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Aspect Mining
Jin Huang
Huazhong University of Science & Technology, China
[email protected] [email protected]
http://cern.ch/huangj (Under Construction)
Outline Introduction
Example Application
Related work Recent research Future work Useful Information
Crosscutting Concerns (CCs) Type of Crosscutting Concern
Homogeneous CCs: Similar pattern Heterogeneous CCs: Different pattern
CCs cause comprehension and maintenance problems
C Example : Homogeneous CCs
void *HeapAlloc(size_t size) { #ifdef NUTDEBUG ...//some code #endif NODE **fpp = 0; //code removed #if defined(__arm__) || defined(__m68k__) || defined(__H8300H__) || … while ((size & 0x03) != 0) size++; #endif if (size >= available) { #ifdef NUTDEBUG ...//some code #endif return 0;}
debug concern
system-specific concern
int is_orphaned_pgrp(int pgrp) { int retval; … read_lock(&tasklist_lock); … retval = will_become_orphaned _pgrp(pgrp, NULL); … read_unlock(&tasklist_lock); … return retval;}
synchronization concern
Separating concerns
Is There a Solution? Is AOP ?
Prefetching - Heterogeneous CCs Prefetching
Prefetching is used to preload the file from disk to memory. It is an OS mechanism for performance.
[‘Checking system rules using system-specific, programmer-written compiler extensions’. (OSDI, 2000)]
Execution paths for prefetching Randomly accessing path Sequentially accessing path
Example 2: Prefetching
Application: Spring AOP
Spring: An open source application framework Inversion of Control container Aspect-oriented programming framework Transaction management
Transaction management With a number of transaction management Provides a more simple API for programmatic transaction
management than the APIs of JTA. Paper: “Bringing Advanced Transaction Management
Capabilities to Spring” Applicationshttp://www.oracle.com/technology/tech/java/spring/jta_spring_article.pdf
Why Aspect Mining ?
If AOP can do better than OOP ?
Outline Introduction Related work Recent research Future work Useful Information
Fan-in Analysis Fan-in Analysis
An aspect mining approach that identifying CCs as methods that are called from many different call sites
['Identifying Crosscutting Concerns Using Fan-In Analysis'.(WCRE2004, Marius Marin)]
Limitation: Only considering fan-in values of methods , can not
find patterns of complex crosscutting concerns.
Random Walk Model
Motivation Inspired by pageRank algorithm of google
Contribution Paper first adopts the Markov model for
computing popularity and significance values of elements in the coupling graphs.
Structure based mining approach: considering the coupling graphs of programs
‘Efficiently mining crosscutting concerns through random walks’. (Charles Zhang, AOSD 2007)
Clustering Approach
Motivation Information Retrieval: Clustering
Contribution Vector Space Model: a new model for aspect
mining Clustering approach is adopted for identifying
CCs ‘Aspect Mining using a Vector-Space Model Based
Clustering Approach’ . (G. S. Moldovan and G. Serban, LATE, 2007)
Program Analyses for Aspect Mining
Program Analyses (PA) Framework Static analyse: points-to analyses, escape analyseis,
and dependence analyses Analyses Tools for Java: Soot, Indus.
Aspect Mining through Program Analyses Clone Detection: Mining homogeneous CCs. Dependence Analysis: provide us
dependencies for aspect mining.
Outline Introduction Related work Recent research
Two States Model Algorithm Selection Model Experiment Conclusion
Future work Useful Information
Two States Model (1/2)
Information Retrieval Algorithm HITS algorithm: ‘Authoritative sources in a
hyperlinked environment’, Jon Kleinberg, 1998. Two-States Model
Scatter - probability of being crosscutting logic. Centralization - probability of being core logic
Interaction of Two States
Algorithm (1/2)
For each node q: aq : Scatter value for vertex q. hq : Centralization value for vertex q
Computation modelProbability Distribution Iterative Computation: t → t+1
Algorithm (2/2)
Matrix form of previous equation
Obviously the equation (8) converges with the properties of stochastic matrix
Selection Model
Selection Model Implementation and Integration Frequency out-degree
Experiments setting
Cases: Prevayler, JHotDraw and HSQLDB.
Metrics Precision: Threshold is set to be 0.4 Recall: Threshold is set to be 0.5
Comparison with Fan-in and Pagerank algorithms
Results for Prevayler
Results for JHotDraw
Results for HSQLDB
Advices for Aspect Refactoring Graph AGAC
AGAC is generated with our model. Grouping Crosscutting Concerns
Association rules mining from AGAC Grouping CCs from rules
Conclusion We apply two-state model for aspect mining. This
model is based on scatter and centralization states of program elements.
We design a algorithm to compute of ”scatter” and ”centralization” states.
with two-state model, we generate advices for Aspect refactoring
‘Aspect Mining through Link Analysis’, Jin Huang, FCST 2010.
Outline Introduction Related work Recent research Future work
Structural Aspect Mining Clustering
Useful Information
Structural Aspect Mining 1/2 Disadvantage of Existing Aspect Mining Methods
Too simple to find Structural information for aspect refactoring.
Example: Observer pattern may cause crosscutting concerns for ‘updating’.
Refactoring
Structural Aspect Mining 2/2 Related Topics
Interactions of Structural Aspects. 'Analyzing Interactions of Structural Aspects'. Benoit Kessler, Eric Tanter. International Workshop on Aspects, Dependencies and Interactions (ADI). 2007.
Aspect Dependences and Interactions. 'AspectOptima: A Case Study on Aspect Dependencies and Interactions‘, Jorg Kienzle, Ekwa Duala-Ekoko, Samuel Gelineau , 2009.
Clustering Vector Space Model
OO Metrics are suggested. 'Aspect Mining Using Self-Organizing Maps With
Method Level Dynamic Software Metrics as Input Vectors'. Sayyed Garba Maisikeli. 2009.
EM Clustering An model-based clustering approach Problems: Center Identification, Number of clusters
Outline Introduction Related work Recent research Future work Useful Information
Useful information AOP vs. OOP:
http://www.coolverification.com/2006/10/the_aop_vs_oop_.html
Mining Software Engineering Data: http://people.engr.ncsu.edu/txie/research.htm
Program Analysis Indus: http://indus.projects.cis.ksu.edu Soot: www.sable.mcgill.ca/soot
Information Retrieval: Link Analysis:
http://www.cs.uic.edu/~liub/teach/cs583-fall-05/CS583-link-analysis.ppt
EM Clustering: http://en.wikipedia.org/wiki/Expectation-maximization_algorithm
Thank You !Q&A
