SurfClipse-- An IDE based context-aware Meta Search Engine (ERA Track)
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Traditional web search forces the developers to leave their working environments and look for solutions in the web browsers. It often does not consider the context of their programming problems. The context-switching between the web browser and the working environment is time-consuming and distracting, and the keyword-based traditional search often does not help much in problem solving. In this paper, we propose an Eclipse IDE-based web search solution that collects the data from three web search APIs– Google, Yahoo, Bing and a programming Q & A site– StackOverflow. It then provides search results within IDE taking not only the content of the selected error into account but also the problem context, popularity and search engine recommendation of the result links. Experiments with 25 runtime errors and exceptions show that the proposed ap- proach outperforms the keyword-based search approaches with a recommendation accuracy of 96%. We also validate the results with a user study involving five prospective participants where we get a result agreement of 64.28%. While the preliminary results are promising, the approach needs to be further validated with more errors and exceptions followed by a user study with more participants to establish itself as a complete IDE-based web search solution.
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SurfClipse-- An IDE based context-aware Meta Search Engine (ERA Track)
- 1. AN IDE-BASED CONTEXT-AWARE META SEARCH ENGINE Mohammad Masudur Rahman, Shamima Yeasmin, and Chanchal K. Roy Department of Computer Science University of Saskatchewan 20th Working Conference on Reverse Engineering (WCRE 2013), Koblenz, Germany
- 2. SOFTWARE MAINTENANCE, BUGS & EXCEPTIONS
- 3. EXCEPTION HANDLING: IDE SUPPORT 1 2
- 4. EXCEPTION HANDLING: DEVELOPERS (NOVICE & EXPERT)
- 5. EXCEPTION HANDLING: WEB SEARCH
- 6. 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.
- 7. 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
- 8. 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.
- 9. THE KEY IDEA !! META SEARCH ENGINE
- 10. PROPOSED IDE-BASED META SEARCH MODEL
- 11. 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
- 12. 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
- 13. 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.
- 14. METRICS NORMALIZATION Normalization applied to -- Sst , Scc , Sso , Stt , Spr and Sstr Avoiding bias to any particular aspect )min()max( )min( , ii ii normalizedi SS SS S
- 15. 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)
- 16. 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.
- 17. EXPERIMENTAL RESULTS Score Top 10 Rank10 Top 20 Rank20 Scnt 10 3.60 16 8.63 Scnt, Scxt 11 3.00 16 7.43 Scnt, Spop 13 4.69 18 8.11 Scnt, Sser 23 4.39 23 4.39 Scnt, Scxt, Spop 13 4.07 18 7.61 Scnt, Scxt, Sser 24 4.45 24 4.45 Scnt, Scxt, Sser, Spop 23 4.26 24 4.54 Top10: No. of test cases solved when the top 10 results considered Rank10: Average rank of solutions when the top 10 results considered
- 18. 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.
- 19. USER STUDY RESULTS Question ID ANSR ANSM Agreement Q1 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.
- 20. THREATS TO VALIDITY Search is not real time yet. Different aspects need different weights.
- 21. 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.
- 22. 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.
- 23. THANK YOU !!!
Notas do Editor
- Good Morning everyone I am Masudur Rahman from University of Saskatchewan. Welcome to my presentation. Here, I am going to present our paper titled as “An IDE-Based Context-Aware Meta Search Engine” Basically, here, we proposed an IDE-Based recommendation system that works like a meta search engine, that means, it captures results from multiple search engines against a selected exception, and then analyze them to produce a better and context-relevant result set.
- Study shows that about 50%-80% effort is spent on software maintenance, And one of major concern during maintenance is bug fixation. Software bugs are generally associated with runtime different errors and exceptions. To deal with that errors and exceptions, developers spend a lot of time, its about 19% of their programming time. Why? Its because of the traditional web search which has no ties with the IDE. It does not consider the context of the problem developer is facing and developer has to include the context information into the search query, which is challenging, because which term is more important than others is not clear; so, basically, this is a trial and error approach for the developer which is time-consuming. Besides, the switching between IDE and the web browser is often not very interesting if you are trying to concentrate on a problem in the IDE. So, what is the solution??? IDE-based search engine, and it has to consider the problem context of course.
