Retrieval from software libraries for bug localization: a comparative study of generic and composite text models

By Shivani Rao, A. Kak. IEEE Working Conference on Mining Software Repositories, 2011

298 citations recorded by the source corpus.

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Learning to rank relevant files for bug reports using domain knowledge (2014, methodology): “These include approaches based on LDA [25, 34, 40], Latent Semantic Indexing (LSI) [40, 45], Smoothed Unigram Model (SUM) [40, 45], and SVMs [19, 34].”
Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports (2012, methodology): “SUM, which was used by Rao and Kak [32] for bug localization.”
From Word Embeddings to Document Similarities for Improved Information Retrieval in Software Engineering (2016, methodology): “[37] compare various IR techniques on bug localization, and report that traditional IR techniques such as VSM and Unigram Model (UM) outperform the more sophisticated LSI and LDA techniques.”
A Survey on Software Fault Localization (2016, result): “Recently, some studies [85], [234], [298], [315], [425] have applied information retrieval techniques to software fault localization.”
Improving bug localization using structured information retrieval (2013, methodology): “[29] compared a range of IR techniques: Unigram, Vector Space, Latent Semantic Analysis (LSA), LDA, Cluster Based, and various combinations.”