Analyzing OSS Developers’ Working Time
Using Mailing Lists Archives
Masateru Tsunoda

Akito Monden

Takeshi Kakimoto

Nara Institute of Science andNara Institute of Science andNara Institute of Science andTechnologyTechnologyTechnology
Kansai Science City, 630-0192 Japan Kansai Science City, 630-0192 Japan Kansai Science City, 630-0192 Japanmasate-t@is.naist.jp

akito-m@is.naist.jp

takesi-k@is.naist.jp

Yasutaka Kamei

Ken-ichi Matsumoto

Nara Institute of Science andTechnology
Kansai Science City, 630-0192 Japan

Nara Institute of Science andTechnology
Kansai Science City, 630-0192 Japanyasuta-k@is.naist.jp

matumoto@is.naist.jp
Table 1. Description of Each ML

Categories and Subject Descriptors

List

K.6.1 [Management of Computing and Information Systems]:
Project and People Management – Staffing;

Description

committers

General Terms: Managementhackers

Keywords
Overtime work, workload

patches

1. INTRODUCTIONwww

We chose PostgreSQL, a relational database system for the MSR
mining challenge.

Notification of CVS commits aresent to this list.
Discussion of current development
issues, problems and bugs, andproposed new features.
Patches for new features and bug
fixes should be sent to this list.
Discussion of development andcoordination of the PostgreSQLwebsites.

Archived
fromApril
2000
January
1997
June
2000
August2003

Our research question is in the following mining area:-committershackers
patches
www20000

Process analysis

Our mining question is “when OSS developers work?” OSS developers’ working time may be a good indicator to understand thedevelopment style of a project. (For example, if many developers
work in office hour, these might be daily works in a company.)

15000

10000

2. INPUT DATA
We used mailing lists (MLs) archives of PostgreSQL, downloaded
from http://www.postgresql.org/community/lists/. The MLs
mainly consist of user lists and developer lists. We used developerlists archive since we needed developers’ working time. Table 1
explains details of each ML. Figure 1 shows amounts of messagesof each ML in the developer lists. Amounts of messages wereincreasing year by year. The ML of hackers had many more messages than other MLs. We extracted MLs archives till December
2005. Note that most of committers’ messages were automaticallygenerated when source code was checked into software configuration management repository.

500001997199920012003

2005

Figure 1. Amount of Messages in Each Year
We picked up “mail sent time” to identify developers’ workingtime. Getting mail sent time from the MLs archives consists of thefollowing two steps: First, we downloaded the MLs archives with

Copyright is held by the author/owner(s).
MSR’06, May 22–23, 2006, Shanghai, China.
ACM 1-59593-085-X/06/0005.

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10%

20%

committershackers
patches
www

8%15%6%4%

committershackers
patches
www

10%
2%
0%

5%
02

4

68

10

1214

16

1820

22

SunFigure 2. Ratio of Messages in Each Hour

40%
committershackers
patches
www30%

50%

40%
1997

TueW edThuFriSat

Figure 3. Ratio of Messages in Each Day of a Week

70%

60%

Mon

committershackers
patches
www20%

1999

2001

2003

10%
1997

2005

1999

2001

2003

2005

Figure 4. Ratio of Messages Sent at Overtime Period in EachYear

Figure 5. Ratio of Messages Sent on Weekend in Each Year

Irvine1, a web download tool. Then, we extracted mail sent timefrom the downloaded archives with a Perl script.

Ratio of messages sent at overtime period in each year is shown inFigure 4. The ratio of messages of committers was increasing year
by year while other ML groups did not show clear trends. This
may suggest that many committers are recently required to work atovertime period by some reasons (e.g. too many patches to inspect). Ratio of messages sent on weekend by year is shown in
Figure 5. In spite of the increase of committers’ messages sent atovertime period, committers’ messages sent on weekend did notincrease. This may suggest that even if committers are willing towork at overtime period, they do not want to work on weekend.

In our analysis, we mainly focused on the following aspects:-Mail sent hour-Days of a week of mail sent date

-Difference in hours and date among ML groups-Time trend

To see developer workload, we defined the overtime period. Overtime period includes before 9a.m. and after 5p.m. on weekday,
and all day of weekend. Because each ML has different amount ofmessages, we used ratio of messages, defined as amount of messages divided by total amount, of each ML group.

4. CONCLUSIONSWe analyzed mailing lists archives of PostgreSQL. We focused on
mail sent hour, days of a week of mail sent date, difference inhours and data among ML groups, and time trend. Our finding isthat the ratio of committers’ messages sent at overtime period wasincreasing year by year.

3. RESULTS AND INTERPRETATIONS
The ratio of messages in each hour is shown in Figure 2. The MLof hackers was active in the morning. On the contrary, ratio ofcommitters’ messages in the evening is comparatively higher thanother ML groups. The ratio of messages in each day of a week isshown in Figure 3. Most developers work on weekday. The ratioof committers’ messages on weekend is slightly higher than otherML groups.
15. ACKNOWLEDGMENTS
This work is supported by the EASE (Empirical Approach toSoftware Engineering) project of the Comprehensive Development of e-Society Foundation Software program of the Ministryof Education, Culture, Sports, Science and Technology of Japan.

http://hp.vector.co.jp/authors/VA024591/ (in Japanese)

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