Overview and Evaluation of Java Component Search System SPARS-J

1
Overview and Evaluation ofJava Component
Search System SPARS-J

• Reishi Yokomori , Hideo Nishi, Fumiaki
  Umemori, Tetsuo Yamamoto, Makoto
  Matsushita, Shinji Kusumoto Katsuro Inoue
• Japan Science and Technology Agency
• Osaka University

2
Outline

• Motivation and research aim
• SPARS-J
• SPARS-J (Outline)
• Ranking method
• System architecture
• Experimental evaluation for SPARS-J
• Conclusion and Future work

3
Motivation

• A library of software is a fount of wisdom.
• Reuse of software components improves
  productivity and quality.
• Example of components source code, document ..
• Maintenance activity is more easier with the
  library.
• However, a collection of software is not utilized
  effectively.
• A developer doesnt know an existence of
  desirable components.
• Although there are a lot of components, these
  components are not organized.
• We need a system to manage components and to
  search suitable component.

4
Research aim

• We build a system which have functions as follows
• searches component, which is suitable for users
  request
• manages the component information
• Targets
• Intranet
• Closed software development environment inside a
  company
• Internet
• Source code from a lot of open-source-software
  community
• Source Forge, Jakarta Project. etc.

5
Outline

• Motivation and research aim
• SPARS-J
• SPARS-J (Outline)
• Ranking method
• System architecture
• Experimental evaluation for SPARS-J
• Conclusion and Future work

6
SPARS-J(Software Product Archive,analysis and
Retrieval System for Java)

• SPARS-J is Java Source Code Search System
• analyzes and extracts components automatically.
• Component a source code of class or interface
• builds a database based on the analysis.
• Use-Relation, Similar Components, Metrics, .....
• provides keyword-search.
• Three ranking methods KR, CR, KRCR
• Analysis information
• Components using (used by) the component
• Package hierarchy

7
Ranking search results

• Ranking method
• Component used repeatedly (by important
  component)
• Ranking based on use relation between components
• Component suited to a user request
• Frequency of word appearance (arranged TF-IDF)
• A class-name, a method-name, ..., have special
  importance
• Integrated Ranking
• Components prized both in KR and CR are very
  important
• Integration by Borda Count method

8
System architecture of SPARS-J (Building a
Database)
Library(Java source files)
Database
Component analysis
store

• Component Information
• Indexes
• Use-Relation
• Clustered Component Graph
• Component Rank

• extracts components
• indexes each appeared word
• extracts use-relation
• clustering similar components
• calculates Component rank

provide
9
System architecture of SPARS-J (Searching
Components)
Component analysis
Component retrieval

• searches components
• from Indexes
• sorts components
• by CR, KR, KRCR

Query
User interface
Query

• analyzes query
• Analysis condition
• Keywords
• displays search results
• Additional Information
• Source Code
• Use Relation
• Similar Components
• Metrics
• etc.........

Components List
Result
Request
Information
10
Screenshot (Top page)
11
Screenshot (Search results)
12
Screenshot (Source code)
13
Screenshot (Similar components)
14
Screenshot (Using the component)
15
Screenshot (Used by the component)
16
Screenshot (Package browsing)
17
Outline

• Motivation and research aim
• SPARS-J
• SPARS-J (Outline)
• Ranking method
• System architecture
• Experimental evaluation for SPARS-J
• Conclusion and Future work

18
Experimental Evaluation

• Comparison of each ranking method in SPARS-J
• We investigate the best ranking method
• CR vs. KR vs. CRKR
• Comparison with other search engines
• We verify SPARS-Js effectiveness as a software
  component search engine.
• vs. Google, Namazu
• Application of SPARS-J in actual development
  environment
• We confirm that SPARS-J is useful to management
  and understanding of software.

19
Experiment 1 Comparison of ranking method in
SPARS-J

• Purpose of Experiment
• We investigate the best method among 3 ranking
  method in SPARS-J.
• CR (Based on Use-relation)
• KR (Based on TF-IDF)
• CRKR ( Integrating 1 2)
• Preparation
• Database from Java source codes publicly
  available
• About 140,000 files from JDK, SourceForge,
  etc.....
• Keywords
• 10 queries assumed development of simple system

20
Experiment 1 Comparison of ranking method in
SPARS-J

• Criterion of Evaluation
• Precision of components in the top 10 Result
• The percentage of suitable components
• User tends to look at only a higher ranked
  results.
• High precision means that there are many useful
  components in range of users visibility.
• Ndpm
• The percentage of the component pair which
  differs rank order between two ranking methods.
• We define users ideal ranking in advance, and
  calculate ndpm.
• The quantitative indicator which shows a distance
  from ideal
• Ndpm considers all the components in a search
  result.
• Its distance becomes large when required
  components are ranked low.

