EDP7255
Li SNiu XJia ZLiao XWang JLi T(2020)Guiding log revisions by learning from software evolution historyEmpirical Software Engineering10.1007/s10664-019-09757-y25:3(2302-2340)Online publication date: 1-May-2020
https://dl.acm.org/doi/10.1007/s10664-019-09757-y
Zhi CYin JDeng SYe MFu MXie T(2019)An Exploratory Study of Logging Configuration Practice in Java2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME.2019.00079(459-469)Online publication date: Sep-2019
https://doi.org/10.1109/ICSME.2019.00079
Gupta MMandal ADasgupta GSerebrenik A(2018)Runtime Monitoring in Continuous Deployment by Differencing Execution Behavior ModelService-Oriented Computing10.1007/978-3-030-03596-9_58(812-827)Online publication date: 12-Nov-2018
https://dl.acm.org/doi/10.1007/978-3-030-03596-9_58

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