CSMR06a.ppt

CSMR06a.ppt

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  Efficient Identification ofDesign Patterns with Bit-vector Algorithm Olivier Kaczor, Yann-Gaël Guéhéneuc and Sylvie Hamel {kaczorol, guehene, hamelsyl}@iro.umontreal.ca GEODES GEODES qnd LBIT Department of Informatics and Operations Research University of Montreal © Olivier Kaczor 2006
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  Outline 1. Introduction 2. Related Work 3. Our Approach: 1. Code source transformation 2. Identification with Bit-vector algorithm 4. Case Study 5. Current and Future Work 2/22
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  1. Introduction (1/2) Design patterns: Provide good solutions to recurring design problems Provide design vocabulary to improve communication Encourage designs re-use Auto document the source code “Design patterns help a designer get a design "right" faster” [Gamma and al. 1994] 3/22
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  1. Introduction (2/2) Why detect design motifs: Measure the quality of a system Help document the source code Help understand the design problems and choices Sub-problem: Identify micro-architectures similar to design motifs Program model rarely reflects a design motif completely Help to improve the code by applying corrections based on the design motifs What is a design motif occurrence? An occurrence is a micro-architecture where each role of the design motif is played by only one entity 4/22
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  2. Related Work (1/3) Some known identification techniques: – Logic programming [Kramer-Prechelt 1996, Wuyts 1998] – Constraints programming with explanations [Guéhéneuc-Jussien 2001] – Fuzzy logic [Jahnke and al. 1997, Niere and al. 2001] – Graphs transformation [Smolarova-Kadlec 2000] General problem: limited performance 5/22
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  2. Related Work (2/3) Metrics to improve efficiency [Antoniol and al. 1998, Guéhéneuc and al. 2004] Idea : Reduce the search space by removing entities which obviously do not participate in a design motif according to expected metrics values 1. Create fingerprints of design patterns roles with internal attributes of classes playing those roles 2. Use those fingerprints to create identification rules Multi-stage-filtering: A B C D Metrics Structural Other evaluations evaluations evaluations D≤C≤B≤A 6/22
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  2. Related Work (3/3) Similar problem in bioinformatics: – Localising mutated (or not) genes in long anonymous DNA sequences – Localising modified (or not) proteins in long amino-acid sequences Solutions adopted in bioinformatics: – Vector algorithms [Myers 1997, Bergeron-Hamel 2002] – Automata simulation [Holub 1997] – Dynamic programming alignment [Needleman-Wunsch 1970, Smith- Waterman 1981] 7/22
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  Design patterns detectionsystem: Design Source Code Patterns Results Detection Source Code Detection Transformation Algorithm 8/22
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  3. Our approach:Source code transformation (1/3) Problem: Bioinformatics algorithms work on strings Program and design motifs models must be converted in strings Solution: Program and design motifs models can be seen as directed graphs Build their string representations by going through every edge in their graphs 9/22
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  3. Our approach:Source code transformation (2/3) Steps to obtain the string representations: 1. Transform the graphs into Eulerian graphs: – Build an adjacency matrix to identify vertices with unequal in-degree and out-degree – Compute an optimal list of flows to be added between those vertices with the transportation simplex Client as Component co out in Component Leaf Composite dm +operation() Component 0 1 1 in in Leaf 0 0 0 Leaf Composite +operation() +operation() Composite 1 0 0 +add( c : Component ) +remove( c : Component ) +getChild( i : int ) Leaf: out-degree: 0 in-degree: 1 10/22
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  3. Our approach:Source code transformation (3/3) Steps to obtain the string representations: 2. Find an Eulerian circuit and build the strings: – Resolve the directed Chinese Postman Problem to find the shortest tour of the graphs (an Eulerian circuit) – Build the strings by running through the circuits Client as Component co dm +operation() in in Leaf Composite +operation() +operation() +add( c : Component ) +remove( c : Component ) +getChild( i : int ) Component in Leaf dm Component in Composite co Component 11/22
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  3. Our approach:Identification algorithm (1/6) Objective: Vector Algorithm Problem: The design motif string is more like a regular expression than a word Solution: Iterative bit-vector algorithm 12/22
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  3. Our approach:Identification algorithm (2/6) Let x = x1x2…xm be a string representing a program model and c a symbol in that string We define the characteristic vector of c associated to the string x to be: Characteristic vectors are circular sequences of bits on which we operate with standard bit operations: • and • or • left shift • right shift • etc. 13/22
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  3. Our approach:Identification algorithm (3/6) Example: A in B in D dm B in E co B in C dm G cr C dm G cr D dm G cr E dm G as F ag A 14/22
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  3. Our approach:Identification algorithm (4/6) We match against each other the two strings to build a list of potential occurrences The algorithm reads triplets of tokens in the design motif string and associates program entities to the roles For example: We retrieve entities before and after an in token in the program string with bit-wise operations on the characteristic vectors to have potential entities for the Component and Leaf roles 15/22
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  3. Our approach:Identification algorithm (5/6) We search for entities after a Component entity and an in relationship Triplets (ex: B in Composite) First (in) Third (in) Fourth (co) Composite Composite Component Component Component Leaf Leaf Leaf A B A B B B C E B C B C C B D E B D B C D B E E B E B C E B D C B D D B D E B E C B E D B E E Occurrences after processing the first third and fourth triplets 16/22
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  3. Our approach:Identification algorithm (6/6) A program model rarely reflects a design motif completely We include automatic and manual approximation mechanisms 1. Association relationships: – Composition, aggregation, association and use relationship – Conjunction of characteristic vectors 2. Entity counts – Ex: A Composite without leaf – Triplets can be overlooked 3. Inheritance relationship: – Some entities could be inserted or removed from a hierarchy – Parent and children of a class are also possible entities for a role 17/22
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  4. Case Study (1/3) We applied our algorithm on 3 public domain software: 1. Juzzle v0.5 2. JHotDraw v5.1 3. QuickUML 2001 We used an AMD Athlon at 2Ghz with 1024 Mb RAM Programs Sizes Computation times Juzzle v0.5 99 5 JHotDraw v5.1 261 37 QuickUML 2001 373 149 Computation times (in seconds) for building the string representation 18/22
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  4. Case Study (2/3) We compared our bit-vector technique with Ptidej, a framework implementing: Explanation-based constraint programming Metric-enhanced explanation-based constraint programming CP CP+M BV BV+O Abstract Factory JHotDraw v5.1 1202 275 218 27 Juzzle v0.5 1 2 0.9 0.3 QuickUML 2001 785 153 97 29 Composite JHotDraw v5.1 +∞ 17362 129 25 Juzzle v0.5 45 3 0.5 0.3 QuickUML 2001 +∞ 26514 185 27 Identification times (in seconds) of design motifs with all approximations 19/22
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  4. Case Study (3/3) Exact Approximate Existing CP CP+M BV CP CP+M BV Occurrences Abstract Factory JHotDraw v5.1 0 221 69 221 2994 849 194 Juzzle v0.5 0 0 0 0 9 0 13 QuickUML 2001 13 57 23 57 593 124 118 Composite JHotDraw v5.1 70 N/A 0 0 N/A 16983 609 Juzzle v0.5 0 0 0 0 20 0 0 QuickUML 2001 22 N/A 0 0 N/A 4743 513 Number of identified occurrences of the design motifs 20/22
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  5. Current andFuture Work Improve the precision of the results – Combine bit-vector algorithms with empirical metrics values – Add negative relationships in design motifs – Add dynamic information in the string representations Try the approach on more and bigger software Try other bioinformatics string matching algorithms 21/22