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cs.SE2021
Lessons learned from hyper-parameter tuning for microservice candidate identification
Rahul Yedida, Rahul Krishna, Anup Kalia +3
When optimizing software for the cloud, monolithic applications need to be partitioned into many smaller *microservices*. While many tools have been proposed for this task, we warn…
cs.SE2020
On the Value of Oversampling for Deep Learning in Software Defect Prediction
Rahul Yedida, Tim Menzies
One truism of deep learning is that the automatic feature engineering (seen in the first layers of those networks) excuses data scientists from performing tedious manual feature en…
cs.SE2020
Learning to Recognize Actionable Static Code Warnings (is Intrinsically Easy)
Xueqi Yang, Jianfeng Chen, Rahul Yedida +2
Static code warning tools often generate warnings that programmers ignore. Such tools can be made more useful via data mining algorithms that select the "actionable" warnings; i.e.…