Aspect Mining

Jin Huang

Huazhong University of Science & Technology, China

derick0320@gmail.com Jin.Huang@cern.ch

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