21
Result (Experiment 1)
Ndpm
Precision
Keyword CR KR CRKR CR KR CRKR
A 1 1 1 0.036 0.048 0.037
B 1 1 1 0.194 0.261 0.221
C 0.5 0.5 0.5 0.133 0.117 0.092
D 0.4 0.9 0.8 0.123 0.200 0.189
E 0.4 0.4 0.4 0.208 0.192 0.194
F 0.2 0.2 0.2 0.184 0.184 0.160
G 0.9 1 1 0.081 0.103 0.080
H 1 0.8 1 0.047 0.109 0.052
I 0.6 0.7 0.7 0.210 0.324 0.267
J 0.5 0.7 0.7 0.219 0.243 0.114
Ave. 0.65 0.72 0.73 0.143 0.178 0.141
22
Consideration (Experiment 1)

• By Paired-Difference T-Test, we have confirmed
  that following difference are significant at the
  5 level.
• Precision KR,CRKR CR
• Ndpm CR,CRKR KR
• Characteristic of each method
• CR
• CR generally ranks components in desirable order.
  
• Higher ranked components are important but often
  have no relevance to keyword.
• KR
• KR generally appreciates components which have
  strong relevance.
• In required component, keyword doesnt always
  appear with high frequency.
• CRKR
• CRKR has good result at both precision and ndpm.
• CRKR has the best of both ranking
• We use CRKR as a default ranking method.

23
Experiment 2Comparison with other search engines

• Purpose of Experiment
• We verify SPARS-Js effectiveness as a software
  component search engine.
• SPARS-J
• Database from 140,000 files (Same as Experiment
  1)
• We use CRKR as ranking method.
• Google
• Famous web search Engine
• Input queries to www.google.co.jp
• Namazu
• Full-text search system for documents.
• Namazu uses TF-IDF to rank documents.
• Database from 140,000 files (Same files as
  SPARS-J)
• Preparation
• Keywords 10 queries (Same as Experiment 1)
• Criterion of Evaluation Precision of the top 10
  Result

24
Result (Experiment 2)
Precision of the top 10 result
keyword SPARS-J Google Namazu
A 1 0.7 0.9
B 1 0.4 0.6
C 0.5 0.3 0.4
D 0.8 0.3 0.6
E 0.4 0.1 0.3
F 0.2 0 0.1
G 1 0.3 0.4
H 1 0.1 0.2
I 0.7 0.4 0.4
J 0.7 0.4 0.7
Ave. 0.73 0.3 0.46
25
Consideration (Experiment 2)

• By Paired-Difference T-Test, we have confirmed
  that following difference are significant at the
  5 level.
• Precision
• SPARS-J Namazu Google
• () SPARS-J (CR, KR, CRKR) Namazu
• Consideration of Results
• Google
• In the result, there are many pages other than an
  explanation of Java source code.
• Performance depends on how much description there
  are.
• Namazu
• Since the datasets consists of only source codes,
  the result is better than Google.
• Without characteristics of Java programs, we
  cannot get good results.
• For searching software components, SPARS-J is
  more useful than other search engines.

26
Experiment 3 Application of SPARS-J in actual
development environment

• Purpose of Experiment
• We confirm that SPARS-J is useful to management
  and understanding of software resource.
• Criterion of Evaluation
• Qualitative evaluation about SPARS-J
• Preparation
• We set up SPARS-J to a company.
• 7 employees use SPARS-J for two weeks.
• They are all engaged in the software development
  and the maintenance activity.
• We carry out a questionnaire survey about SPARS-J

27
Result (Experiment 3)
( Useful or Used repeatedly 5 4 3 2 1
Useless or seldom Used )
Questionnaire Item \ examinee A B C D E F G Mode
Package Browser 4 5 5 5 4 3 3 5
Similar components 4 5 5 2 4 3 5,4
Components used by the class 5 5 5 5 5 5 5
Components using the class 5 1 5 5 5 5 5
Metrics of the class 1 4 1 2 4 5 4,1
Download of the class 1 3 5 5 2 5 5
Contribution to reduction of time cost 3 5 5 3 4 1 5,3
Improvement for software quality 5 3 3 3 4 1 3
Understanding of software resource 3 1 5 3 5 2 1 5,3,1
View-ability of the component-list view 4 4 5 5 3 3 5 5
View-ability of the highlighted source code 3 5 5 5 5 5 5 5
28
Consideration (Experiment 3)

