Semantic clustering: Identifying topics in source code

@article{Kuhn2007SemanticCI,
  title={Semantic clustering: Identifying topics in source code},
  author={Adrian Kuhn and St{\'e}phane Ducasse and Tudor G{\^i}rba},
  journal={Inf. Softw. Technol.},
  year={2007},
  volume={49},
  pages={230-243}
}
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497 Citations
Highly Influential Citations
40
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150
Methods Citations
158
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497 Citations

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Normalized Software Distance (nsd), an information-theoretic method that captures semantic relatedness in source code by exploiting the distributional cues of code terms across the system, is proposed.
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A novel measure Conceptual Conformity (CC) is proposed, which computes the similarity between two latent topic distributions obtained from both the source code and its package, and is used to identify source code that is not relevant to the package’s semantic context and define it as a semantic outlier.
Supporting program comprehension with program summarization
TLDR
This paper proposes to use latent semantic indexing and clustering to group source artifacts with similar vocabulary to analyze the composition of each package in the program and employs Minipar, a nature language parser, to help generate the summaries.
Using Developers Contributions on Software Vocabularies to Identify Experts
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Results confirm similarity between vocabularies might be explored to point out code experts and can recommend among current team members one whose vocabulary is closest to the entity for orphaned entities.
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References

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