Using Metrics to Identify Design Patterns in Object-Oriented Software EPM

Transcription

1 Using Metrics to Identify Design Patterns in Object-Oriented Software G. Antoniol

2 Outline Motivations; Design patterns concepts; Design patterns recovery approach; Experimental results; Conclusions.

3 Motivations Program comprehension; Aid in maintenance; Design-implementation implementation compliance check; Design quality evaluation.

4 Design Patterns Reusable, well-known solutions to common design problems; Categories: structural; creational behavioral. Proxy Pattern Subject op() RealSub op() RealSub -> op() Proxy op() Refers

5 Pattern/Motif/Architecture Pattern also means intent, consequences, The pattern encompasses a lot more then structure or behaviour Motif is just an instance in a code of a trait, act, feature something we observe similar in nature to a DP Actually we observe the micro-architecture and related motifs pointing to DP Motif intent may never be known

6 Approach Metrics based multi-stage recovery process for structural design patterns; Conservative recovery process; Uniform representation of design and code using a design description language; Experimented on industrial and public-domain C++ applications.

7 Old Metric Based Recovery Process

8 A META-Level Design/Code AOL Representation AOL: a design description language; Captures OO design concepts from design and/or code; UML-based; StP/OMT - AOL translator; C++ - AOL translator. CLASS Subject OPERATIONS PUBLIC op(); CLASS RealSub OPERATIONS PUBLIC op(); CLASS Proxy OPERATIONS PUBLIC op(); GENERALIZATION Subject SUBCLASSES Realsub, Proxy; RELATION Refers ROLES CLASS Proxy MULT one, CLASS RealSub MULT one

9 Computed Metrics Private, public, protected methods and attributes Relation number: association aggregation inheritance Depth of the inheritance tree, number of children...

10 Multi-Stage Pattern Recovery Given a design pattern = k Pattern Recovery: check constraints on all dispositions of n classes k by k. Complexity: O( n k ) p ( < e1,..., e >, R )

11 Metrics-based stage Software metrics allows to effectively prune the search space by means of class metrics and class pairs shortest path; First kind of constraints: single class level metrics; Output: a set of class candidate sets for each pattern searched.

12 Metrics-based Stage: continued p C i = k ( < e1,..., e >, R ) M p =< m1,..., m k = { x x D, m Mp : m m, m i, i Example: Proxy. <Sub,RealSub,Proxy>, k=3. < c1, c 2, c 3 > Proxy( D) i, j [ 2,0,0 ], [ 0,1,0 ], [ 0,1,1 ] > i only if # kids( c ) 2 # inh( c2) 1 # inh( c3) 1 #assoc(c 3 > [ x] m } Suppose collected metrics are: kids nbr, father nbr, association nbr M Proxy =< 1 ) j 1 ij

13 Metrics-based Stage: Shortest Path Second kind of constraint: topology based; Class pairs shortest path; Given a class: the remaining pattern constituents must be reachable in a number of steps constrained by the pattern structure; Output: reduced candidate sets R j

14 Topology Constraint

15 Shortest Path Equations Let Cmin be the smallest set given y C build min R i ( y) x C : i ShPath ( x, y ) = ShPath ( e i, e min ) R = { < min r1 ( y),.., y,.., rn ( y) > y C ri ( y) Ri ( y)}

16 Exact Structural Constraints Exact design pattern relations are verified on reduced candidate sets. Let R s be the pattern subset of structural relations: R S = x x=< x,..., x > R r R { 1 n s : r( e p,..., e t ) r( x p,..., x t )}

17 Delegation Stage A class may implement an operation by calling a methods of another class (to which it is associated) thus it delegates the responsibility to it Method calls of candidate classes are analyzed to verify delegation; Often not applicable to design since method calls is always not documented.

