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05/21/2022
How to Find Actionable Static Analysis Warnings
Automatically generated static code warnings suffer from a large number ...
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02/02/2022
How to Improve Deep Learning for Software Analytics (a case study with code smell detection)
To reduce technical debt and make code more maintainable, it is importan...
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09/29/2021
Partitioning Cloud-based Microservices (via Deep Learning)
Cloud-based software has many advantages. When services are divided into...
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08/15/2021
Crowdsourcing the State of the Art(ifacts)
In any field, finding the "leading edge" of research is an on-going chal...
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06/12/2021
Lessons learned from hyper-parameter tuning for microservice candidate identification
When optimizing software for the cloud, monolithic applications need to ...
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01/15/2021
When SIMPLE is better than complex: A case study on deep learning for predicting Bugzilla issue close time
Is deep learning over-hyped? Where are the case studies that compare sta...
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10/22/2020
Text Mining to Identify and Extract Novel Disease Treatments From Unstructured Datasets
Objective: We aim to learn potential novel cures for diseases from unstr...
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08/09/2020
Improving Deep Learning for Defect Prediction (using the GHOST Hyperparameter Optimizer)
There has been much recent interest in the application of deep learning ...
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05/31/2020
How to Recognize Actionable Static Code Warnings (Using Linear SVMs)
Static code warning tools often generate warnings that programmers ignor...
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05/18/2020
Parsimonious Computing: A Minority Training Regime for Effective Prediction in Large Microarray Expression Data Sets
Rigorous mathematical investigation of learning rates used in back-propa...
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06/01/2019
Evolution of Novel Activation Functions in Neural Network Training with Applications to Classification of Exoplanets
We present analytical exploration of novel activation functions as conse...
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02/20/2019
A novel adaptive learning rate scheduler for deep neural networks
Optimizing deep neural networks is largely thought to be an empirical pr...
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06/19/2018