Bug localization using latent Dirichlet allocation

Lukins, Stacy K.; Kraft, Nicholas A.; Etzkorn, Letha H.

doi:10.1016/j.infsof.2010.04.002

Cited by 267 publications

(173 citation statements)

References 25 publications

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“…IR-Based Bug Localization: Various IR-based bug localization approaches that employ information retrieval techniques to calculate the similarity between a bug report and a program element (e.g., a method or a source code file) have been proposed [39,30,22,44,57,41,47,48,53].…”

Section: Related Workmentioning

confidence: 99%

Information retrieval and spectrum based bug localization: better together

Oentaryo

2015

Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

112

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Debugging often takes much effort and resources. To help developers debug, numerous information retrieval (IR)-based and spectrum-based bug localization techniques have been proposed. IR-based techniques process textual information in bug reports, while spectrum-based techniques process program spectra (i.e., a record of which program elements are executed for each test case). Both eventually generate a ranked list of program elements that are likely to contain the bug. However, these techniques only consider one source of information, either bug reports or program spectra, which is not optimal. To deal with the limitation of existing techniques, in this work, we propose a new multi-modal technique that considers both bug reports and program spectra to localize bugs. Our approach adaptively creates a bug-specific model to map a particular bug to its possible location, and introduces a novel idea of suspicious words that are highly associated to a bug. We evaluate our approach on 157 real bugs from four software systems, and compare it with a state-of-the-art IR-based bug localization method, a state-of-the-art spectrum-based bug localization method, and three state-of-the-art multi-modal feature location methods that are adapted for bug localization. Experiments show that our approach can outperform the baselines by at least 47.62%, 31.48%, 27.78%, and 28.80% in terms of number of bugs successfully localized when a developer inspects 1, 5, and 10 program elements (i.e., Top 1, Top 5, and Top 10), and Mean Average Precision (MAP) respectively.

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Section: Related Workmentioning

confidence: 99%

Information retrieval and spectrum based bug localization: better together

Oentaryo

2015

Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

112

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“…Xie Bing [19] from Beijing university proposed a function recognition approach based on LDA and code static analysis technology o better support the activity of code reuse. In addition, topic model was also used to study class cohesion [20]and bug location [21].…”

Section: Overview Of Topic Modelmentioning

confidence: 99%

A Novel Approach Based on Topic Modeling for Clone Group Mapping

Zhang¹,

Zhang²,

Wang³

et al. 2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…Researchers have used topic modeling to support varied software engineering tasks, including traceability link recovery [9], concept/feature location [10], source code metrics [11], and many other tasks [12], [13], however, the application of using topic modeling to support interactive program comprehension has not got as much attention. Also, there are gaps between knowing and doing when applying topic modeling to software engineering practice.…”

Section: Introductionmentioning

confidence: 99%

JSEA: A Program Comprehension Tool Adopting LDA-based Topic Modeling

Wang¹,

Liu²

2017

ijacsa

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Abstract-Understanding a large number of source code is a big challenge for software development teams in software maintenance process. Using topic models is a promising way to automatically discover feature and structure from textual software assets, and thus support developers comprehending programs on software maintenance. To explore the application of applying topic modeling to software engineering practice, we proposed JSEA (Java Software Engineers Assistant), an interactive program comprehension tool adopting LDA-based topic modeling, to support developers during performing software maintenance tasks. JSEA utilizes essential information automatically generated from Java source code to establish a project overview and to bring search capability for software engineers. The results of our preliminary experimentation suggest the practicality of JSEA.

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Bug localization using latent Dirichlet allocation

Cited by 267 publications

References 25 publications

Information retrieval and spectrum based bug localization: better together

Information retrieval and spectrum based bug localization: better together

A Novel Approach Based on Topic Modeling for Clone Group Mapping

JSEA: A Program Comprehension Tool Adopting LDA-based Topic Modeling

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