The document describes a system called Fica that filters code clones for individual users based on machine learning analysis. Fica detects clones in source code, marks clones as interesting or uninteresting based on user feedback, and uses this feedback to train machine learning models to predict how new clones should be classified for that user. The accuracy of Fica's predictions improves as it receives more training from the user.

1. .
Filtering Clones for
Individual User Based on
. Machine Learning Analysis
Jiachen Yang, Keisuke Hotta, Yoshiki Higo,
Hiroshi Igaki, Shinji Kusumoto
Graduate School of Information Science and Technology, Osaka University
June 4, 2012
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Jiachen Yang (IST, Osaka-U) Fica@IWSC2012 June 4, 2012 1 / 14

2. Motivating Example
Participants of survey
Clonesets
Red: Un-interesting
Blue: Interesting
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Jiachen Yang (IST, Osaka-U) Fica@IWSC2012 June 4, 2012 2 / 14

3. Motivating Example
Participants of survey
1 2 3 4 5 6 7 8
Clonesets
Red: Un-interesting
Blue: Interesting
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Jiachen Yang (IST, Osaka-U) Fica@IWSC2012 June 4, 2012 2 / 14

4. Motivating Example
Participants of survey
Clonesets
Red: Un-interesting
Blue: Interesting
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Jiachen Yang (IST, Osaka-U) Fica@IWSC2012 June 4, 2012 2 / 14

5. Interesting U:0 vs I:8
1542 static . har *.
c . 126 c
. har *.
.
1543 . istory_substring ( string , start , end).
h . 127 . ubstring ( string , start , end).
s .
1544 . const char *string;. . 128 . const char *string;. .
1545 . int start , end;.. 129 . int start , end;. .
1546 . .
{ 130 . .
{
1547 . register int len ;. . 131 . register int len ;. .
1548 . register char *result ;. . 132 . register char *result ;. .
1549 . len = end − start;. . 133 . len = end − start;. .
1550 . result = (char *)xmalloc (len + 1);. . 134 . result = (char *)xmalloc (len + 1);. .
1551 . strncpy ( result , string + start, len);. . 135 . strncpy ( result , string + start, len);.
.
1552 . result [ len ] = '0';. . 136 . result [ len ] = '0';. .
1553 . return result ;. . 137 . return ( result );..
1554 . .
} 138 . .
}
(a) lib/readline/histexpand.c (b) stringlib.c
Figure: Example of source code in bash-4.2
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Jiachen Yang (IST, Osaka-U) Fica@IWSC2012 June 4, 2012 3 / 14

6. Un-Interesting U:8 vs I:0
191 ... __P((char *, arrayind_t, . har *));. 309 static
c . int run_one_command __P((. har *));.
c .
192 .static intmax_t subexpr __P((char *));. 310 .static
. int run_wordexp __P((char *));. .
193 .static intmax_t expcomma __P((void));.311 .static
. int uidget __P((void));..
194 .static intmax_t expassign __P((void));. 312 .static
. void init_interactive __P((void));. .
195 .static intmax_t expcond __P((void));. 313 .static
. void init_noninteractive __P((void));..
196 .static intmax_t explor __P((void));. . 314 .static void init_interactive_script __P((void));..
197 .static intmax_t expland __P((void. );
)
. 315 .static void set_shell_name __P((char. *));
.
(a) expr.c (b) shell.c
Figure: Example of source code in bash-4.2
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Jiachen Yang (IST, Osaka-U) Fica@IWSC2012 June 4, 2012 4 / 14

7. Disagreed U:4 vs I:4
710 static int
711 displen (s) 1098 else
712 const char *s; 1099 {
713 { 1100 if ( wcharlist == 0)
714 wchar_t *wcstr; 1101 {
715 size_t wclen, slen ; 1102 size_t len. .
;
716 wcstr = 0..; 1103 . len = mbstowcs (wcharlist, charlist , 0);.
.
717 . len = mbstowcs (wcstr, s, 0);.
s . 1104 . if (len == −1). .
718 .if (slen == −1). . 1105 . len = 0;..
719 . slen = 0;. . 1106 . wcharlist = (wchar_t *)xmalloc (sizeof .... .
720 w
. cstr = (wchar_t *)xmalloc (sizeof ....
. 1107 . mbstowcs (wcharlist, charlist , len + 1);..
721 m
. bstowcs (wcstr, s, slen + 1);.
. 1108 }
722 wclen = wcswidth (wcstr, slen); 1109 if (wcschr (wcharlist , wc))
723 free (wcstr); 1110 break;
724 return (( int)wclen); 1111 }
725 }
(b) subst.c
(a) execute_cmd.c
Figure: Example of source code in bash-4.2
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Jiachen Yang (IST, Osaka-U) Fica@IWSC2012 June 4, 2012 5 / 14

