Predicting software change-proneness with code smells and class imbalance learning
Arvinder Kaur; Kamaldeep Kaur; Shilpi Jain · 2016
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
The objective of this paper is to study the relationship between different types of object-oriented software metrics, code smells and actual changes in software code that occur during maintenance period. It is hypothesized that code smells…
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WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: OpenAlex · DOI 10.1109/icacci.2016.7732136