The document discusses choosing an appropriate log level when adding a new logging statement. It finds that an ordinal regression model can effectively model log levels, achieving an AUC of 0.76-0.81 in within-project evaluation and 0.71-0.8 in cross-project evaluation. The most influential factors for determining log levels vary between projects and include metrics related to the logging statement, containing code block, and file as well as code change and historical change metrics.

1. Which log level should developers
choose for a new logging statement?
Journal-first Presentation | Empirical Software
Engineering
Heng Li Weiyi Shang Ahmed E.
Hassan

2. Logs are usually the only resource for
diagnosing field issues
2

3. A verbosity level can be assigned to
each logging statement
3
Log.info(“Log
message”)
Trace
Debu
g
Info
WarnErrorFatal

4. Log levels are used to disable some verbose
log messages while enable important ones
4
Trace
Debu
g
Info
WarnErrorFatal
Trace
Debu
g
Info
Warn
Error
Fatal Less verbose
levels
(higher levels)
More verbose
levels (lower
levels)

5. Log levels are used to disable some verbose
log messages while enable important ones
5
Trace
Debu
g
Info
Warn
Error
Fatal
System/module
level setting:
LogLevel = Warn
Less verbose
levels
(higher levels)
More verbose
levels (lower
levels)

6. Improper log levels can have many
negative impacts
6
“…tends to generate a lot of log noise…”
“These warnings worry users, especially first time users”

7. Researchers and industrial experts
highlighted the challenge of choosing proper
log levels
7
Developers spend much
efforts on adjusting log
levels
Severity levels are often
used inaccurately
[Oliner et al. CACM’12]
[Yuan et al. ICSE’12]

8. We want to suggest log levels when
developers add a new logging statement
8
Add a new
logging
statement
Which log
level should
be used?
+
Logger.level();

9. We study the code change history
of four subject systems
9
Over 2 M lines of code
Over 13 K logging statements
Added 17 K logging statements in history

10. Not single log level
dominates other log levels
13(0%)
2330(47%)
549(11%) 430(9%)
1655(33%)
185(4%)
1257(24%)
1839(35%)
1149(22%)
722(14%)
73(1%)
2(0%)
477(29%)
541(33%)
169(10%)
408(25%)
32(2%)
648(14%)
1603(36%)
985(22%)
459(10%)
799(18%)
2(0%)
0
1000
2000
0
500
1000
1500
2000
0
200
400
600
0
500
1000
1500
DirectoryServerHadoopHamaQpid
trace debug info warn error fatal
Log level
Numberoflogs
10Trace Debug Info Warn Error Fatal
Numberoflogs
95%

11. Developers spend much effort
adjusting log levels
11
Trace Debug Info Warn Error Fatal
Trace 0 25 2 0 0 0
Debug 16 9 41 3 7 1
Info 8 211 7 13 4 0
Warn 0 23 35 0 16 3
Error 0 12 23 23 2 4
Fatal 0 1 0 1 1 0
491 logging statements had at least
one log level change
Log level after changesInitial
log
level

12. Developers spend much effort
adjusting log levels
12
Trace Debug Info Warn Error Fatal
Trace 0 25 2 0 0 0
Debug 16 9 41 3 7 1
Info 8 211 7 13 4 0
Warn 0 23 35 0 16 3
Error 0 12 23 23 2 4
Fatal 0 1 0 1 1 0
72% of the log level changes
are from a higher level to a lower level
Log level after changesInitial
log
level

13. Developers spend much effort
adjusting log levels
13
Trace Debug Info Warn Error Fatal
Trace 0 25 2 0 0 0
Debug 16 9 41 3 7 1
Info 8 211 7 13 4 0
Warn 0 23 35 0 16 3
Error 0 12 23 23 2 4
Fatal 0 1 0 1 1 0
78% of the log level changes
are between adjacent levels
Log level after changesInitial
log
level

14. Developers spend much effort
adjusting log levels
14
Trace Debug Info Warn Error Fatal
Trace 0 25 2 0 0 0
Debug 16 9 41 3 7 1
Info 8 211 7 13 4 0
Warn 0 23 35 0 16 3
Error 0 12 23 23 2 4
Fatal 0 1 0 1 1 0
51% of the log level changes
are between “info” and “debug” levels
Log level after changesInitial
log
level

15. Which log level should developers choose
for a new logging statement?
15
RQ1: How well can we model the log
levels of logging statements?
RQ2: What are the important factors for
determining the log level of a logging
statement?

16. Which log level should developers choose
for a new logging statement?
16
RQ1: How well can we model the log
levels of logging statements?
RQ2: What are the important factors for
determining the log level of a logging
statement?

