Concept Location using Information Retrieval and Relevance Feedback
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The document discusses the challenges of formulating effective queries for source code retrieval in the context of an Eclipse bug involving a black rectangle in the JFace text editor. It introduces relevance feedback as a method to enhance query formulation by using developer input to refine results iteratively. The results indicate that relevance feedback can improve the identification of modified methods, leading to more efficient information retrieval in unfamiliar software environments.
Concept Location using Information Retrieval and Relevance Feedback
- 1. On the Use of Relevance Feedback in IR-Based Concept Location Gregory Gay*, Sonia Haiduc**, Andrian Marcus**, Tim Menzies* * West Virginia University, Morgantown, WV, USA ** Wayne State University, Detroit, MI, USA
- 3. IR-based concept location Query Ranked list of results Source code
- 4. Challenge: the query • Text in the query needs to match the text in the source code • Difficult to formulate good queries - unfamiliar source code - unknown target -> hard to describe something that you do not know
- 5. Eclipse bug #13926 Bug description: JFace Text Editor Leaves a Black Rectangle on Content Assist text insertion. Inserting a selected completion proposal from the context information popup causes a black rectangle to appear on top of the display.
- 6. Queries • Q1: jface text editor black rectangle insert text • Q2: jface text editor rectangle insert context information • Q3: jface text editor content assist • Q4: jface insert selected completion proposal context information
- 7. Queries and results • Q1: jface text editor black rectangle insert text – position of modified method: 7496 • Q2: jface text editor rectangle insert context information – position of modified method: 258 • Q3: jface text editor content assist – position of modified method: 119 • Q4: jface insert selected completion proposal context information – position of modified method: 723 Whole change request: 54
- 8. IR CL in unfamiliar software Developers: • Rarely begin with a good query: hard to choose the right words • Analyze very briefly list of results before reformulating query • Even after reformulation, vague idea of what to look for -> queries not always better • Can recognize whether the results retrieved are relevant or not to the problem
- 9. Questions • Is there a way to make the query formulation easier on the developers? • Is there a way to ensure that the subsequent queries lead us in the right direction? • Can we do this by following the common practices of the developers? • Can we improve IR-based CL using this approach?
- 10. Relevance feedback • Uses developer feedback about relevancy of returned results to automatically reformulate queries • Queries are reformulated by: – Adding terms from relevant documents – Removing terms from irrelevant documents • Iterative process • Common technique in text retrieval • Used also in SE
- 11. JFace Text Editor Leaves a Black Rectangle on Content Assist text insertion. Inserting a selected completion proposal from the context information popup causes a black rectangle to appear on top of the display. 1. createContextInfoPopup() in org.eclipse.jface.text.contentassist.ContextInformationPopup 2. configure() in org.eclipse.jdt.internal.debug.ui.JDIContentAssistPreference 3. showContextProposals() in org.eclipse.jface.text.contentassist.ContextInformationPopup + words in documents - words in documents New Query
- 12. IRRF tool • IR Engine: Lucene – based on the Vector Space Model (VSM) – input: methods, query – output: a ranked list of methods ordered by their textual similarity to the query • Relevance feedback: Rocchio algorithm – the classic algorithm for RF; used also in SE – models a way of incorporating relevance feedback information into the VSM
- 13. Evaluation • Extracted bug descriptions and set of methods modified in the bug fixes from bug tracking systems • Consider bug descriptions as initial queries for IR • Measure #methods investigated until reaching a modified method before and after using RF • Relevance feedback: – one developer provides feedback – feedback round ends after marking N methods as relevant or irrelevant (N = 1, 3 ,5)
- 14. Stop criteria • Target method in top N results • More than 50 methods analyzed • Position of target methods in the ranked list of results increases for 2 consecutive rounds -> query moving away from wanted methods
- 15. Systems System Vers. LOC Methods Classes Eclipse 2.0 2,500,000 74,996 7,500 jEdit 4.2 300,000 5,366 750 Adempiere 3.1.0 330,000 28,622 1,900
- 16. Results System RF improves RF does not IR improve IR Eclipse 6 1 jEdit 3 3 Adempiere 4 1 All 13 5
- 17. Results • Eclipse: Report # Baseline IRRF N=1 IRRF N=3 IRRF N=5 19686 428 453 (5r) 48 (16r) 46 m(9r) • jEdit: Report # Baseline IRRF N=1 IRRF N=3 IRRF N=5 1607211 354 103(5r) 36 (12r) 28 (6r) • Adempiere: Report # Baseline IRRF N=1 IRRF N=3 IRRF N=5 1628050 52 3 (3r) 5 (2r) 7 (2r)
- 18. Questions – revisited (1) • Is there a way to make the query formulation easier on the developers? – automatic query formulation • Is there a way to ensure that the subsequent queries lead us in the right direction? – add terms from relevant documents, remove terms from irrelevant documents – stop when we move away from the target (results worsen for 2 consecutive rounds)
- 19. Questions – revisited (2) • Can we do this by following the common practices of the developers? – developers still analyze only a few results in the result list before reformulation • Can we improve IR-based CL using relevance feedback? – in some cases yes
- 20. Current and future work • Studies involving more systems and more developers • Automatically calibrating the parameters for a specific system and a specific set of change requests • Study the circumstances when RF does not improve IR