Slides of my inauguration lecture at the University of Klagenfurt in Austria. In this talk I outline several challenges of evolving software systems and present several ideas and findings from my research to address them. In particular, I show how we can use the history of software projects to identify critical parts of a software system and how we can use visualization techniques to help software engineers to understand the implementation of large, complex software systems including large spreadsheets.

1. The Knowledgeable
Software Engineer
Univ.-Prof. Dr. Martin Pinzger
Professor of Software Engineering
Software Engineering Research Group
University of Klagenfurt

2. Software systems
2
500 million

3. Mobile applications (apps)
3
1,000 million

4. Software in your car
4
Software is everywhere!

5. Fact: Many software systems are large
5
How many lines of code?
10 MLOC = 14 meters

6. Fact: Software systems are complex
6

7. Challenge: Understanding software systems
7
Martin
?
Andreas
?

8. Perspective of software developers
8
Difficult to comprehend dependencies

9. A solution: DA4Java visualization
9
NbBundleTest
testExistingR.()
testNonE.()
main()
NbBundle
getMessage()

10. Install from: http://serg.aau.at/bin/view/MartinPinzger/DA4Java

11. Initial evaluation of DA4Java
Pros/cons
+ DA4Java reduces clutter/information overload
+ Good input for discussing dependencies
- Performance, graph can still get very complex
Todo
Add information about changes
User studies to evaluate the approach
Use the approach in different domains
11

12. Applying the idea to spreadsheets
12

13. 13
50% form the basis for decisions
Spreadsheets are business critical
Errors often lead to financial loss
see: http://www.eusprig.org/horror-stories.htm

14. Interviewed 27 prof. spreadsheet users
14
What annoys you?
What makes you happy?

15. 15
Support for understanding is missing
How are the different worksheets related? (44%)
Where do formulas refer to? (38%)
What cells are meant for input? (22%)
What cells contain output? (22%)

16. End Result
Solution: Breviz spreadsheet visualization
16
exam Richard Griffin lab Richard Griffin
overall Richard Griffin
AVERAGE

17. Breviz: Global View
17

18. Breviz: Formula View
18

19. Evaluation with spreadsheet users
Interviews with 27 users
Case studies with 9 spreadsheets
19

20. 20
Results
Does the visualization help to understand large, complex
spreadsheets?
Answers
“This really helps me to understand what [worksheet] is what.”
“The global view reveals the idea (design) behind the spreadsheet.”
“The different levels allow to show and filter details.”
Whats more ...?

21. Upload your spreadsheet at: http://app.infotron.nl
21

22. Fact: Software systems evolve
Lehmans’ Laws of software evolution
1. Continuing change
A program that is used in a real-world environment must change
2. Increasing complexity
As a program evolves, it becomes more complex
22

23. Growth and changes of Mozilla
231998

24. Implications of Lehmans’ Laws
24
Maintenance
75%
Initial development
25%
Maintenance costs increase
60% is spent on understanding
Developers perform “quick fixes”
Number of bugs increases

25. Challenge: Evolving software systems
25
Martin
?
Andreas
?

26. A solution: Business intelligence for SE
26
Source
Code
Bugs
Tasks
Emails
Knowledge
Repository
Data Mining
Identify bottlenecks in the team work
To understand the effects of source code
changes on the design
To identify failure prone-entities that
need more testing

27. Identifying failure-prone binaries
Released in January, 2007
> 4 years of development
Several thousand developers
Several thousand binaries (*.exe, *.dll)
Several millions of commits
27
RQ: Is fragmentation of contributions related
with the number of post-release failures?

28. Approach
28
Change
Logs
Bugs Regression
Analysis
Measuring
Contributions
Count post-release
failure reports

29. Developer contributions
29
Alice
Printer.dll
System.dll
Bob
Change Logs Build System
4
2
3
4:Alice
4:Alice,
5:Bob

30. Developer contribution network
30
Alice
Bob
Dan
Eric
Fu
Go
Hin
ab
c
Windows binary (*.dll)
Developer
Which binary is failure-prone?

31. Network centrality measures
31
Alice
Bob
Dan
Eric
Fu
Go
Hin
ab
c
Freeman degree
Alice
Bob
Dan
Eric
Fu
Go
Hin
ab
c
Alice
Bob
Dan
Eric
Fu
Go
Hin
ab
c
Bonacich’s powerCloseness
Alice
Bob
Dan
Eric
Fu
Go
Hin
ab
c

32. Larger fragmentation - more failures
32
40200
1.00
0.90
0.80
0.70
0.60
0.50
40200
1.00
0.90
0.80
0.70
0.60
0.50
40200
1.00
0.90
0.80
0.70
0.60
0.50
R-Square Pearson Spearman
Linear regression of 50 random splits
#Failures = b0 + b1*Closeness + b2*#Authors + b3*#Commits

33. What can we learn from that?
Reorganize contributions? (Yes)
Increase testing effort for central binaries? (Yes)
Redesign central binaries? (Maybe)
33
Alice
Bob
Dan
Eric
Fu
Go
Hin
ab
c
5
4
6
2 4
6
2
5 7
4

34. The knowledgeable software engineer
34
Martin Andreas
Knowledge
Repository

35. Strong collaborations
35

36. Software Engineering Research Group
36http://serg.aau.at martin.pinzger@aau.at