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Found 3 papers, 1 papers with code
Do Not Take It for Granted: Comparing Open-Source Libraries for Software Development Effort Estimation
We carry out a thorough empirical study comparing the performance of the machine learners on 5 SEE datasets in the two most common SEE scenarios (i. e., out-of-the-box-ml and tuned-ml) as well as an in-depth analysis of the documentation and code of their APIs.
On The Effectiveness of One-Class Support Vector Machine in Different Defect Prediction Scenarios
While, we cannot conclude that OCSVM is the best classifier, our results still show interesting findings.
Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Replication Study
In the last decade, several studies have explored automated techniques to estimate the effort of agile software development.
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