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Aspect Mining

Aspect Mining. Jin Huang Huazhong University of Science & Technology, China derick0320@gmail.com Jin.Huang@cern.ch http://cern.ch/huangj ( U nder C onstruction). Outline. Introduction Example Application Related work Recent research Future work Useful Information.

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Aspect Mining

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  1. Aspect Mining Jin Huang Huazhong University of Science & Technology, China derick0320@gmail.comJin.Huang@cern.ch http://cern.ch/huangj (Under Construction)

  2. Outline • Introduction • Example • Application • Related work • Recent research • Future work • Useful Information

  3. Crosscutting Concerns (CCs) • Type of Crosscutting Concern • Homogeneous CCs: Similar pattern • HeterogeneousCCs: Different pattern • CCs cause comprehensionand maintenanceproblems

  4. 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; } 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; } C Example : Homogeneous CCs debug concern system-specific concern synchronization concern

  5. Is There a Solution? Is AOP ? Separating concerns

  6. 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 accessingpath • Sequentially accessing path

  7. Example 2: Prefetching

  8. 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

  9. WhyAspectMining? • If AOP can do better than OOP ?

  10. Outline Introduction Related work Recent research Future work Useful Information

  11. 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.

  12. 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)

  13. 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)

  14. 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.

  15. Outline • Introduction • Related work • Recent research • Two States Model • Algorithm • Selection Model • Experiment • Conclusion • Future work • Useful Information

  16. 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

  17. Algorithm (1/2) • For each node q: • aq: Scatter value for vertex q. • hq: Centralization value for vertex q • Computation model • Probability Distribution • Iterative Computation: t → t+1

  18. Algorithm (2/2) • Obviously the equation (8) converges with the properties of stochastic matrix Matrix form of previous equation

  19. Selection Model • Selection Model • Implementation and Integration • Frequency • out-degree

  20. Experiments setting • Metrics • Precision: Threshold is set to be 0.4 • Recall: Threshold is set to be 0.5 • Comparison with Fan-in and Pagerank algorithms Cases: Prevayler, JHotDraw and HSQLDB.

  21. Results for Prevayler

  22. Results for JHotDraw

  23. Results for HSQLDB

  24. Advices for Aspect Refactoring • Graph AGAC • AGAC is generated with our model. • Grouping Crosscutting Concerns • Association rules mining from AGAC • Grouping CCs from rules

  25. 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 Aspectrefactoring ‘Aspect Mining through Link Analysis’, Jin Huang, FCST 2010.

  26. Outline • Introduction • Related work • Recent research • Future work • Structural Aspect Mining • Clustering • Useful Information

  27. Structural Aspect Mining 1/2 Refactoring • Disadvantage of Existing Aspect Mining Methods • Too simple to find Structural information for aspect refactoring. • Example: Observer pattern may cause crosscutting concernsfor‘updating’.

  28. 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.

  29. Clustering • Vector Space Model • OO Metrics are suggested. • 'Aspect Mining Using Self-Organizing Maps With Method Level Dynamic Software Metrics as Input Vectors'. SayyedGarbaMaisikeli. 2009. • EM Clustering • An model-based clustering approach • Problems: Center Identification, Number of clusters

  30. Outline Introduction Related work Recent research Future work Useful Information

  31. 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

  32. Thank You ! Q&A

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