Characterizing logging practices in open-source software

Proceedings - International Conference on Software Engineering

Volumn , Issue , 2012, Pages 102-112

  Yuan, Ding ^a,b   Park, Soyeon ^a   Zhou, Yuanyuan ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

empirical study; failure diagnosis; log message; log quality

Indexed keywords

EMPIRICAL STUDIES; FAILURE DIAGNOSIS; LOG MESSAGE; LOG QUALITY; OPEN-SOURCE SOFTWARES; PRODUCTION RUN; QUANTITATIVE CHARACTERISTICS;

PROGRAM COMPILERS; SOFTWARE ENGINEERING;

WELL LOGGING;

EID: 84864189557     PISSN: 02705257     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSE.2012.6227202     Document Type: Conference Paper

References (37)

1. ArcSight Log analysis. http://www.arcsight.com/Logger.
2. T. Ball and S. K. Rajamani. The SLAM project: debugging system software via static analysis. In Proceedings of the 29th ACM SIGPLAN-SIGACT symposium on Principles of programming languages (POPL), pages 1-3, 2002.
3. L. Baresi and M. Young. Toward translating design constraints to run-time assertions. Electron. Notes Theor. Comput. Sci., 116:73-84, January 2005.
4. M. Bruntink, A. v. Deursen, and T. Tourwé. Discovering faults in idiom-based exception handling. In Proceedings of the 28th international conference on Software engineering (ICSE), pages 242-251, 2006.
5. Cisco system log management. http://www.cisco.com/en/US/docs/voice ip comm/cucm/service/3-3-2/ccmsrvs/sssyslog.html.
6. Dell. Streamlined Troubleshooting with the Dell system E-Support tool. Dell Power Solutions, 2008.
7. EMC seen collecting and managing log as key driver for 94 percent of customers. http://www.rsa.com/press-release.aspx?id=7596.
8. D. Engler, B. Chelf, and A. Chou. Checking system rules using system-specific, programmer-written compiler extensions. In Proceedings of the 4th conference on Symposium on Operating System Design & Implementation (OSDI), pages 1-16, 2000.
9. D. Engler, D. Y. Chen, S. Hallem, A. Chou, and B. Chelf. Bugs as deviant behavior: A general approach to inferring errors in systems code. In Proceedings of the eighteenth ACM symposium on Operating systems principles (SOSP), pages 57-72, 2001.
10. M. Gabel, J. Yang, Y. Yu, M. Goldszmidt, and Z. Su. Scalable and systematic detection of buggy inconsistencies in source code. In Proceedings of the ACM international conference on Object oriented programming systems languages and applications (OOPSLA), pages 175-190, 2010.
11. M. Hauswirth and T. M. Chilimbi. Low-overhead memory leak detection using adaptive statistical profiling. In Proceedings of the 11th international conference on Architectural support for programming languages and operating systems, ASPLOS-XI, pages 156-164, 2004.
12. M.-C. Hsueh, T. K. Tsai, and R. K. Iyer. Fault injection techniques and tools. Computer, 30:75-82, 1997.
13. W. Jiang, C. Hu, S. Pasupathy, A. Kanevsky, Z. Li, and Y. Zhou. Understanding customer problem troubleshooting from storage system logs. In Proccedings of the 7th conference on File and storage technologies (FAST), pages 43-56, 2009.
14. B. W. Kernighan and R. Pike. The Practice of Programming. Addison-Wesley, 1999.
15. Z. Li, S. Lu, S. Myagmar, and Y. Zhou. CP-Miner: A Tool for Finding Copy-paste and Related Bugs in Operating System Code. In Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation (OSDI), pages 176-192, 2004.
16. Apache Logging Services - Log4j. http://logging.apache.org/log4j.
17. L. Mariani and F. Pastore. Automated identification of failure causes in system logs. In Proceedings of the 2008 19th International Symposium on Software Reliability Engineering (ISSRE), pages 117-126, 2008.
18. L. Mariani, F. Pastore, and M. Pezzè. A toolset for automated failure analysis. In Proceedings of the 31st International Conference on Software Engineering (ICSE), pages 563-566, 2009.
19. J. C. Munson and S. G. Elbaum. Code churn: A measure for estimating the impact of code change. In Proceedings of the International Conference on Software Maintenance (ICSM), pages 24-31, 1998.
20. N. Nagappan and T. Ball. Use of relative code churn measures to predict system defect density. In Proceedings of the 27th international conference on Software engineering (ICSE), pages 284-292, 2005.
21. NetApp. Proactive health management with auto-support. NetApp White Paper, 2007.
22. Netcraft report: Apache httpd is the most popular webserver. http://news.netcraft.com/archives/category/webserver-survey/.
23. Lessons from the success of ssh. http://www.cs.virginia.edu/~drl7x/ sshVsTelnetWeb3.pdf.
24. M. Pezzè and J. Wuttke. Automatic generation of runtime failure detectors from property templates. In B. H. Cheng, R. Lemos, H. Giese, P. Inverardi, and J. Magee, editors, Software Engineering for Self-Adaptive Systems, pages 223-240. Springer-Verlag, Berlin, Heidelberg, 2009.
25. SLOCCount: Source Lines Of Code Count. http://www.dwheeler.com/sloccount/ .
26. Splunk Log management. http://www.splunk.com/view/logmanagement/SP- CAAAC6F.
27. Squid bug report 3319. http://bugs.squid-cache.org/show-bug.cgi?id=3319.
28. RFC3164 - the BSD Syslog protocol. http://tools.ietf.org/html/rfc3164.
29. L. Tan, D. Yuan, G. Krishna, and Y. Zhou. /* iComment: Bugs or bad comments? */. In Proceedings of twenty-first ACM SIGOPS symposium on Operating systems principles (SOSP), pages 145-158, 2007.
30. Top 10 Enterprise database systems to consider. http://www.serverwatch. com/trends/article.php/3883441/Top-10-Enterprise-Database-Systems-to-Consider. htm.
31. D. Wessels. Squid: The Definitive Guide. O'Reilly, 2004.
32. W. Xu, L. Huang, A. Fox, D. Patterson, and M. I. Jordan. Detecting large-scale system problems by mining console logs. In Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles (SOSP), pages 117-132, 2009.
33. A. T. T. Ying, G. C. Murphy, R. Ng, and M. C. Chu-Carroll. Predicting source code changes by mining change history. IEEE Trans. Softw. Eng., 30:574-586, September 2004.
34. D. Yuan, H. Mai, W. Xiong, L. Tan, Y. Zhou, and S. Pasupathy. SherLog: Error diagnosis by connecting clues from runtime logs. In Proceedings of the fifteenth edition of ASPLOS on Architectural support for programming languages and operating systems (ASPLOS), pages 143-154, 2010.
35. D. Yuan, S. Park, P. Huang, Y. Liu, M. Lee, Y. Zhou, and S. Savage. Did you log the error? Characterizing and improving software error reporting. Technical Report.
36. D. Yuan, J. Zheng, S. Park, Y. Zhou, and S. Savage. Improving software diagnosability via log enhancement. In Proceedings of the sixteenth international conference on Architectural support for programming languages and operating systems (ASPLOS), pages 3-14, 2011.
37. A. Zeller. Isolating cause-effect chains from computer programs. In Proceedings of the 10th ACM SIGSOFT symposium on Foundations of software engineering, SIGSOFT'02/FSE-10, pages 1-10, 2002.