• Highly rated questionnaire items
• Reference by package browser
• Reference by similar components
• Reference by components using (used by) the class
• View-ability of the component list view and
  source code
• Activities realized by using SPARS-J
• Listing of applications which uses certain
  component
• Impact analysis at reediting components

29
Consideration (Experiment 3)

• Other comment
• Response speed is very quick, and we have felt no
  stress.
• Since it is not necessary to install in a client,
  sharing of software components is easy.
• SPARS-J can support maintenance work effectively.
• Easier grasp of software components

30
Conclusion and Future works

• Conclusion
• We construct software component search system
  SPARS-J.
• Search engine for Java source code
• Ranking components with consideration of
  characteristics.
• Provision of useful relevant information.
• We verified the validity of SPARS-J based on
  experimental evaluation.
• SPARS-J is useful to search software components.
• SPARS-J is very helpful to grasp and manage
  components.
• Future works
• The quantitative evaluation other than ranking
  performance
• Support for other software component

31
(No Transcript)
32
(No Transcript)
33
(No Transcript)
34
Outline

• Motivation and research aim
• SPARS-J
• Outline
• System architecture
• Ranking method
• Each part
• Analysis part
• Retrieval part
• User Interface
• Experiment
• Conclusion and Future work

35
Component analysis part

• Extract component and its information from a Java
  source file
• The process
• Extract a component
• Index the component
• Extract use relations
• Clustering similar components
• Rank components based on use relations (CR method)

36
Extract and index a component

• Extracting component
• Find class or interface block in a java source
  file
• Location information in the file (start line
  number, end line number)
• Indexing
• Extract index key from the component
• Index key a word and the kind of it
• No reserved words are extracted
• Count frequency in use of the word

public final class Sort / quicksort /
private static void quicksort() int
pivot quicksort()
quicksort()
word kind
Sort Class name
quicksort Comment
quicksort Method name
pivot Variable name
quicksort Method call

1
1
1
1
2

Index key
frequency
37
Extract use relations

• Extract use relations among components using
  semantic analysis
• Make component graph from use relations
• Node component
• Edge use relation

Inheritance
Interface implementation
Variable type
Instance creation
Field access
Method call
Data
public class Test extend Data
public static void main()
Sort.quicksort(super.array)

Inheritance Field access
Sort
Test
Method call
The kind of use relation
Component graph
38
Similar component

• Similar component is copied component or minor
  modified component
• We merge similar components into single component
• Merged component have use relations that all
  component before merging have

C
G
B
F
A
D
E
Component graph
Clustered component graph
39
Clustering components

• We measure characteristics metrics to merge
  components
• The difference ratio of each component metrics
• Metrics
• complexity
• The number of methods, cyclomatic, etc.
• represent a structural characteristic
• Token-composition
• The number of appearances of each token
• represent a surface characteristic

40
Ranking based on use relation

• Component Rank (CR)
• Reusable component have many use relation
• The example of use is much
• General purpose component
• Sophisticated component
• We measure use relation quantitatively, and rank
  components
• The component used by many components is
  important
• The component used by important component is also
  important

Katsuro Inoue, Reishi Yokomori, Hikaru Fujiwara,
Tetsuo Yamamoto, Makoto Matsushita, Shinji
Kusumoto "Component Rank Relative Significance
Rank for Software Component Search", ICSE,
Portland, OR, May 6, 2003.
41
Propagating weights
A
B
C
Ad-hoc weights are assigned to each node
42
Propagating weights
A
B
C
The node weights are re-defined by the incoming
edge weights
43
Propagating weights
0.5
0.175
A
B
0.345
C
We get new node weights
44
Propagating weights
0.4
0.2
0.2
A
B
0.2
0.2
0.4
0.4
C

• We get stable weight assignment
• next-step weights are the same as previous ones
• Component Rank order of nodes sorted by the
  weight

45
Outline

• Motivation and research aim
• SPARS-J
• Outline
• System architecture
• Ranking method
• Each part
• Analysis part
• Retrieval part
• User Interface
• Experiment
• Conclusion and Future work

46
Component retrieval part

• Search components from database, rank components
• The process
• Search components
• Ranking suited to a user request
• Aggregate two ranks (CR and KR)

