Shinpei Hayashi, Katsuyuki Sekine,and Motoshi Saeki

Department of Computer Science,Tokyo Institute of Technology, Japan

iFL: An InteractiveEnvironment for Understanding

Feature Implementations

ICSM 2010 ERA14 Sep., 2010 TOKYO INSTITUTE OF TECHNOLOGY

DEPARTMENT OF COMPUTER SCIENCE

We have developed iFL− An environment for program understanding− Interactively supports the understanding of feature

implementation using a feature location technique− Can reduce understanding costs

Abstract

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Program understanding is costly− Extending/fixing existing features

Understanding the implementation of target feature is necessary

− Dominant of maintenance costs [Vestdam 04]

Our focus: feature/concept location (FL)− Locating/extracting code fragments which

implement the given feature/concept

Background

[Vestdam 04]: “Maintaining Program Understanding – Issues, Tools, and Future Directions”,Nordic Journal of Computing, 2004. 3

FL Example (Search-based)

……public Time(String hour, …) {

......}…public void createSchedule() {

......}public void updateSchedule(…) {

……

scheduler

Source Code

schedule time Search

Feature Location(Search-based approach)

FL

I want to understandthe feature

converting input timestrings to schedule

objects…

Reading these methodsfor understanding 4

A newmaintainer

Constructing appropriate queries requires rich knowledge for the implementation − Times: time, date, hour/minute/second− Images: image, picture, figure

Developers in practice use several keywords for FL through trial and error

Problem 1:How to Find Appropriate Queries?

Search

FL How??

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Complete (Optimum) FL results are rare− Accuracy of used FL techniques− Individual difference in appropriate code

Problem 2:How to Fix FL Results?

schedule time Search

FL

An FL result(code fragments)

Optimum result(Code fragments that should be understood)

Unnecessary code(false positives)

Necessary code(false negatives)

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Selection and understanding of code fragments

Query Input

Feature location(calculating scores)

schedule Search

1st: ScheduleManager.addSchedule()2nd: EditSchedule.inputCheck()…

Updatingqueries

Relevancefeedback

(addition of hints)

Added two feedback processesOur Solution: Feedbacks

Finish if the user judges that he/shehas read all the necessary code fragments 7

Wide query for initial FL− By expanding queries to its synonyms

Narrow query for subsequent FLs− By using concrete identifies in source code

Query Expansion

Expand

• schedule• agenda• plan

schedule* date* Search

1st FL

public void createSchedule() {…String hour = …Time time = new Time(hour, …);…

}

schedule time Search

2nd FL

• list• time• date

A code fragment in a FL resultThesaurus

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Dependency

Code fragmentwith its score

Improving FL results by users feedback− Adding a hint when the selected code fragments

is relevant or irrelevant to the feature− Feedbacks are propagated into other fragments

using dependencies

Relevance Feedback

i th result of FL

1 2 9 6

: relevant

1 8 11 6

(i+1) th result of FL

propagationby dependencies

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Supporting Tool: iFL Implemented as an Eclipse plug-in

− For static analysis: Eclipse JDT− For dynamic analysis: Reticella [Noda 09]

− For a thesaurus: WordNet

JDT

Reticella iFL-core

Eclipse

WordNet

Exec. Traces /dependencies

Syntacticinformation

SynonymInfo.

Implemented!

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Supporting Tool: iFL

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How iFL Works

Inputting Query

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How iFL Works

Calculating scoresEvaluated code fragments

with their scores

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How iFL WorksAssociated method will be shown

in the code editorwhen user selects a code fragment

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How iFL Works

Calculating scores again

15

Adding hints

How iFL Works

Scoresupdated

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How iFL Works

Codereading

FL

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A user (familiar with Java and iFL) actually understood feature implementations

Preliminary Evaluation

# CorrectEvents

# FLexecution

Interactivecosts

Non-interactive

costsΔ Costs Overheads # Query

updating

S1 19 5 20 31 0.92 1 2S2 7 5 8 10 0.67 1 1S3 1 2 2 2 0.00 1 0S4 10 6 10 13 1.00 0 2S5 3 6 6 15 0.75 3 2J1 10 4 20 156 0.93 10 2J2 4 6 18 173 0.92 14 3

5 change requirements and related featuresfrom Sched(home-grown, small-sized)

2 change requirements and related featuresfrom JDraw(open-source, medium-sized)

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Evaluation Criteria

# CorrectEvents

# FLexecution

Interactivecosts

Non-interactive

costsΔ Costs Overheads # Query

updating

S1 19 5 20 31 0.92 1 2S2 7 5 8 10 0.67 1 1S3 1 2 2 2 0.00 1 0S4 10 6 10 13 1.00 0 2S5 3 6 6 15 0.75 3 2J1 10 4 20 156 0.93 10 2J2 4 6 18 173 0.92 14 3

