AN IDE-BASED CONTEXT-AWARE META SEARCH ENGINEMohammad Masudur Rahman, Shamima Yeasmin, and Chanchal K. RoyDepartment of Computer ScienceUniversity of Saskatchewan

20th Working Conference on Reverse Engineering (WCRE 2013), Koblenz, Germany

SOFTWARE MAINTENANCE, BUGS & EXCEPTIONS

Very Common Event!!

EXCEPTION HANDLING: IDE SUPPORT

1

2

EXCEPTION HANDLING: DEVELOPERS (NOVICE & EXPERT)

EXCEPTION HANDLING: WEB SEARCHClass can not access a member of class java.util.HashMap$HashIterator with modifiers "public final”

IDE-BASED WEB SEARCH About 80% effort on Software Maintenance Bug fixation– error and exception handling Developers spend about 19% of time in web

search Traditional web search

Does not consider context of search (No ties between IDE and web browser)

Context-switching and distracting Time consuming Often not much productive

o IDE-Based context-aware search addresses those issues.

EXISTING RELATED WORKS Cordeiro et al. (RSSE’ 2012)– Context-based

recommendation system Ponzanelli et al. (ICSE 2013)– Seahawk Poshyvanyk et al. (IWICSS 2007)– COTS

(Google Desktop) into Eclipse IDE Brandt et al. (SIGCHI 2010)– Integrating

Google web search into IDE

MOTIVATION EXPERIMENTS

Search Query

Common Results

Google Only

Yahoo Only

Bing Only

Content Only

32 09 16 18

Content and Context

47 09 11 10

83 Exceptions Solutions found for at most 58 exceptions.

THE KEY IDEA !! META SEARCH ENGINE

PROPOSED IDE-BASED META SEARCH MODEL

PROPOSED IDE-BASED META SEARCH MODEL Distinguished Features

Meta search engine– captures data from multiple search engines

More precise context– both stack trace and associated code as exception context

Popularity and confidence of result links Complete web browsing experience within the

IDE

PROPOSED METRICS & SCORES Title to title Matching Score (Stitle)– Cosine

similarity measurement Stack trace Matching Score (Sst)– SimHash

based similarity measurement Code context Matching Score (Scc)– SimHash

based similarity measurement StackOverflow Vote Score (Sso)– Summation

of differences between up and down votes for all posts in the link

PROPOSED METRICS & SCORES Top Ten Score (Stt)– Position of result link in

the top 10 of each provider. Page Rank Score (Spr)-- Relative popularity

among all links in the corpus using Page Rank algorithm.

Site Traffic Rank Score (Sstr)-- Alexa and Compete Rank of each link

Search Engine weight (Ssew)---Relative reliability or importance of each search engine. Experiments with 75 programming queries against the search engines.

METRICS NORMALIZATION

Normalization applied to -- Sst , Scc , Sso , Stt , Spr and Sstr

Avoiding bias to any particular aspect

)min()max()min(

,ii

iinormalizedi SS

SSS

FINAL SCORE COMPONENTS Content Relevance Scnt=Stitle Context Relevance Scxt=(Sst + Scc)/2 Link Popularity Spop=(Sso +Spr + Sstr)/3 Search Engine Confidence Sser=(Ssew x Stt)

EXPERIMENT OVERVIEW 25 Exceptions collected from Eclipse IDE

workspaces. Related to Eclipse plug-in framework and

Java Application Development Solutions chosen from exhaustive web search

with cross validations by peers Recommended results manually validated.

EXPERIMENTAL RESULTSScore Top 10 Rank10 Top 20 Rank20

Scnt 10 3.60 16 8.63Scnt, Scxt 11 3.00 16 7.43Scnt, Spop 13 4.69 18 8.11Scnt, Sser 23 4.39 23 4.39Scnt, Scxt, Spop 13 4.07 18 7.61Scnt, Scxt, Sser 24 4.45 24 4.45Scnt, Scxt, Sser, Spop 23 4.26 24 4.54

Top10: No. of test cases solved when the top 10 results consideredRank10: Average rank of solutions when the top 10 results considered

USER STUDY Five interesting exception test cases. Five CS graduates research students as

participants. Top 10 results from SurfClipse randomly

presented to the participants. To avoid the bias of choosing top rated

solutions. 64.28% agreement found.

USER STUDY RESULTSQuestion ID ANSR ANSM AgreementQ1 2.8 2.0 71.43%Q2 4.6 2.8 60.87%Q3 4.6 2.4 52.17%Q4 4.2 3.0 71.43%Q5 5.8 3.8 65.52%Overall 4.4 2.8 64.28%

ANSR: Avg. no. of solutions recommended by the participants.ANSM: Avg. no. of solution matched with that by our approach.Agreement: % of agreement between solutions.

THREATS TO VALIDITY Search is not real time yet. Different aspects need different weights.

LATEST UPDATES A Distributed model for IDE-Based web

search– client-server architecture, remotely hosted web service

Parallel processing in computation Two modes of operations– proactive and

interactive Granular refinement of metrics and assigning

relative weights (i.e., importance) Complete IDE-based web search solution.

CONCLUSION & FUTURE WORKS A novel IDE-Based search with meta

search capabilities Exploits existing search service providers Considers content, context, popularity and

search engine confidence of a result. Recommends correct solution for 24(96%) out

of 25 test cases. 64.28% agreement in user study. Needs more extended experiments and user

study. Metrics need to be fine-tuned and more

granulated.

THANK YOU !!!