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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.
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The Knowledgeable Software Engineer
Univ.-Prof. Dr. Martin PinzgerProfessor of Software Engineering
Software Engineering Research GroupUniversity of Klagenfurt
Software systems
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500 million
Mobile applications (apps)
3
1,000 million
Software in your car
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Software is everywhere!
Fact: Many software systems are large
5
How many lines of code?
10 MLOC = 14 meters
Fact: Software systems are complex
6
Challenge: Understanding software systems
7
Martin
?
Andreas
?
Perspective of software developers
8
Difficult to comprehend dependencies
A solution: DA4Java visualization
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NbBundleTest
testExistingR.()
testNonE.()
main()
NbBundle
getMessage()
Install from: http://serg.aau.at/bin/view/MartinPinzger/DA4Java
Initial evaluation of DA4Java
Pros/cons+ DA4Java reduces clutter/information overload+ Good input for discussing dependencies- Performance, graph can still get very complex
TodoAdd information about changesUser studies to evaluate the approachUse the approach in different domains
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Applying the idea to spreadsheets12
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50% form the basis for decisionsSpreadsheets are business critical
Errors often lead to financial loss see: http://www.eusprig.org/horror-stories.htm
Interviewed 27 prof. spreadsheet users
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What annoys you?
What makes you happy?
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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%)
End Result
Solution: Breviz spreadsheet visualization
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exam Richard Griffin lab Richard Griffin
overall Richard Griffin
AVERAGE
Breviz: Global View
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Breviz: Formula View
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Evaluation with spreadsheet users
Interviews with 27 users
Case studies with 9 spreadsheets19
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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 ...?
Fact: Software systems evolve
Lehmans’ Laws of software evolution
1. Continuing changeA program that is used in a real-world environment must change
2. Increasing complexityAs a program evolves, it becomes more complex
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Growth and changes of Mozilla
231998
Implications of Lehmans’ Laws
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Maintenance75%
Initial development25%
Maintenance costs increase60% is spent on understanding
Developers perform “quick fixes”
Number of bugs increases
Challenge: Evolving software systems
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Martin
?
Andreas
?
A solution: Business intelligence for SE
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SourceCode
Bugs
Tasks
Emails
KnowledgeRepository
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
Identifying failure-prone binaries
Released in January, 2007
> 4 years of development
Several thousand developers
Several thousand binaries (*.exe, *.dll)
Several millions of commits
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RQ: Is fragmentation of contributions related with the number of post-release failures?
Approach
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Change
Logs
Bugs Regression Analysis
Measuring Contributions
Count post-release failure reports
Developer contributions
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Alice
Printer.dll
System.dll
Bob
Change Logs Build System
4
2
3
4:Alice
4:Alice, 5:Bob
Developer contribution network
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Alice
Bob
Dan
Eric
Fu
Go
Hin
ab
c
Windows binary (*.dll)Developer
Which binary is failure-prone?
Network centrality measures
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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
Larger fragmentation - more failures
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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
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
The knowledgeable software engineer
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Martin Andreas
KnowledgeRepository
Strong collaborations
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Software Engineering Research Group
36http://serg.aau.at martin.pinzger@aau.at
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