1. An Experience Report on Scaling Tools
for MSR Studies Using MapReduce
Weiyi Shang, Bram Adams, Ahmed E. Hassan
Software Analysis and Intelligence Lab (SAIL)
School of Computing, Queen’s University

2. Mining Software Repositories:
Propagating code changes
2
Method
A is
changed
Method
A calls
Method
B
Method
C calls
Method
A
Change
methods
B and C
Method
A is
changed
When method A is
changed, 90% of the
time method D is
changed.
Change
method
D
History
helps!

3. Traditional pipeline for MSR studies
Software
repositories
Data preparation (ETL)
Extraction
Transformation
Loading
Data
Warehouse
Data Analysis
3
Source
code
history
Bug
database
Mailing
list
System
log
Continues
to grow
More complex
algorithms
MSR studies must scale

4. Existing solutions to scale
powerful machines
ad hoc distributed computing
multi-threaded and multi-core
4

5. Example: D-CCFinder Clone Detector
40 days on 1 pc machine 52 hours on 80-
machines cluster
5

6. Web Analysis is similar to MSR
studies
Large-scale data Scan-centric Rapidly evolving
6

7. Web-scale platforms
7
We believe that the MSR field can benefit
from web-scale platforms to overcome
the limitations of current approaches.

8. In our previous research
8
Hadoop is up to 3 times faster
on a 4-machine cluster
Feasibility study using Hadoop to scale a
software evolution study on Eclipse.

9. In this paper
9
1. Does MapReduce scale to
other MSR studies and larger
clusters?
2. What are the challenges and
experiences of scaling MSR
studies?

10. ReduceMap
An example of MapReduce
Data
good
hello
fish
cat
school
night
happy
dog
ValueKey
dog3
cat3
fish4
good4
hello5
night5
happy5
school6
ValueKey
23
24
35
16
Counting the frequency of word lengths
10
Key
4
5
4
3
6
5
5
3

11. Three large-scale MSR studies
• Software evolution study
– J-REX: code-change information abstractor for
Java from line level to program entity level
• Code clone detection
– CC-Finder: code clone detection tool
• Log analysis
– JACK: log analysis tool for detecting system
anomalies during load testing
11

12. Experimental environment
CPU type #machines Memor
y size
Operating
system
Intel Quad
Core Q6600
(2.40 GHz)
18 3GB Ubuntu 8.04
8 Xeon (3.0
GHZ)
10 8GB CentOS 5.2
12

13. Input data
Data Size Data type #Files
Eclipse
Datatools
10.4 GB
227 MB
CVS repository
CVS repository
189,156
10,629
FreeBSD 5.1 GB source code 317,740
Log files No.1
Log files No.2
9.9 GB
2.1 GB
execution log
execution log
54
54
13

14. 1. Does MapReduce scale to other
MSR studies and larger clusters?
14

15. 98
580
0 200 400 600 800
SHARCNET(×10)
1 machine
min
80
755
0 200 400 600 800
SHARCNET(×10)
1 machine
Software Evolution & Log analysis
J-REX
JACK
×9
×6
min
15

16. Code clone detection
Can MapReduce scale up CCFinder ?
Yes!
58 hours on an 18-machine cluster.
16

17. 2. What are the challenges and
experiences of scaling MSR studies?
17

18. Challenge 1: Locality of MSR analysis
18
Local
analysis
Semi-local
analysis
Global
analysis
Web
MSR MSR MSR

19. Challenge 2: Granularity of MSR analysis
19
Fine-grained
analysis
Coarse-grained
analysis
• Web community experience:
– #Map: 10 ~ 100 × #
machines
– #Reduce: 0.95 or 1.75 ×
#CPU cores
• MSR experience:
– #Reduce tasks= #CPU cores
(fine-grained analysis)
– #Reduce task= #input
records (coarse-grained
analysis)Web
MSR MSR

20. Challenges of migrating MSR studies to
MapReduce
1. Locality of MSR analysis
2. Granularity of MSR analysis
3. Locating a suitable cluster
4. Managing data during analysis
5. Recovering from errors
20

21. 21
Questions?