The document discusses research into automatically generating work descriptions for software developers based on their interactions and artifacts. The researchers analyzed informal notes and comments from several projects involving over 1,000 developers total. They found that work descriptions contain common semantic elements like activities, artifacts, and problems. Descriptions also have predictable levels of detail. Based on these findings, the researchers believe heuristics and shared semantics can be used to automatically annotate developers' contexts and generate work descriptions.

1. MSR’2010, Cape Town,
South Africa, Mai 2010
Can Development Work
Describe Itself?
Walid Maalej, Technische Universität München
Hans‐Jörg Happel, FZI Research Center Karlsruhe

2. ExecuIve Summary
Informal notes that To a large extent,
describe developers’ work descripKons
work contain well‐ can be generated by
defined semanKcs, observing the work
granularity levels, and context of developers
informaKon paMers and their interacKons
1 2
Grounded Theory on Work DescripIons
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 2

3. Outline
1 MoIvaIon
2 Research SeRng
3 Research Results
4 Work DescripIon AutomaIon
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 3

4. What Are Work DescripIons?
Personal note
Commit message ArIfacts
Comments
including
work
descripIons
Social media Time sheet
A work descripIon is an informal text wriTen by a
knowledge worker to summarize achievements and
other notable issues of a parIcular work session
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 4

5. Previous Studies Showed InteresIng ProperIes
of Work DescripIons
Regularities in Content and
Effort and Quality Issues
Metadata
 5% of developers‘ Ime is  The overall vocabulary
spent for describing work usage seems to be
(30 min. per day ) predictable
 10% of the sessions have  The vocabulary size is
pseudo descripIons rather small
(either no Ime or not
 Different projects have
moIvaIon)
similar ranking of terms
To which extent can developers‘ work descriptions be automated?
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 5

6. Outline
1 MoIvaIon
2 Research SeRng
3 Research Results
4 Work DescripIon AutomaIon
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 6

7. Research QuesIons
Content of Work DescripIons
The semanIcs of informaIon
included in work descripIons
InformaIon EnIIes
InformaIon Granularity
Text fragments with similar The levels of detail included
semanIcs (abstracIon levels)
Occurrences
CombinaIons
Preferences
Levels
Causes
Which infor‐ Do certain
How are these What are Which properIes
maIon enIIes developers prefer
enIIes granularity effect the
are included and certain
combined? levels? granularity?
how o`en? informaIon?
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 7

8. Data Sets Collected in Different Contexts
Data set Summary Period Developers Entries
Developers‘ personal
MyComp notes at a German 2001 – 2009 25 38,005
soTware company
Commit messages
Apache and code comments 2001 – 2009 1,145 598,418
of all Apache projects
Commit messages
Unicase
and code comments 2008 – 2009 18 5097
of the unicase project
Personal notes in a
Eureka observaKonal study at 2008 21 91
5 companies
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 8

9. The Data Analysis Process
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 9

10. Outline
1 MoIvaIon
2 Research SeRng
3 Research Results
4 Work DescripIon AutomaIon
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 10

11. InformaIon EnIIes and Their Usage Frequencies
Occurrences %
Entity Average Apache Mycomp Unicase Eureka
Activity 71 69 76 71 67
Artifact 55 60 53 49 58
Problem 47 47 47 49 45
Rationale 28 30 29 25 31
New Work 24 24 20 28 22
Status 19 24 20 17 15
Reference 15 15 19 17 10
Solution 15 19 15 16 11
Experience 10 11 6 9 13
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 11

12. Findings on InformaIon EnIIes
1
The majority of informaKon on performed acKviKes (82%) is
combined with concerned arKfacts
2
InformaKon on problems is used to describe work done, work
need to be done, and the context of experiences
3
The combinaKon paMerns show that sharing knowledge and
managing work are two goals of work descripKons
4
There two clusters of developers: those who prefer to use
arKfacts and those who prefer to use problems to describe work
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 12

13. Granularity Levels and Usage Frequencies
Granularity Occurrences %
Level
Average Apache Mycomp Unicase Eureka
Implementation 54 58 37 62 60
Domain Project 31 29 34 29 30
Requirement 12 10 26 6 7
Method 33 33 49 28 20
Class 29 29 25 31 32
Object
Line 17 17 8 17 27
Component 15 14 16 19 10
Edit 53 55 41 57 60
Activity SE Process 36 34 42 30 39
Knowledge 12 13 15 11 9
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 13

14. Findings on InformaIon Granularity
1
The majority of work descripKons (62%) include informaKon from
a single granularity level
2
Developers think consistently (in a single abstracKon level) when
taking notes about arKfacts
3
The shorter the session is the more fine‐grained are the
described arKfacts
4
Levels of acKvity granularity overlap (edit, process and
knowledge)
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 14

15. Outline
1 MoIvaIon
2 Research SeRng
3 Research Results
4 Work DescripIon AutomaIon
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 15

16. Two Main Enablers For AutomaIng Work
DescripIons
AutomaKng Work DescripKon
Shared semanKcs of developers’ HeurisKcs derived from
working context, i.e. acKviKes, empirical findings on
arKfacts, and problems developers’ behavior
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 16

17. Shared SemanIcs to Annotate Context:
Developers’ InteracIons
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 17

18. Shared SemanIcs to Annotate Context
Developers’ ArIfacts
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 18

19. HeurisIcs to Generate Work DescripIons
Appropriate Granularity
• Guess the appropriate
Relevant vs. Irrelevant Context
level of detail
2 • Only a subset of artifacts
concerned by the interactions
1
Problem-Solution States
is included in the description
• Detect if a developer is Four factors to • Useful metrics are accumulated
encountering a problem, generate work usage duration, usage age, and
searching for a solution, or description usage frequency
applying a solution
3
• Indictors are are error messages,
breakpoint usage, searches, or 4
usage of particular keywords
Developers Preferences
• Learn from previous behavior of
developers and which information
they describe in which situation
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 19

20. Summary of the Talk
• InformaKon on acKviKes, arKfacts, problems, new
InformaIon work, and status is included for work management • Most informaIon
EnIIes
• InformaKon on soluKons, raKonale, and experience enIIes can be
is included to capture and share knowledge created
automaIcally by
observing
• There are different levels of domain, object, and
developer’s
InformaIon acKvity granularity
context
Granularity • These are used consistently and with common
paMerns • For that we
propose a set of
ontologies and
• Developers either think problem‐centered or
heurisIcs to be
Developers‘ arKfact‐centered when describing their work
used
Preference • They use well defined informaKon paMerns such as
<acKvity concerns arKfacts>
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 20

21. Feedback, QuesIons, SuggesIons
and CollaboraIon are Welcomed!
Walid Maalej Hans‐Jörg Happel
TUM FZI
maalejw@cs.tum.edu happel@fzi.de
© W. Maalej, Mai 2010 Analyzing Work DescripIon – MSR 2010 21