Proceedings ArticleDOI
Mining business topics in source code using latent dirichlet allocation
Girish Maskeri,Santonu Sarkar,Kenneth Heafield +2 more
- pp 113-120
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TLDR
Preliminary results indicate that LDA is able to identify some of the domain topics and is a satisfactory starting point for further manual refinement of topics, and a human assisted approach based on LDA for extracting domain topics from source code is proposed.Abstract:
One of the difficulties in maintaining a large software system is the absence of documented business domain topics and correlation between these domain topics and source code. Without such a correlation, people without any prior application knowledge would find it hard to comprehend the functionality of the system. Latent Dirichlet Allocation (LDA), a statistical model, has emerged as a popular technique for discovering topics in large text document corpus. But its applicability in extracting business domain topics from source code has not been explored so far. This paper investigates LDA in the context of comprehending large software systems and proposes a human assisted approachbased on LDA for extracting domain topics from source code. This method has been applied on a number of open source and proprietary systems. Preliminary results indicate that LDA is able to identify some of the domain topics and isa satisfactory starting point for further manual refinement of topicsread more
Citations
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What is wrong with topic modeling? And how to fix it using search-based software engineering
TL;DR: LDADE, a search-based software engineering tool which uses Differential Evolution (DE) to tune the LDA’s parameters, is used to provide a method in which distributions generated by LDA are more stable and can be used for further analysis.
References
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Proceedings Article
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