Learning to rank relevant files for bug reports using domain knowledge

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Practitioners' expectations on automated fault localization (2016, unknown): “Scalability 90% [29], [52] 75% [16], [24], [56], [59], [60] 50% [4], [5], [35], [40], [44], [55], [57] ? [19]”
From Word Embeddings to Document Similarities for Improved Information Retrieval in Software Engineering (2016, methodology): “MAP and MRR are standard evaluation metrics in IR, and were used previously in related work on bug localization [38, 46, 47].”
Easy over hard: a case study on deep learning (2017, result): “By comparing it to baseline methods (Naive Bayes [32], learn-to-rank [76], BugLocator [79]), Lam et al.”
Scaffle: bug localization on millions of files (2020, methodology): “Overall, Scaffle provides a practical technique for pinpointing the files to consider in a large-scale code base, which helps assigning crashes to the appropriate team or developer, and may serve as a starting point for automated program repair.”
Learning to rank code examples for code search engines (2017, methodology): “Therefore, as suggested by Ye et al. study (Ye et al. 2014), as a practical solution for building the training dataset in the field of learning-to-rank, we consider only the code snippets that are the most similar to the query as the candidate code examples.”
Deep Code Search (2018, methodology): “The MRR [45, 79] is the average of the reciprocal ranks of results of a set of queries Q .”