1. Analyzing & visualizing spreadsheets
Felienne Hermans (@felienne)

2. Analyzing & visualizing spreadsheets
Felienne Hermans (@felienne)
In this slidedeck I present an
overview of my PhD research. I
recently defended my dissertation
titled ‘Analyzing and visualizing
Spreadsheets’

3. In this slidedeck I present an
overview of my PhD research. I
recently defended my dissertation
titled ‘Analyzing and visualizing
Spreadsheets’
 This one!

4. Bridging the gap
Funny story: I wasn’t hired to
research spreadsheets at all. When
I started my PhD project, I was
supposed to research the gap
between business users and
programmers.
Users
Programmers

5. To research this gap, I started by
studying business in practice

6. What surprised me, is that this gap
wasn’t that big, it was more like a
small creek than a huge cliff.
Some programmers were heavilly
involved in business, and even more
interesting: some business guys were
doing serious programming.
Programmers
Users

7. What surprised me, is that this gap
wasn’t that big, it was more like a
small creek than a huge cliff.
Some programmers were heavilly
involved in business, and even more
interesting: some business guys were
doing serious programming.
In Excel!
Programmers
Users

8. What surprised me, is that this gap
wasn’t that big, it was more like a
small creek than a huge cliff.
Some programmers were heavilly
involved in business, and even more
interesting: some business guys were
doing serious programming.
In Excel!
So I looked into some previous work
on the impact of spreadsheets on
business.
Programmers
Users

9. 95% of all U.S. firms use spreadsheets for
financial reporting

10. 90% of all analysts in industry perform
calculations in spreadsheets

11. 50% of spreadsheets form the basis for
decisions

12. Importance can grow over time
When studying the impact of
spreadsheets, we found that they
do not become important
overnight. As processes change,
spreadsheets can become key
company assets over time.
Nobody sets out to create a mission
critical spreadsheet, they “just
happen”

13. This is a simple spreadsheet for many
users
Furthermore, spreadsheets can
become surprisingly complex.

14. And, spreadsheet exist
‘under the radar’
Another interesting property of
spreadsheets is that they often live
‘under the radar’:
There is no list of spreadsheets, no
one keeps track of what sheets are
needed for what report and some
spreadsheets do not have a clear
owner.

15. Only 33% of spreadsheets has
a manual
Finally, spreadsheets are lacking
documentation. In only one third of
spreadsheets we found
‘documentation’ (i.e. Some sort of
explanation on how to use the
spreadsheet) Technical
documentation, explaining why a
spreadsheet was designed as it is,
was hardly ever found.

16. Complex spreadsheets without
documentation can lead to serious errors
You can imagine the combination
of all the above facts:
• Spreadsheets are important
• They are complex
• They lack documentation
is a potential recipe for disaster.
And indeed, those errors happen

17. The European Spreadsheet Risk Interest
Group (Eusprig.org) collects horror stories

27. Estimated loss: 10 billion dollars a year

28. We interviewed spreadsheet
professionals
Once I had studied related
spreadsheet work and the horror
stories from Eusprig, I wanted to
gain a deeper understanding of
spreadsheet problems in practice.
So I interviewed 27 spreadsheet
professionals at the Dutch Robeco
bank.

29. We interviewed spreadsheet
professionals
Once I had studied related
spreadsheet work and the horror
stories from Eusprig, I wanted to
gain a deeper understanding of
spreadsheet problems in practice.
So I interviewed 27 spreadsheet
professionals at the Dutch Robeco
bank.
I asked only two questions (a semi-
structured interview) to obtain an
overall view of spreadsheet
problems:

30. What annoys you?

31. And what makes you happy?

32. Financial professionals spend 2 days a
week working with Excel
From the interviews, we learned the
following facts

33. Spreadsheets can have a long life,
5 years on average

34. Average sheet is used by 12 different
people

35. There is a gap! Between importance and
treatment.
Then I concluded that there is an
interesting gap that needs
bridging:
the gap between how important
spreadsheets are and how well
they are treated.
So how could this gap be bridged?

36. It looks like software in the 70s!
Let’s summarize the problems
around spreadsheets again:
• They lack documentation
• They contain errors
• They stay alive for several years
and are used by several people
• They are complex
Does this remind you of
something?
It reminded me of the problems in
the early days of software

37. Hence, we tried to bridge this gap with
methods from software engineering.

38. Spreadsheet users lack great tool
support
If you compare the tooling of
spreadsheet developers with that
of software developers, the
difference is clear.

39. Modern IDEs (like Visual Studio)
have all kinds of build-in tools to
help you build software in a
responsible way: debugging,
testing, analyzing and visualizing
are accessible at the click of a
button.

40. Compare this to a spreadsheet
environment, like Excel. Lots of
support to create a spreadsheet,
with fonts and colors and borders,
but none of the helpful tools to
build a maintainable spreadsheet.

41. We did not start coding immediately
However tempting, we did not start
to build a spreadsheet IDE
immediately. Instead, we looked
at the results of the interviews, to
find the most pressing information
need that spreadsheet users had.

