These slides show an example of a presentation produced by students following the course LINGI2252 "Software Engineering: Measures and Maintenance", taught by Prof. Kim Mens to Master-level students in computer science and computer science engineering at the Louvain School of Engineering, UCL, Belgium. Throughout this course, students have to analyze the quality of an existing software system (and more specifically, its maintainability), understand the nature of some of the problems encountered when maintaining complex software systems, suggest appropriate solutions to improve reusability and maintainability of a software system, measure its quality and support its evolution and program in Smalltalk, a pure object-oriented programming language.

1. INGI2252
Measures & Maintenance
An excerpt of some final presentations
by Prof. Kim Mens
partly based on a presentation by R. Capron & R. Wallez, Dec. 2012

2. Concept of the Course
« Software Measures and Maintenance »
• Two interleaved parts
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Software development and maintenance
Software measures and metrics
• Main objective
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How to develop « better » and more « maintainable » software systems?
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and object-oriented software in particular
How to assess the quality of a software system?
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and its maintainability in particular
• Side objective
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Learn yet a new programming language (Smalltalk)

3. Techniques and tools
« Software Measures and Maintenance »
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“Pure” object-oriented programming
Integrated development environments
Best programming practices & design heuristics
Framework development
Refactoring & bad smells
Unit testing
Software metrics and measures
And a whole bunch of software analysis tools

4. Course assignment
« Software Measures and Maintenance »
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No traditional exam
Instead, analyze quality (maintainability) of an existing software system
with techniques and tools seen in the course
Part 1 : Manual code assessment
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Part 2 : Automated analysis
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Based on good practices, conventions, heuristics, design principles and patterns
Using software metrics, visualisation and code querying tools like MOOSE, Mondrian, SOUL, ...
Present overall results of the analysis to a « client »

5. Global view of the analyzed system
Using the « System Complexity » visualization

6. Summary of the system’s measures
Using the « Overview Pyramid »
NOP : Number of Packages
NOC : Number of Classes
NOM : Number of Methods
LOC : Lines of Code
CYCLO : McCabe’s Cyclomatic complexity number
ANDC : Average Number of Derived Classes
AHH : Average Hierarchy Height
CALLS : Number of Distinct Method Invocations
FANOUT : Number of Called Classes

7. Critical evaluation of
thresholds used
Compare predefined thresholds
(good, bad, average) used by the tool
with :
• The system being analyzed
• Some “good” reference systems
 Collection hierarchy
 Pharo Kernel

8. Evolution of a metric
in terms of the chosen threshold

9. Detailed analysis of FANOUT metric
per class of the analyzed system

10. Illustration of the lack of comments
 Number of Comments
 Number of Public Methods
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11. Absence of accessors and mutators
Using the « Class Blueprint »

12. After adding accessors and mutators
Using the « Class Blueprint »

13. Number of statements vs. lines of code
Using a Mondrian « Scatter Plot »

15. Detection of code duplication
Using the « Side-by-side Code Duplication » tool

16. Discovering dependencies
between packages
Using the
« Dependency Structure Matrix »