Project conducted for Seminar in Machine Learning for Software Engineering. Aim of our research was to explore possible directions of Deep Learning solutions for log detection in a snippet of code.
Q. Fu et al., “Where do developers log? An empirical study on logging practices in industry,” in Companion proceedings of the 36th international conference on software engineering, 2014, pp. 24–33. :
two large industrial systems (2.5M and 10.4M LOC, respectively) at Microsoft.
H. Li, W. Shang, Y. Zou, and A. E. Hassan, “Towards just-in-time suggestions for log changes,” Empirical Software Engineering, vol. 22, no. 4, pp. 1831–1865, 2017.:
a case study on four open source projects: Hadoop, Directory Server, Commons HttpClient, and Qpid. (We use regular expressions to identify the added and deleted logging statements across
commits (see on-line replication package for the used regular expressions13))
Q. Fu et al., “Where do developers log? An empirical study on logging practices in industry,” in Companion proceedings of the 36th international conference on software engineering, 2014, pp. 24–33. : two large industrial systems (2.5M and 10.4M LOC, respectively) at Microsoft.
H. Li, W. Shang, Y. Zou, and A. E. Hassan, “Towards just-in-time suggestions for log changes,” Empirical Software Engineering, vol. 22, no. 4, pp. 1831–1865, 2017.: a case study on four open source projects: Hadoop, Directory Server, Commons HttpClient, and Qpid. (We use regular expressions to identify the added and deleted logging statements across commits (see on-line replication package for the used regular expressions13))
Edit if more dataset sources are found