17. We derive 22 metrics from 5 dimensions
to model log levels
17
Logging statement
metrics
Containing block metrics
Containing file metrics
Code change metrics
Historical change metrics
The metrics are
extracted for each
logging statement,
at the time when the
logging statement is
added
We exclude the
logging statements
with subsequent log
level change

18. Order is important
18
Trace
Debug
Info
Warn
Error
Fatal
We use an Ordinal
Regression Model
for ordinal
responses
Higher
levels
Lower levels

19. Ordinal Regression Model
19
Logging statement
metrics
Containing block
metrics
Containing file metrics
Code change metrics
Historical change
metrics Trace
Debug
Info
Warn
Error
Fatal

20. Ordinal regression model can effectively
model log levels
20
0.76 0.78 0.81
0.75
0.4
0.5
0.6
0.7
0.8
0.9
1
Random
guess
AUC
The performance (AUC) of a within-project evaluation

21. Ordinal regression model can effectively
model log levels
21
0.76 0.78
0.81
0.750.72
0.76
0.8
0.71
0.4
0.5
0.6
0.7
0.8
0.9
1 Within-project Cross-project
The performance (AUC) of a cross-project
evaluation
AUC

22. Which log level should developers choose
for a new logging statement?
22
RQ1: How well can we model the log
levels of logging statements?
RQ2: What are the important factors for
determining the log level of a logging
statement?

23. We use a Wald 𝝌 𝟐 test to measure the
variable importance
23
Wald 𝝌 𝟐
Test
tests the significance of a variable against the null
hypothesis that the coefficient is equal to zero:
𝐻0: 𝜃 = 0
𝐻0: 𝜃0 = 𝜃1 = ⋯ = 𝜃 𝑘
Joint Wald 𝝌 𝟐 Test
tests the joint impact of a group
of
variables to a model’s fitness:

24. The most influential factors for log levels
are different for different projects
24
889
189
96
224
640
894
577
723
258
549
91
1134
5 0 0
78
0 67 0 0
0
500
1000
1500
The joint importance of each dimension of metrics
Logging statement metrics
Containing block metrics
Wald𝜒2statistic

25. The most influential factors for log levels
are different for different projects
25
889
189
96
224
640
894
577
723
258
549
91
1134
5 0 0
78
0 67 0 0
0
500
1000
1500
The joint importance of each dimension of metrics
Logging statement metrics
Containing block metrics
Wald𝜒2statistic

26. The most influential factors for log levels
are different for different projects
26
889
189
96
224
640
894
577
723
258
549
91
1134
5 0 0
78
0 67 0 0
0
500
1000
1500
The joint importance of each dimension of metrics
Logging statement metrics
Containing block metrics
Wald𝜒2statistic

27. 27
Log levels are used to disable some verbose
log messages while enable important ones
Trace
Debug
Info
Warn
Error
Fatal
System/ module level
setting:
LogLevel = W arn
Less verbose levels
(higher levels)
More verbose
levels (lower levels)

28. Log levels are used to disable some verbose
log messages while enable important ones
Trace
Debug
Info
Warn
Error
Fatal
System/ module level
setting:
LogLevel = W arn
Less verbose levels
(higher levels)
More verbose
levels (lower levels)
28

29. 29

30. Log levels are used to disable some verbose
log messages while enable important ones
Trace
Debug
Info
Warn
Error
Fatal
System/ module level
setting:
LogLevel = W arn
Less verbose levels
(higher levels)
More verbose
levels (lower levels)
30

31. 31
Ordinal regression model can effectively
model log levels
0.72
0.76
0.8
0.71
0.76 0.78
0.81
0.75
0.4
0.5
0.6
0.7
0.8
0.9
1 Within-project Cross-project
The performance (AUC) of a cross-project evaluation
AUC

32. Log levels are used to disable some verbose
log messages while enable important ones
Trace
Debug
Info
Warn
Error
Fatal
System/ module level
setting:
LogLevel = W arn
Less verbose levels
(higher levels)
More verbose
levels (lower levels)
32
Ordinal regression model can effectively
model log levels
0.72
0.76
0.8
0.71
0.76 0.78
0.81
0.75
0.4
0.5
0.6
0.7
0.8
0.9
1 Within-project Cross-project
The performance (AUC) of a cross-project evaluation
AUC

33. 33

34. Log levels are used to disable some verbose
log messages while enable important ones
Trace
Debug
Info
Warn
Error
Fatal
System/ module level
setting:
LogLevel = W arn
Less verbose levels
(higher levels)
More verbose
levels (lower levels)
34
Ordinal regression model can effectively
model log levels
0.72
0.76
0.8
0.71
0.76 0.78
0.81
0.75
0.4
0.5
0.6
0.7
0.8
0.9
1 Within-project Cross-project
The performance (AUC) of a cross-project evaluation
AUC
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hengli@cs.queensu.ca