42. Most important problem: support for
understanding spreadsheets was missing

43. To address this information need
specifically, we developed our
tool Breviz.
This tool visualizes the
dependencies among worksheets,
depicted as rectangles with arrows
drawn between them. The thicker
the arrow, the more connections
there are.
Example: In worksheet ‘POA
Project’ formulas are placed that
refer to cells in ‘ProjectTeam’

44. We went back to practice
With our tool, we went back to
practice, to see whether it really
supported spreadsheet users.

45. Turned out, it did. Some of the
responses of users:
“This diagram
reminds me of
what I had in mind
when building”

46. Turned out, it did. Some of the
responses of users:
This remark is interesting:
apparently, this spreadsheet user
did do some modeling before
building a spreadsheet.
“This diagram
reminds me of
what I had in mind
when building”

47. Turned out, it did. Some of the
responses of users:
A clear sign that we were on the
right track!
“This makes my job
10 times easier”

48. This work was published
at ICSE 2011

49. However, unexpected things also
happened. Not all spreadsheets
looked as well structured as this
one.
Let’s look at some of them:

52. Here, pink blocks represent
worksheets outside of the
spreadsheet. So this spreadsheet
gathers information from over 20
other worksheets and combines
this information.

53. Users diagnosed with the diagrams
We found that, due to the diversity
on the diagrams, users started to
judge spreadsheets based on their
dataflow diagrams.
We therefore formalized this
feeling users had into ‘smells’ at
the design level.
These spreadsheet smells turned
out to be very similar to code
smells as defined by Fowler.

54. Consider for instance the ‘feature
envy’ smell. This occurs when a
method from class B refers to
many fields outside its own class.
This method envies all the cool
fields that A has, hence the name.

55. Consider for instance the ‘feature
envy’ smell. This occurs when a
method from class B refers to
many fields outside its own class.
This method envies all the cool
fields that A has, hence the name.
Easy to see how this smell could
be defined on spreadsheets,
where a formula in worksheet B
could be overly interested in cells
on worksheet A.

56. We added support in Breviz for
detecting and visualizing these
inter-worksheet code smells.

57. We went back to practice
Next, of course, we went back to
practice, to see how users felt
about the detected smells.

58. “That
should be
improved”
Results showed that users
understoond why certain
constructions were qualified as
smelly.

59. “That
should be
improved”
Results showed that users
understoond why certain
constructions were qualified as
smelly.
“This must be
confusing for others”

60. Published at ICSE 2012

61. However, new problems were to be
discovered. We found that, once
the structure of the spreadsheets
had been understood and
validated, complex formulas still
got in the way of understanding
spreadsheets.

62. This led us to the idea of formula smells

63. Again, we took our inpiration from
the smells that Fowler defines in his
canonical book on refctoring.

64. Published at ICSM 2012

65. In a recent extention of the paper,
we also suggest refactorings
corresponding to smells.
This formula, for instance, contain
the same subformula twice.
Extracting this subformula into a
seperate cell will improve
readbility.

66. We went back to practice
And again... A look in practice

67. We found that cloning (i.e. Copy
pasting) in spreadsheets was a
problem. If data is copy-pasted,
updates will not be propagated to
the copies and that might lead to
errors.
Based on existing work in clone
detection in source code, we
developed an algorithm to detec
clones.

68. Clone visualization was added to
our visualization, indicated with a
dashed arrow. After all, when data
is copy-pasted between
worksheets, there is a dependency
between those worksheets (albeit a
different one than a formula link)

69. To validate our algorithm, we
performed a case study at the
distribution centre of the South
Dutch food bank. There, they
process 100.000 kilos of food per
month, and keep track of that with
spreadsheets.
We were able to detect 61 near-
miss clones, of which 25 were
actual errors.
Because of our analysis, this
distrubution centre is now running
error-free spreadsheets!

70. To be published at ICSE 2013

71. And this paper concluded my PhD
thesis.
I will continue to work on
spreadsheet analysis for at least
five more years at Delft University of
Technology, so in the remaining
few slides, I’ll line out what I will be
working on in the future.

72. Remember spreadsheets stay in
business for 5 years and are used
by 12 people during their life span?
This makes it interesting to consider
‘spreadsheet evolution’ and study
how spreadsheets are created.

73. Visual Basic Analysis
In our current visualization and
analysis technique, we only
consider formulas.
However, spreadsheets also allow
for code to interact with data and
formulas (VBA code in Excel).
By analyzing this, we could make
our analysis more complete and
interesting.

74. Spreadsheet testing
Finally, we want to research how
spreadsheet users test. One might
think that spreadsheet users do not
test, but this is not true.

75. In our previous studies, we often
saw formules like this one. Here,
nothing is really calculated.
Instead, some sort of validation is
performed: if ‘find zone’!W3 is
smaller than 0, we are not
interested in the value.
When we could extract these type
of formulas, we could use them to
test the spreadsheet.

76. Analyzing and visualizing spreadsheets
Felienne Hermans
Thanks for reading about the
research adventure I was enjoying
the past 4 years!
If you want to know more, have a
look at my blog: www.felienne.com
If you are intrested in collaborating,
please send me an
Email f.f.j.hermans@tudelft.nl
or a tweet @felienne