Spontaneous Code Recommendation based on Open Source Code Repository

(1)

Spontaneous Code

Recommendation based on

Open Source Code Repository

Hidehiko Masuhara

[email protected]

Tokyo Tech

joint work with Takuya Watanabe,

Naoya Murakami, Tomoyuki Aotani

(2)

Do you program with Google?

What are you looking for

with Google?

(3)

We can’t remember

all the APIs and usages

• Too many big class libraries

• Non-trivial usages:

what should we write, e.g.,



after created a JFrame?



to format a Date in HH:MM style?



to match a regexp to a string?



to download from an FTP server?

(4)

JavaDoc and Google

aren’t sufficient

• API documents and IDE assistance:

do not tell non-trivial usages

• Keyword-based web search tools:



can show

examples



thought interrupting

how to

format

Date?

think

keywords

type-in

keywords

browse

results

(5)

as you write

in the editor…

similar programs

are displayed

— serve as

examples

Selene: a spontaneous

(6)

Let’s make a window

displaying the current time

12:45

(7)

Key Ideas behind Selene

• Entire editing text

as a query



no need to think about keywords

• Textual search



fast enough with a large repository



language independent

• A large code repository (2 million files)

(8)

Architectural overview

Client

Keyword

search

Server

frontend

Eclipse plugin

Snippet

selection

keyword

extraction

keyword

extraction

3. snippet

selection

& ranking

1. query

extraction

2. find

similar files

(9)

Snippet selection

editing

program

similar files

cursor pos.

similar fragments

snippets

to display

code after similar farg.

= what to do next

(10)

Research challenges

• Huge design&parameter space



Query extraction: entire text / lines around

cursor / term weighting / ...



Snippet selection: similarity algorithms /

# of displaying snippets & lines

• Code clone problem

(11)

Query extraction &

snippet selection

(12)

Basic algorithm & variability

• Query extraction



extracts keywords in the editing text



weighting by distance from cursor

• Snippet ranking



against the lines above the cursor



compute vector similarity / LCS



weighting by inverse term freq.

• Snippet display



# of snippets x # of lines



comments

how much?

how many?

which?

how much?

show/remove?

(13)

Parameter optimization thru

mechanical evaluation

• Evaluation method:

randomly

chosen

program

extract

query

Selene

snippets

precision

recall

(14)

How many snippets?

• total 120 lines

for all snippets

• more snippets,

less lines/snippet

• Best:

6 snippets x 18 lines

→

_{snippets window}

0

5

10

15

20

25 1 2 3 4 5 6 7 8 9

# snippets

re

c

a

ll (

%

)

(15)

overall recall ratio 17.6%

→

25.3%

Optimal parameters

• Query extraction



extracts keywords in the editing text



weighting by distance from cursor

• Snippet ranking



for each fragment in a window



compute vector similarity / LCS



weighting by inverse term freq.

• Snippet display



# of snippets x # of lines



comments

0.5 →

0.11 10 lines

LCS

0.2 →

0.2 6x18

remove comments

(16)

(17)

Problem: Code Clones!

(18)

Problem: Code Clones!

(19)

How to remove clones

• Offline vs

online



Offline: 2 M files (or billion lines)



Online: 50 files (or 100-10,000 lines)

• Clone detection algorithms



Matrix based (cf. CCFinder)



Clustering (e.g.,

k

-means)



Freshness count

requires a

(20)

Freshness count algorithm

74.0

64.0

63.0

60.0

panel, JFrame, setDefaultCloseOperation, WindowConstants, _{setDefaultCloseOperation,} WindowConstants, setSize, JPanel,

TabelLayout

setDefaultCloseOperation, WindowConstants, setSize, JPanel

TableLayout, setPaintBorderLines setDefaultCloseOperation,

TabelPanel, JPanel

TableLayout, setPaintBorderLines, setSize

JPanel, contentPane, BorderLayout setBackground, Color, WHITE,

fieldPanel, TableLayout

similarity

tokens

“known tokens”

(21)

Freshness count algorithm

74.0

64.0

63.0

60.0

TabelLayout

TabelPanel, JPanel

Freshness

count

remove known

tokens; count

# of remaining

tokens

3

4

5

8

(22)

Freshness count algorithm

74.0

64.0

63.0

60.0

TabelLayout

TabelPanel, JPanel

show the

snippet with

max. (similarity

+ fresness)

3

4

5

8

77.0

68.0

(23)

Freshness count algorithm

74.0

64.0

63.0

60.0

panel, JFrame, setDefaultCloseOperation, WindowConstants,

setSize, JPanel, TableLayout _{WindowConstants, setSize, JPanel,}setDefaultCloseOperation, TabelLayout

TabelPanel, JPanel

add shown

tokens to

“known”;

(24)

Freshness count algorithm

74.0

64.0

63.0

60.0

panel, JFrame, setDefaultCloseOperation, WindowConstants,

setSize, JPanel, TableLayout _{WindowConstants, setSize, JPanel,}setDefaultCloseOperation, TabelLayout

TabelPanel, JPanel

1

2

6

77.0

65.0

66.0

66.0 show the

snippet with

max. (similarity

+ fresness)

(25)

(26)

Result

~ 5% increase in recall ratio

(w/our mechanical evaluation [RSSE’12])

0%

10%

20%

30%

40%

B

u

tl

er'

s

Ra

ndo

m

pr

o

blem

set

recall

Duplication

removal

Original

(27)

Query extraction

(again & ongoing)

• Do lines around cursor

represent the programmer’s thought?

?

extract

query

(28)

Programs have

no linear structure

• Common actions are often refactored

into another method



e.g., GUI component initialization

• ...or into another class (e.g., factory)

• Two classes interact for one concern

(29)

Recently visited locations



programmer’s thought

• We see the refactored code fragments



if it is related to what we want to do



IDE offers many supports

• Hypothesis:

recently visited code fragments are

better than text around cursor

(30)

Conclusion

• Selene is spontaneous code

recommendation tool supported by



a large code repository



fast textual search engine

• Interesting research questions



keyword extraction, code clones,

programmers’ concerns, GUI, ...