Natural language or not (NLON) : a package for software engineering text analysis pipeline
Mäntylä, Mika V.; Calefato, Fabio; Claes, Maëlick (2018-05-28)
Mika V. Mäntylä, Fabio Calefato, and Maelick Claes. 2018. Natural language or not (NLON): a package for software engineering text analysis pipeline. In Proceedings of the 15th International Conference on Mining Software Repositories (MSR '18). ACM, New York, NY, USA, 387-391. DOI: https://doi.org/10.1145/3196398.3196444
© 2018 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 15th International Conference on Mining Software Repositories (MSR '18). DOI: https://doi.org/10.1145/3196398.3196444.
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https://urn.fi/URN:NBN:fi-fe201901041335
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Abstract
textual information to separate natural language from other information, such as log messages, that are often part of the communication in software engineering. We present a simple approach for classifying whether some textual input is natural language or not. Although our NLoN package relies on only 11 language features and character tri-grams, we are able to achieve an area under the ROC curve performances between 0.976–0.987 on three different data sources, with Lasso regression from Glmnet as our learner and two human raters for providing ground truth. Cross-source prediction performance is lower and has more fluctuation with top ROC performances from 0.913 to 0.980. Compared with prior work, our approach offers similar performance but is considerably more lightweight, making it easier to apply in software engineering text mining pipelines. Our source code and data are provided as an R-package for further improvements.
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