8. Fica — the name
Filter for
Individual user on code
Clone
Analysis
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9. Fica — the website
Figure: Snapshot of Fica
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10. ... ... ........ ........ ........ ....... . . .... .

11. ... ... ........ ........ ........ ....... . . .... .

12. ... ... ........ ........ ........ ....... . . .... .

13. ... ... ........ ........ ........ ....... . . .... .

14. Compare Code Clone Similarity
Pi = possibility to be interesting
Pu = possibility to be un-interesting
Len Pi Pi /Pu Pu Comp
50 5.56% 1.18 4.72% O
87 2.89% 1.11 2.59% O
79 1.97% 0.69 2.87% X
63 3.55% 0.64 5.57% O
77 2.66% 0.46 5.83% X
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15. Good Experiment Result
All training 44 Matched 32 un-interesting 1
All evaluation 34 Accuracy 94.12% interesting 1
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16. Bad Experiment Result
All training 47 Matched 14 un-interesting 16
All evaluation 31 Accuracy 45.16% interesting 1
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Jiachen Yang (IST, Osaka-U) Fica@IWSC2012 June 4, 2012 13 / 14

17. Open Question
How to improve accuracy?
By combining metrics like McCabe Cyclomatic
Complexity?
Thank you!
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18. Unmatched: User un-interesting
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19. Unmatched: User interesting
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Jiachen Yang (IST, Osaka-U) Fica@IWSC2012 June 4, 2012 16 / 14

20. Overall Workflow
. Submits source code
1
.
2 Detects clones
.
3 Mark clones as “interesting”
or not
. Records marked clones into
4
database
.
5 Studies characteristics of
marks using machine learning
Figure: Overall Workflow
algorithms of Fica with CDT
.
6 Ranks unmarked clones based
on machine learning
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Jiachen Yang (IST, Osaka-U) Fica@IWSC2012 June 4, 2012 17 / 14

21. Calc Similarity of Clones
|t : t ∈ d|
tf(t, d) = (1)
|d|
|D|
idf(t, D) = log (2)
1 + |d ∈ D : t ∈ d|
tfidf(t, d, D) = tf(t, d) × idf(t, D) (3)
−− −→
−−−
tfidf(d, D) = [tfidf(t, d, D) ∀t ∈ d] (4)
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Jiachen Yang (IST, Osaka-U) Fica@IWSC2012 June 4, 2012 18 / 14

22. Predicting Category
−− −→ −− −→
−−− −−−
sim(a, b, D) = tfidf(a, D) · tfidf(b, D) (5)
{
0 , sim(a, b, D) = 0
nsim(a, b, D) = sim(a,b,D) (6)
|sim(a,b,D)| , otherwise
{
∑
1 , |M| = 0
poss(t, M) = ∀m∈M nsim(t,m,M)
(7)
|M| , otherwise
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23. Result — bash
A B C D E F G H
100
75
Accuracy (%)
50
25
0
10 20 30 40 50 60 70 80 90 100
Percentage of Training Set (%)
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24. Result — git
A B C D E F G H
100
75
Accuracy (%)
50
25
0
10 20 30 40 50 60 70 80 90 100
Percentage of Training Set (%)
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25. Result — xz
A B C D E F G H
100
75
Accuracy (%)
50
25
0
10 20 30 40 50 60 70 80 90 100
Percentage of Training Set (%)
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26. Result — e2fsprogs
A B C D E F G H
100
75
Accuracy (%)
50
25
0
10 20 30 40 50 60 70 80 90 100
Percentage of Training Set (%)
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27. Result — All Projects
A B C D E F G H
100
75
Accuracy (%)
50
25
0
10 20 30 40 50 60 70 80 90 100
Percentage of Training Set (%)
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Jiachen Yang (IST, Osaka-U) Fica@IWSC2012 June 4, 2012 24 / 14