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Multi-AbstractionRetrievalMulti-AbstractionRetrieval MotivationMotivation ExperimentsExperiments Overall Framework Multi-Abstraction Concern Localization.

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Presentation on theme: "Multi-AbstractionRetrievalMulti-AbstractionRetrieval MotivationMotivation ExperimentsExperiments Overall Framework Multi-Abstraction Concern Localization."— Presentation transcript:

1 Multi-AbstractionRetrievalMulti-AbstractionRetrieval MotivationMotivation ExperimentsExperiments Overall Framework Multi-Abstraction Concern Localization Tien-Duy B. Le, Shaowei Wang, and David Lo {btdle.2012, Abstraction Hierarchy Method Corpus Concerns Preprocessing Hierarchy Creation Level 1 Level 2 Level N …. Standard Retrieval Technique + Multi- Abstraction Retrieval Ranked Methods Per Concern We remove Java keywords, punctuation marks, special symbols, and break identifiers into tokens based on Camel casing convention Finally, we apply Porter Stemming algorithm to reduce English words into their root forms. We apply Latent Dirichlet Allocation (LDA), with different number of topics, a number of times, to construct an abstraction hierarchy Each application of LDA creates a topic model, which corresponds to an abstraction level. We refer to the number of topic models contained in a hierarchy as the height of the hierarchy Concern Localization is the process of locating code units that match a particular textual description (bug reports or feature requests) Recent concern localization techniques compare documents at one level of abstraction (i.e. words/topics) A word can be abstracted at multiple levels of abstraction. For example, Eindhoven can be abstracted to North Brabant, Netherlands, Western Europe, European Continent, Earth etc. In multi-abstraction concern localization, we represent documents at multiple abstraction levels by leveraging multiple topic models. Text Preprocessing Hierarchy Creation Step We propose multi-abstraction Vector Space Model (VSM MA ) by combining VSM with our abstraction hierarchy. In multi-abstraction VSM, document vectors are extended by adding elements corresponding to topics in the hierarchy. Given a query q and a document d in corpus D, the similarity between q and d is calculated in VSM MA as follows: V is the size of the original document vector w i is the i th word in d L is the height of abstraction hierarchy H H i is the i th abstraction level in the hierarchy is the probability of topic t i to appear in d as assigned by the k th topic model in abstraction hierarchy H tf-idf (w,d,D) is the term frequency-inverse document frequency of word w in document d given corpus D Effectiveness of Multi-Abstraction VSM Number of TopicsMAPImprovement Baseline (VSM)0.0669N/A H % H250, % H350, 100, % H450, 100, 150, % The MAP improvement of H4 (over baseline) is 19.36% The MAP is improved when the height of the abstraction hierarchy is increased Future Work Extend the experiments with combinations of  Different numbers of topics in each level of the hierarchy  Different hierarchy heights  Different topic models (Pachinko Allocation Model, Syntactic Topic Model, Hierarchical LDA)  Experiment with Panichella et al. ‘s method [1] to infer good LDA configurations for our approach [1]A. Panichella, B. Dit, R.Oliveto, M.D. Penta, D. Poshyvanyk, and A.D Lucia. How to effectively use topic models for software engineering tasks? an approach based on genetic algorithms. (ICSE 2013) Where


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