Bug localization using latent Dirichlet allocation

By Stacy K. Lukins, Nicholas A. Kraft, Letha H. Etzkorn. Information and Software Technology, 2010

319 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/5535d452e6d70342096c8c3768d7071d3a540443

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Razvan C. Bunescu
2. Xin Ye
3. Chang Liu
4. Hongyu Zhang
5. Denys Poshyvanyk
6. David Lo
7. Massimiliano Di Penta
8. Rocco Oliveto

In Software

1. Xin Ye
2. Chang Liu
3. Razvan C. Bunescu
4. Hongyu Zhang
5. Denys Poshyvanyk
6. Massimiliano Di Penta
7. Rocco Oliveto
8. David Lo

Citations in the corpus

• Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports (2012, methodology): “Our experiments also show that BugLocator outperforms existing bug localization methods using Vector Space Model (VSM) [32], Latent Dirichlet Allocation (LDA) [25], Latent Semantic Indexing ( LSI) [30, 31], and Smoothed Unigram Model (SUM) [32].”
• How to effectively use topic models for software engineering tasks? An approach based on Genetic Algorithms (2013, methodology): “A number of approaches using LSI and LDA have been proposed to support software engineering tasks: feature location [4], change impact analysis [5], bug localization [6], clone detection [7], traceability link recovery [8], [9], expert developer recommendation [10], code measurement [11], [12], arti”
• NIRMAL: Automatic identification of software relevant tweets leveraging language model (2015, background): “In recent years, many researchers have applied and customized techniques from Natural Language Processing (NLP) domain to analyze software data [2], [4], [6], [11], [14], [18], [23].”
• Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey (2017, methodology): “, called LACT. For evaluation of LACT, used 43 open-source software systems in dierent programming languages and showed LACT can categorize software systems based on type of programming language. In [23, 24] proposed an approach topic modeling based on LDA model for the purpose of bug localization. ”
• Feature location in source code: a taxonomy and survey (2013, methodology): “Recently, LDA has been applied for bug localization [29, 65].”
• Improving bug localization using structured information retrieval (2013, methodology): “[21] proposed a Latent Dirichlet Allocation (LDA) approach, while Rao et al.”
• Topic Modeling Using Latent Dirichlet allocation (2021, methodology): “In a relevant study, LDA was employed to determine the bug in the source code [84] by Lukins et al. A bug report based query was shaped and decided the portion of source code to correct it.”
• 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].”