- There are some existing studies that try to address the issues of traditional web search. However, they are basically based on StackOverflow, for example the first two works. StackOverflow is a big source of information and recently it has 1.9 million users with 12 million posts; However, we cannot ignore the whole web for information, and that is why our approach comes into play. The rest two works basically tries to integrate Google desktop search and Google web search in the IDE; However, we are interested to exploit multiple search engines to get more confident set of results for the developer. The baseline idea is to leverage the existing resources for solving technical challenges in a smart way.
- This is our proposed meta search model for IDE-based recommendation. It has tow modules: Client module Computation module. Once the developer selects an exception from Error log or console view, the client module captures the error message, stack trace and the context code likely responsible for exception and sends to the computation module. Upon getting the search request, the computation module sends the error message to multiple search engines. We use Google, Bing, Yahoo and the StackOverflow API to collect results and use them to develop the corpus. Once the corpus is developed, we apply our proposed metrics and algorithms to produce a result set that is relevant to the encountered exception. Now lets assume, what the traditional search engines do? Do they consider context? No. It’s the developer who has to represent the context besides the error message in the search query.
- So, basically, we are providing four interesting and essential things in this model. It exploits the idea of meta search. Why meta search? Lets discuss in the Q/A session. We are considering more precise context: both stack trace and context code. We are also considering popularity and confidence of a result link. And the developer can readily browse the web link recommended with in IDE.
- These are metrics we consider to determine the relevance of a result page against the query exception. Please note that we collected the exception message and exception context in the form of stack trace and context code during search request. So, title to tile matching basically tries to determine the content similarity between exception message and the result page title. We use cosine similarity measurement for that. Then comes the context information. We did HTML scraping and extract the content from different tags like pre, code, blockquote as they are likely to contain the context information about the discussed problems in the page. Once extracted, we use SimHash based similarity to determine the relevance of the discussed problem with the query exception. SimHash basically produces a Hash value for a block of content, and if the hash value of two blocks are closer, they are considered similar. We use this metric for both stack trace and context code matching.
- We also consider other metrics like Top ten score – it marks if a result is found within the top 10 results of any search engine. Page Rank score – we develop a artificial network among the result links in the corpus to determine their relative importance using PageRank algorithm. Site Traffic Rank score – we collect Alexa rank for each result link. Search engine weight– we calculate the support for each result from the search engines.
- For most of the metrics We use this formula to perform the normalization if it is not already normalized.
- So, we have got different perspectives of each result link And now, we use those perspectives to determine different types of scores. We get: Content-relevance Context-relevance Popularity and Search engine confidence for each result. Then, we give all of them equal share in the final score, and now we are working on their relative weights.
- We design a limited experiment with 25 exceptions related to Eclipse plug-in framework And we got interesting results.
- Here, we decompose different component scores and show how much effective they are in recommendation. We collect the top 10 and top 20 results and found that our algorithm can recommend up to 24 exceptions. More interestingly, solutions are found within the top 5 positions mostly.
- We also try to test the recommendations through a user study, because, the approach is all about the users benefit. So, we collect the top 10 recommended results for five exceptions and present the results to the participants in a randomized order. The idea is to check how developers apply their sense of relevance.
- Here, we got 64.28% agreement between our results and their confirmation. Basically, what we did is– we tried to map their selection to the top 5 results of each exception and found that agreement. We also noticed that the result which they mark as relevant are found mostly in the top 5 results of our recommendation. So, it shows that the tool is working quite accurately in relevance computation as an initial attempt. However, it requires more extended experiment and extended user study to claim something solid.
- Here are some latest updates about what we did by this time: We applied parallel processing in the computation model to make it work faster. Present version is quite slower and not like real time. We implemented a client-server architecture for this search so that it can be platform-independent and any IDE can leverage the search as a web service We implemented two modes of operation– proactive and interactive, where the proactive version automatically triggers upon an exception. We did more analysis with the metrics and scores.
- To summarize, We proposed a novel IDE-based search approach that exploits problem context and collects results from a meta search engine. Our preliminary experiments show some interesting results. However, of course the idea needs to be further experimented and tested to discover its potential which is our future work And we are working on that.
- So, that’s all about my talk. Thanks to all for your time. Questions ??