MSR’2010,
Cape
Town,

South
Africa,
Mai
2010





                                  Can
Development
Work

                ...
ExecuIve
Summary



          Informal
notes
that
                             To
a
large
extent,

          describe
deve...
Outline


            1
           MoIvaIon



            2
           Research
SeRng



            3
           Researc...
What
Are
Work
DescripIons?


                                      Personal
note



       Commit
message
                ...
Previous
Studies
Showed
InteresIng
ProperIes

                of
Work
DescripIons

                                   

  ...
Outline


            1
           MoIvaIon



            2
           Research
SeRng



            3
           Researc...
Research
QuesIons
                                                  


                                          
Content
...
Data
Sets
Collected
in
Different
Contexts


 Data
set
       Summary
                 Period
                     Developer...
The
Data
Analysis
Process
                                                 





©
W.
Maalej,
Mai
2010
       Analyzing
Wo...
Outline


            1
           MoIvaIon



            2
           Research
SeRng



            3
           Researc...
InformaIon
EnIIes
and
Their
Usage
Frequencies

                                              


                         O...
Findings
on
InformaIon
EnIIes
                                                

           1

                 The
majorit...
Granularity
Levels
and
Usage
Frequencies
                                                 


             Granularity     ...
Findings
on
InformaIon
Granularity

           1

                 The
majority
of
work
descripKons
(62%)
include
informaK...
Outline


            1
           MoIvaIon



            2
           Research
SeRng



            3
           Researc...
Two
Main
Enablers
For
AutomaIng
Work

                      DescripIons 


                           AutomaKng
Work
Descr...
Shared
SemanIcs
to
Annotate
Context:

                   Developers’
InteracIons
                                         ...
Shared
SemanIcs
to
Annotate
Context
                                               

                     Developers’
ArIf...
HeurisIcs
to
Generate
Work
DescripIons
                                                 

                                ...
Summary
of
the
Talk


                         •  InformaKon
on
acKviKes,
arKfacts,
problems,
new


   InformaIon
        ...
Feedback,
QuesIons,
SuggesIons

                  and
CollaboraIon
are
Welcomed!

                   Walid
Maalej

       ...
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Can Development Work Describe Itself?

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Work descriptions are informal notes taken by developers to summarize work achieved in a particular session. Existing studies indicate that maintaining them is a distracting task, which costs a developer more than 30 min. a day. The goal of this research is to analyze the purposes of work descriptions, and find out if automated tools can assist developers in efficiently creating them. For this, we mine a large dataset of heterogeneous work descriptions from open source and commercial projects. We analyze the semantics of these documents and identify common information entities and granularity levels. Information on performed actions, concerned artifacts, references and new work, shows the work management purpose of work descriptions. Information on problems, rationale and experience shows their knowledge sharing purpose. We discuss how work description information, in particular information used for work management, can be generated by observing developers' interactions. Our findings have many implications for next generation software engineering tools.

Paper: Walid Maalej and Hans-Jörg Happel, Can Development Work Describe Itself? In Proceedings of the 7th IEEE Conference on Mining Software Repositories, IEEE CS, 2010.

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Can Development Work Describe Itself?

  1. 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. 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. 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. 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. 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. 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. 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. 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. 9. The
Data
Analysis
Process 
 ©
W.
Maalej,
Mai
2010
 Analyzing
Work
DescripIon
–
MSR
2010
 9
  10. 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. 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. 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. 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. 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. 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. 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. 17. Shared
SemanIcs
to
Annotate
Context:
 Developers’
InteracIons 
 ©
W.
Maalej,
Mai
2010
 Analyzing
Work
DescripIon
–
MSR
2010
 17
  18. 18. Shared
SemanIcs
to
Annotate
Context 
 Developers’
ArIfacts 
 ©
W.
Maalej,
Mai
2010
 Analyzing
Work
DescripIon
–
MSR
2010
 18
  19. 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. 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. 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

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