47
Search components

• Search query
• Words a user input
• The kind of an index word, package name
• Components contain given query are searched from
  Database

48
Ranking suited to a user request

• Keyword Rank (KR)
• Components which contain words given by a user
  are searched
• Rank components using the value calculated from
  index word weight
• Index word weight
• Many frequency in use of a component
• A word contained particular components
• A word represent the component function such as
  Class name
• Sort the sum of all given word weight
• TF-IDF weighting using full-text search engine

49
Calculation of KR value
the kind of a word weight
Class name 200
Interface name 50
Method name 200
Package name 50
Import 30
Method call 10
Field access 10
Variable type 10
Instance creation 10
Local var access 1
Comment 30
Doc comment 50
Line comment 10
String 1

• Calculate weight Wct with component c word t
• TFi The frequency with which a kind i of word t
  occurs in component c
• IDF the total number of components / the number
  of components containing word t
• kwi Weight of a kind i
• KR value is the sum of all word Wct

50
Aggregate two ranks

• Aggregate two ranks KR and CR
• Aggregation method
• Borda Count method known a voting system
• Use for single or multiple-seat elections
• This form of voting is extremely popular in
  determining awards
• SPARS-J
• Rank components both KR and CR
• Using KR and CR, the component that be suitable
  users request, reusable and sophisticated

51
Borda Count method

• There are 10 voters and 5 candidates (from A to
  E)
• Each voter rank candidates
• 1 point for last place, 2 points for second from
  last place , and N points for first place
• 1st5points,2nd4points,
• A1536428points
• B38points
• C38points
• D22points
• E26points

1st 2nd 3rd 4th 5th
3 A B C D E
3 E B C D A
2 C B A E D
2 C D B A E
Aggregation
1st 1st 3rd 4th 5th
B C A D E
52
Outline

• Motivation and research aim
• SPARS-J
• Outline
• System architecture
• Ranking method
• Each part
• Analysis part
• Retrieval part
• User Interface
• Experiment
• Conclusion and Future work

53
User interface

• Receive a users query and provide the search
  results through Web browser
• Microsoft Internet Explore, Mozilla, etc.
• The process
• Parse query word and the search condition
• Show rank ordered results
• Show analyzed information of the component
• Used by/Using the component
• Metrics

54
Analyzed information

• A component information are as follows
• Metrics
• The number of method, variable
• LOC, cyclomatic
• Etc. (measurable metrics in the component itself)
• Components used by/using the component
• Show lists of nodes followed use relation
• Components that are similar to the component
• Show lists of similar components

55
Package browsing

• The naming structure for Java packages is
  hierarchical
• A user can search lists of components in same
  package of a component easily

56
Outline

• Motivation and research aim
• SPARS-J
• Outline
• System architecture
• Ranking method
• Each part
• Analysis part
• Retrieval part
• User Interface
• Experiment
• Conclusion and Future work

57
Experiment(1/2)

• Comparison with Google
• Register about 130,000 components get from
  Internet
• Query words calculator applet and chat server
  client
• Calculate relevance ratio of 10 rank higher
• Relevance The component is reusable source code
• Google is a web search engine
• Add java source term to the query words
• Follow one link from the result web page

58
Experiment(2/2)

• Example 1
• calculator applet
• SPARS-J
• 9 hits
• 7 suited components
• Example 2
• chat server client
• SPARS-J
• 69 hits
• 57 suited components
• Using SPARS-J, suited component is high order

Example1
Example2
SAPRS-J SAPRS-J Google Google SPARS-J SPARS-J Google Google
order Relevance Ratio Relevance Ratio Relevance Ratio Relevance ratio
1 ? 1 ? 1 ? 1 0
2 ? 1 0.5 ? 1 0
3 ? 1 ? 0.67 ? 1 0
4 ? 1 0.5 ? 1 0
5 ? 1 ? 0.6 ? 1 0
6 0.83 ? 0.67 ? 1 0
7 ? 0.86 0.57 ? 1 ? 0.14
8 0.75 ? 0.63 ? 1 0.13
9 ? 0.78 0.56 ? 1 ? 0.22
10 - - 0.5 ? 1 ? 0.3
59
Conclusion and Future work

• We developed component search engine SPARS-J
• Using SPARS-J, retrieval of components used well
  is enabled easily.
• Future work
• Morphological analysis of Index keyword
• Collaborative filtering
• Investigate best ranking method
• The value of weight
• Aggregation ranks
• Evaluation of SPARS-J
• Usability

60
End
61
Component graph
System Y
System X
A
B
F
C
G
E
D
I
H
component
use relation
62
Weight of nodes
System Y
System X
A
B
F
C
G
E
D
I
H
sum of all node weights 1 ... (1) weight of
node represents significance of node
63
Weights of edges
A
0.4
0.2

• Node weight is distributed to each outgoing edge
• Edge weights are collected at the destination
  node

sum of all outgoing edge weights origin node
weight ... (2) sum of all incoming edge
weights destination node weight ... (3)
64
Definition of weights

• Under constraints (1)(3), we have a simultaneous
  equation

.

