Text mining, bug reports, topic mining, co-evolution

Proceeding of the 8th working conference on Mining software repositories - MSR '11 ◽

10.1145/1985441.1985481 ◽

2011 ◽

Cited By ~ 9

Author(s):

Olga Baysal ◽

Ian Davis ◽

Michael W. Godfrey

Keyword(s):

Text Mining ◽

Bug Reports ◽

Topic Mining

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Analyzing Bug Reports by Topic Mining in Software Evolution

10.1109/compsac51774.2021.00246 ◽

2021 ◽

Author(s):

Uy Nguyen ◽

Kowk Sun Cheng ◽

Samuel Sungmin Cho ◽

Myoungkyu Song

Keyword(s):

Software Evolution ◽

Bug Reports ◽

Topic Mining

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Identifying security bug reports via text mining: An industrial case study

2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010) ◽

10.1109/msr.2010.5463340 ◽

2010 ◽

Cited By ~ 78

Author(s):

Michael Gegick ◽

Pete Rotella ◽

Tao Xie

Keyword(s):

Text Mining ◽

Industrial Case Study ◽

Bug Reports

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Severity Prediction Of Bug Reports using Text Mining: A Systematic Review

2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) ◽

10.1109/icacccn.2018.8748582 ◽

2018 ◽

Author(s):

Arvinder Kaur ◽

Shubhra Goyal Jindal

Keyword(s):

Systematic Review ◽

Text Mining ◽

Bug Reports ◽

Severity Prediction

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MR-LDA

International Journal of Grid and High Performance Computing ◽

10.4018/ijghpc.2016100106 ◽

2016 ◽

Vol 8 (4) ◽

pp. 100-113 ◽

Cited By ~ 3

Author(s):

Xiongwen Pang ◽

Benshuai Wan ◽

Huifang Li ◽

Weiwei Lin

Keyword(s):

Text Mining ◽

Gibbs Sampling ◽

Efficient Method ◽

Sampling Method ◽

Latent Dirichlet Allocation ◽

Experimental Results ◽

Generation Process ◽

Effective Solution ◽

Topic Mining ◽

Dirichlet Allocation

Latent Dirichlet Allocation(LDA) is an efficient method of text mining,but applying LDA directly to Chinese micro-blog texts will not work well because micro-blogs are more social, brief, and closely related with each other. Based on LDA, this paper proposes a Micro-blog Relation LDA model (MR-LDA), which takes the relations between Chinese micro-blog documents and other Chinese micro-blog documents into consideration to help topic mining in micro-blog. The authors extend LDA in the following two points. First, they aggregate several Chinese micro-blogs as a single micro-blog document to solve the problem of short texts. Second, they model the generation process of Chinese micro-blogs more accurately by taking relationship between micro-blog documents into consideration. MR-LDA is more suitable to model Chinese micro-blog data. Gibbs sampling method is borrowed to inference the model. Experimental results on actual datasets show that MR-LDA model can offer an effective solution to text mining for Chinese micro-blog.

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