18 Limitations of the approach Code to AOL translation is affected by inherent ambiguities in language implementation of associations and aggregations see C++ ; Heuristics example: associations: object pointer/reference data member or method formal parameter; aggregations: object instance or array data member, class template argument; Solution: search also soft patterns, where aggregations are substituted by associations.

19 Experiment: Public-Domain Sw Design pattern recovery from code; 6 C++ applications, ranging from 5 to 127KLOC for a total size of 328KLOC; Not explicitly designed using patterns; Reduction effectiveness of the three stages: metrics-based: 3-4 orders of magnitude; structural: 1-2 orders of magnitude; delegation: 2-3 times.

20 Public-Domain SW Results Few patterns instances: Adapter and Proxy most frequent 25 /100KLOC identified, 9 /100KLOC actual; Recovery retrieval effectiveness: characterized using precision and recall; Recall is always 100% (conservative approach); Precision: no delegation: ave = 20.1%, range = %; delegation: ave = 55.4%, range = %; 100%; No soft patterns retrieved were found as actual instances; Time: no delegation: 1s/KLOC, delegation: 1.16s/KLOC.

21 Experiment: Industrial Software Assessment of design pattern use; Telecommunication software: 8 complete C++ components with code and OMT design for a total of about 200 KLOC; Pattern recovery on design: delegation constraints not applicable; only 4 of 8 systems contained pattern instances; two kinds of patterns, 32 Adapter, 6 Bridge.

22 Experiment: Industrial Sw: cont. Pattern Recovery on Code: actual pattern instances present in 2 of 8 systems; Precision: no delegation:16%, delegation:80%; Design-Code Pattern Compliance: no code-design design pattern instances intersection: motivations: no delegation used in design, reused/cots classes in code not modeled in design, design not maintained.

23 Precision/Recall Trade Off On large software a high number of false positive may be generated; Non admissible approaches may be considered; Metrics as: nesting level, cyclomatic complexity, coupling may help to reduce the candidate sets; A compromise between precision and recall may be necessary.

24 Graph Similarity

25 Graph DP structure can be represented as a graph (or a multigraph) A class diagram is just a graph Graph isomorphism is actually not a point as a DP is small and class diagrams are usually large We need something different define a graph similarity

26 Google The WEB is a huge graph Certain nodes are more important than others a good hub is pointed by many good authorities a good compilation of resources web a good authority is pointed by many good hub; National Cancer Institute or NSERC or mono topic but authoritative Goal assign a hub score and authority score it is kind of a graph weighting itself but how

27 Graph to Graps Suppose we have two graphs Ga and Gb each can be described by its adjacency matrix A and B then: T T BZ A + B Z A The series Zodd and Zeven limit of 2k+1 and 2k have different properties We are interested to the series a 1-norm i.e., it is between 0 and 1 Z k k 1 = = 0,1,2,... T T BZ A + B Z A k + k k k F Z 2k

28 Graph similarity Set Z0 a matrix of all 1 Compute: Z T T BZk A + B Zk A 1 = = 0,1,2,... T T BZ A + B Z A k + k k k F Z 2k After a few iteration contains the similarity node by node of the two graphs

29 DP recognition A DP is just a graph A class diagram is a graph Consider that it is actually a multi-graph association, inheritance, aggregation A DP is usually pretty small a few node thus the computation is really fast

30 References Giuliano Antoniol et al Object-oriented design patterns recovery. Journal of Systems and Software 59(2): (2001) Vincent Blondel et al A MEASURE OF SIMILARITY BETWEEN GRAPH VERTICES: APPLICATIONS TO SYNONYM EXTRACTION AND WEB SEARCHING Nikolaos Tsantalis et al, "Design Pattern Detection Using Similarity Scoring," IEEE Transactions on Software Engineering,, vol. 32, no. 11, pp , 909, November 2006 Laura A. Zager,, George C. Verghese: Graph similarity scoring and matching. Appl. Math. Lett. 21(1): (2008) Laura A. Zager: Graph similarity MIT dissertation June 2005