W node weight vector
Dt transposed matrix of distribution ratios

• This simultaneous equation can be solved by
  propagating node weight through edges in the graph

65
Pseudo use relation
A
B
C

• Weight computation does not always converge
• Add a pseudo edge from a node to another, if
  there is no 'real' edge
• Distribution ratios pseudo edges ltlt real
  edges

66
Markov model

• Component rank model can be considered as a
  Markov Chain of user's focus
• User's focus moves from one component to another
  along a use relation at a fixed time duration
• Node weight represents the existence probability
  of the user's focus at infinite future

67
Related Works

• Markov models of documentation traversal
• Influence Weight impact factor of journal
  publication thought incoming references
• Page Rank weight of HTML in the Internet through
  incoming web links
• Explicit use relations
• No clustering (important for software products)
• Measurement reusability of components or
  interfaces
• Use various characteristic metrics
• Indirect indicator of reusability
• Our approach directly reflects usage of
  components

68
1.????????????

• Component Rank(CR)?
• ?????????????????,??????
• ???????????????????
• ?????????????????????
• ??????????,??????????????????????????
• ??????,????????????

69
2.?????????????????

• Keyword Rank(KR)?
• ??????????????TF-IDF????
• ??????????????????????
• ????????????????
• ???,??????????????????
• ??????????????????????????
• ????????????????
• ????????????????

70
3.CR?KR?????????

• ????????
• ?????????,???
• CR?
• ????????????
• KR?
• ???????,???????????????????????
• Borda???
• ????????????????????

71
CR????

• ??????????????????
• ????
• ?????????????
• ??????1
• ???????????
• ??????,??????????
• ??????????
• ????????????????,???????????????
• ????????????,2.3.?????????
• ??????????,?????????????CR????
• ??????????????CR????

72
?????

• ??????????????????,
• ??????????????????????
• ???????????????????????
• SPARS-J????????????,?????????????

73
???????????

• ???????????????
• ????????????????????
• ?????????????????????????????????????
• ??,????????????,??????????????????

????????????
74
?????

• ????????????
• ?????????????????,SPARS-J?????????????????????????
  ??
• SPARS-J???????????
• ???????????,????????????????
• ???????????SPARS-J?????
• ???????????????????,?????????????????????????

75
??1. ????????????

• ?????
• ?????????????????,SPARS-J?????????????????????????
  ??
• ????
• GoogleWeb??????????,???????????????
• Namazu?????????????????
• ????
• ???????????????????
• ???????,???????????????????

76
??????

• ?????????????????????????
• ????????????,?????????????????????????
• ?????????????????????????
• Web???????,????????1???(10?)???????????????,2?????
  ????????????????????????

Amanda Spink, B. J. Jansen, D. Wolfram, T.
SaracevicFrom E-Sex to E-Commerce Web Search
Changes IEEE Computer,Vol.35,No.3,pp.107-109,Mar(
2002).
77
??1. ????????????

• ??
• ??????
• SPARS-J?Namazu?????, JDK???Web???????????????(?14?
  ?????????)???
• Google??????????????????????
• ???????
• ???????????????????10?????
• ??
• ??????????????10??????????,???????

78
??1???
??????????????
keyword SPARS-J Google Namazu
A 1 0.7 0.9
B 1 0.4 0.6
C 0.5 0.3 0.4
D 0.8 0.3 0.6
E 0.4 0.1 0.3
F 0.2 0 0.1
G 1 0.3 0.4
H 1 0.1 0.2
I 0.7 0.4 0.4
J 0.7 0.4 0.7
Ave. 0.73 0.3 0.46
79
??1???

• ?????????????
• ????5????????????
• ???
• SPARS-J Namazu Google

80
??1???

• Google
• Web????????????Java???????????????????????????????
  ?,?????????????
• Namazu
• SPARS-J??????????
• Google????????,???????????????????
• ????????????????????????????????,Java?????????????
  ????
• SPARS-J???????????,?????????????????????

