This document presents a method called DECOR for automatically generating detection algorithms for design defects from specifications. DECOR involves a 10-step process where design defects are specified using rule cards that describe structural and semantic properties more precisely. A framework called SAD then parses the rule cards, builds models of the defects, and visits the models to generate detection algorithms. Preliminary results applying DECOR to detect blobs and spaghetti code in open-source programs show detection precisions ranging from 20-82% depending on the defect and project.

1. Automatic Generation of
Detection Algorithms for Design Defects
Naouel Moha, Yann-Gaël Guéhéneuc, Pierre Leduc
Ptidej team, GEODES, University of Montreal, Canada
LIFL, INRIA project Jacquard, University of Lille, France
CASCON 2006
October 17-20, 2006, Toronto, Canada

2. Context

Design Patterns are “good” solutions to recurring design problems

Design Defects (DDs)



are “bad” solutions to recurring problems
2 categories:
 High-level (global) problems: antipatterns [BRO 98]
 Low-level (local) problems: code smells [FOW 99]
Why it is important to detect DDs ?



DDs lessen the quality of OO architectures and impede their
evolution and their maintenance
Maintenance is expensive because of DDs [PER 92] : adding,
debugging, and evolving of features are difficult
A good software architecture without DDs: easier to understand,
change, and thus maintain
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3. Context

2 examples of high-level DDs

Blob

Large controller class

Many fields and methods with a low cohesion*

Dependent on the data stored in associated
data classes
*How closely the methods are related to the instance variables
in the class. Measure: LCOM (Lack of Cohesion Metric)
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4. Context

2 examples of high-level DDs

Spaghetti Code


Lack of structure : no inheritance, no reuse, no
polymorphism

Long process-oriented methods with no
parameters and low cohesion

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Procedural thinking in OO programming
Excessive use of class and global variables for
processing
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5. Problem Description

What are the problems ?

Textual descriptions of DDs [Brown 98] [Fowler 99]



Lack of precision and misinterpretations
Difficult to build detection algorithms from textual descriptions
Lack of precise specifications and tools: metrics only
[Marinescu 04]
[Tools: SmallLint, PMD]


Lack of work on high-level DDs


Metrics only : metrics cannot express important structural and semantic
properties
Focus only on low-level DDs (code smells)
Semi-manual detection [Marinescu 04] [Munro 05]
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6. Solution
 How to address these problems ?



We propose a language and a framework
to specify precisely high-level DDs in order
to ease and automate their detection
We define a systematic method, called DECOR, to specify high-level DDs
significantly and to generate detection and correction algorithms
from the specifications of DDs
Method in 10 steps
Moha © ASE 2006
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7. Our Approach
We define a systematic method, called DECOR, to specify high-level DDs
significantly and to generate detection and correction algorithms
from the specifications of DDs
 Illustration with the Blob

Blob


Many fields and methods with a low cohesion

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Large controller class
Dependent on the data stored in data classes
associated
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8. Specification of Design Defects
Rule Cards

More structured description
RULE_CARD : Blob {
RULE : Blob {ASSOC: associated FROM: MainClass ONE TO: DataClass MANY } ;
RULE : MainClass {UNION LargeClassLowCohesion ControllerClass } ;
RULE : LargeClassLowCohesion {UNION LargeClass LowCohesion } ;
RULE : LargeClass { (METRIC: NMD + NAD, VERY_HIGH) } ;
RULE : LowCohesion { (METRIC: LCOM5, VERY_HIGH) } ;
RULE : ControllerClass {UNION (SEMANTIC: METHODNAME, {Process, Main, Ctrl})
(SEMANTIC: CLASSNAME, {Process, Main, Ctrl)};
RULE : DataClass { (STRUCT: METHOD, Accessor) } ;
} ;
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9. Generation of Detection Algorithms

SAD Framework (Software Architectural Defects)






A meta-model to instantiate rule cards of DDs
A parser to analyse and reify rule cards
A repository of metrics
Services to analyse structural relationships
Services to perform semantic and structural analyses
Concrete Generation
1. Parse the rule card
2. Build a model representing this rule card
3. Visit the model of the rule card
4. Generate the detection algorithm
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10. Our Approach
We define a systematic method, called DECOR, to specify high-level DDs
significantly and to generate detection and correction algorithms
from the specifications of DDs
Method in 10 steps
Moha © ASE 2006
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11. Preliminary Results
We specify and detect DDs in several open-source programs
Blob
ArgoUML
Precisions
Time
GanttProject
PMD
QuickUML
113 KLOC
1230 classes
Effectifs
Azureus
192 KLOC
1449 classes
22 KLOC
188 classes
42 KLOC
423 classes
9 KLOC
142 classes
Total
91
143
19
15
3
271
70/91
= 76.9%
82/143
= 57.3%
10/19
= 52.6%
3/15
= 20%
1/3
= 33.3%
166/271
= 61.5%
40.5s
2m
5.6s
31.6s
2.1s
3m 19.8s
Spaghetti Code
ArgoUML
Effectifs
Precisions
Time
Azureus
GanttProject
PMD
QuickUML
Total
26
35
8
11
1
81
21/26
= 80.7%
29/35
= 82.8%
6/8
= 75%
6/11
= 54.5%
0/1
= 0%
62/81
= 76.6%
39s
1m55s
5.4s
31s
1.9s
3m12s
 Results are encouraging ! 
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12. Conclusion

Contributions
 Meta-model enriched with semantic and structural
properties
 Rule-based Language & Framework
 Method DECOR in 10 steps

Our approach


A systematic method based on a meta-model and a rule-based
language to generate automatically detection and correction
algos of high-level DDs
Future Work



Correction of DDs
More defects and case studies including commercial programs
Use of a dictionnary such as WordNet for semantics
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