# selected, but unnecessary code fragmentsReduced ratio of overheads

between interactive and non-interactive approaches

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# CorrectEvents

# FLexecution

Interactivecosts

Non-interactive

costsΔ Costs Overheads # Query

updating

S1 19 5 20 31 0.92 1 2S2 7 5 8 10 0.67 1 1S3 1 2 2 2 0.00 1 0S4 10 6 10 13 1.00 0 2S5 3 6 6 15 0.75 3 2J1 10 4 20 156 0.93 10 2J2 4 6 18 173 0.92 14 3

Reduced costs for 6 out of 7 cases− Especially, reduced 90% of costs for 4 cases

Evaluation Results

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# CorrectEvents

# FLexecution

Interactivecosts

Non-interactive

costsΔ Costs Overheads # Query

updating

S1 19 5 20 31 0.92 1 2S2 7 5 8 10 0.67 1 1S3 1 2 2 2 0.00 1 0S4 10 6 10 13 1.00 0 2S5 3 6 6 15 0.75 3 2J1 10 4 20 156 0.93 10 2J2 4 6 18 173 0.92 14 3

Small overheads− Sched: 1.2, JDraw: 12 in average

Evaluation Results

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# CorrectEvents

# FLexecution

Interactivecosts

Non-interactive

costsΔ Costs Overheads # Query

updating

S1 19 5 20 31 0.92 1 2S2 7 5 8 10 0.67 1 1S3 1 2 2 2 0.00 1 0S4 10 6 10 13 1.00 0 2S5 3 6 6 15 0.75 3 2J1 10 4 20 156 0.93 10 2J2 4 6 18 173 0.92 14 3

No effect in S3− Because non-interactive approach is sufficient for understanding− Not because of the fault in interactive approach

Evaluation Results

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Summary− We developed iFL: interactively supporting the

understanding of feature implementation using FL− iFL can reduce understanding costs in 6 out of 7

cases

Future Work− Evaluation++: on larger-scale projects− Feedback++: for more efficient relevance feedback

• Observing code browsing activities on IDE

Conclusion

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additional slides

Sourcecode

Execution trace/ dependencies

Dynamic analysis

Methods withtheir scores

Static analysis

EvaluatingeventsTest

case

Queryschedule

Based on search-based FL Use of static + dynamic analyses

The FL Approach

Events withtheir scores(FL result)

Hints

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Static Analysis: eval. methods Matching queries to identifiers

public void createSchedule() {…String hour = …Time time = new Time(hour, …);…

}

20 for method names

1 for local variables

• schedule• agenda• time• date

Schedule time Search

Thesaurus

The basic score (BS) of createSchedule: 21

Expand

Expanded queries26

Dynamic Analysis Extracting execution traces and their

dependencies by executing source code with a test case

e1: loadSchedule()e2: initWindow()e3: createSchedule()e4: Time()e5: ScheduleModel()e6: updateList()

e1

e3 e6e2

e4

e5

Dependencies(Method invocation relations)Execution trace

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Evaluating Methods Highly scored events are:

− executing methods having high basic scores− Close to highly scored events

Events Methods BS Scoree1 loadSchedule() 20 52.2e2 initWindow() 0 18.7e3 createSchedule() 21 38.7e4 Time() 20 66.0e5 ScheduleModel() 31 42.6e6 updateList() 2 19.0

e1

e3 e6e2

e4

e5

52.2(20)

18.7(0)

19.0(2)

42.6(31)

66.0(20)

38.7(21)

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Selection and Understanding Selecting highly ranked events

− Extracting associated code fragment (method body) and reading it to understand

public Time(String hour, …) {…String hour = …String minute = … …

}

Events Methods Scores Ranke1 loadSchedule() 52.2 2e2 initWindow() 18.7 6e3 createSchedule() 38.7 4e4 Time() 66.0 1e5 ScheduleModel() 42.6 3e6 updateList() 19.0 5

Extracted code fragments

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Relevance Feedback User reads the selected code

fragments, and adds hints− Relevant (): maximum basic score− Irrelevant to the feature (): score becomes 0

Events Methods Hints Basic scores Scores Ranks

e1 loadSchedule() 20 52.2 77.6 2e2 initWindow() 0 18.7 6e3 createSchedule() 21 38.7 96.4 4 1e4 Time() 20 46.5 66.0 70.2 1 3e5 ScheduleModel() 31 42.6 51.0 3 4e6 updateList() 2 19.0 5

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