81
??2. SPARS-J???????????

• ????
• ???????????,????????????????
• SPARS-J??3???????????????????
• ??????????????
• ????????????????????
• 1.2.??????????????
• ????

82
1.????????????

• Component Rank(CR)?
• ???????????????,??????
• ???????????????????
• ?????????????????????
• ??????????,??????????????????????????
• ??????,????????????

83
2.?????????????????

• Keyword Rank(KR)?
• ??????????????TF-IDF????
• ??????????????????????
• ????????????????
• ???,??????????????????
• ??????????????????????????
• ????????????????
• ????????????????

84
3.CR?KR?????????

• ????????
• ?????????,???
• CR?
• ????????????
• KR?
• ???????,???????????????????????
• ????????????????????

85
3.CR?KR?????????

• Borda???
• ??????????????,???????????????
• ?) ??? A, B, C, D, E
• ??,CR?KR??????????CRKR????????

CR KR
1? A D
2? E C
3? C A
4? B B
5? D E
CR KR ???
A 1 3 4
B 4 4 8
C 3 2 5
D 5 1 6
E 2 5 7
????
1? A
2? C
3? D
4? E
5? B
????????
???????
CR?KR???
86
??2. SPARS-J???????????

• ????
• ???????????,????????????????
• SPARS-J??3???????????????????
• ??????????????(CR)
• ????????????????????(KR)
• 1.2.??????????????(CRKR)
• ????
• ???????????????????
• ???????,???????????????????
• ndpm??????????????????????
• ???????,????????????

87
ndpm??????

• ????????????(d,d)????,????????????????????(m)???
• ?????????n????

???
88
??2. SPARS-J???????????

• ??
• ??????
• (??1???)14??????????????
• ???????
• (??1???)10?????????
• ??
• ??????????????10?????????????????
• ??????????????????????????,??????????????????ndpm?
  ???

89
??2???
???
ndpm?
keyword CR KR CRKR CR KR CRKR
A 1 1 1 0.036 0.048 0.037
B 1 1 1 0.194 0.261 0.221
C 0.5 0.5 0.5 0.133 0.117 0.092
D 0.4 0.9 0.8 0.123 0.200 0.189
E 0.4 0.4 0.4 0.208 0.192 0.194
F 0.2 0.2 0.2 0.184 0.184 0.160
G 0.9 1 1 0.081 0.103 0.080
H 1 0.8 1 0.047 0.109 0.052
I 0.6 0.7 0.7 0.210 0.324 0.267
J 0.5 0.7 0.7 0.219 0.243 0.114
Ave. 0.65 0.72 0.73 0.143 0.178 0.141
90
??2???

• ?????????????
• ????5????????????
• ???
• KR,CRKR CR
• ndpm?
• CR,CRKR KR

91
??2???

• CR?
• ?????????????????????????
• KR?
• ?????????????????
• CRKR?
• ????ndpm?????????????????????
• CR??KR?????????????????????????????????

92
??3. ??????????????????

• ????
• ???????????????????,?????????????????????????
• ????
• SPARS-J??????????
• ???????????????????????7?????,SPARS-J????????????
  ?

93
????????
(? 5 4 3 2 1 ?)
A B C D E F G ???
???????????? 4 5 5 5 4 3 3 5
???????????? 4 5 5 2 4 3 5,4
????????????????????? 5 5 5 5 5 5 5
????????????????????? 5 1 5 5 5 5 5
?????????? 1 4 1 2 4 5 4,1
??????????????? 1 3 5 5 2 5 5
????????? 3 5 5 3 4 1 5,3
??????????? 5 3 3 3 4 1 3
???????????? 3 1 5 3 5 2 1 5,3,1
????????????? 4 4 5 5 3 3 5 5
???????????? 3 5 5 5 5 5 5 5
94
????????

• ??????????
• ????????????
• ????????
• ???????????
• ??????????????????
• SPARS-J??????????????
• ???????????????
• ?????????????

95
??3???

• ?????????????????????????????,??????????????????
• ??????
• ???????,?????????
• ????????????????
• ????????????????????,??????????????
• ????????????????

96
?????????

• ??????????????SPARS-J??????
• ????????????
• Google?Namazu???????
• SPARS-J???????????
• CRKR?????????,??????????????????
• ???????????SPARS-J?????
• ????????????????
• ?????
• ??????
• ???????????????????
• ??????????????

97
END
The End