The software industry has not adopted continuous use of static architecture conformance checking. One hindrance is the needed mapping from source code elements to elements of the architecture. We present a novel approach of generating and combining dependency and semantic information extracted from an initial set of mapped source code files. We use this to train a Naive Bayes classifier that is then used to map the remainder of the source code files. We compare this approach with the HuGMe technique on six open source projects with known mappings. We find that our approach provides an average performance improvement of 0.22 and an average precision and recall F1-score improvement of 0.26 in comparison to HuGMe.
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Semi-automatic mapping of source code using naive Bayes
Semantic Scholar · Computer Science · 2019
Abstract
The software industry has not adopted continuous use of static architecture conformance checking. One hindrance is the needed mapping from source code elements to elements of the architecture. We present a novel approach of generating and combining dependency and semantic information extracted from an initial set of mapped source code files. We use this to train a Naive Bayes classifier that is then used to map the remainder of the source code files. We compare this approach with the HuGMe technique on six open source projects with known mappings. We find that our approach provides an average performance improvement of 0.22 and an average precision and recall F1-score improvement of 0.26 in comparison to HuGMe.