Many methods have been proposed to estimate how much effort is required to build and maintain software. Much of that research tries to recommend a single method – an approach that makes the dubious assumption that one method can handle the diversity of software project data. To address this drawback, we apply a configuration technique called “ROME” (Rapid Optimizing Methods for Estimation), which uses sequential model-based optimization (SMO) to find what configuration settings of effort estimation techniques work best for a particular data set.

Software developed on public platforms are a source of data that can be used to make predictions about those projects. While the activity of a single developer may be random and hard to predict, when large groups of developers work together on software projects, the resulting behavior can be predicted with good accuracy. In this study, we use 78,455 months of data from 1,628 GitHub projects to make various predictions about the current status of those projects. The predicting error rate can be greatly reduced using DECART hyperparameter optimization.

In order that the general public is not vulnerable to hackers, security bug reports need to be handled by small groups of engineers before being widely discussed. But learning how to distinguish the security bug reports from other bug reports is challenging since they may occur rarely. Data mining methods that can find such scarce targets require extensive optimization effort. Our approach can quickly optimize models that achieve better recalls than the prior state-of-the-art. These increases in recall are associated with moderate increases in false positive rates.

Machine learning-based security detection models have become prevalent in modern malware and intrusion detection systems. However, such models are susceptible to adversarial evasion attacks. In this type of attack, inputs are specially crafted by intelligent malicious adversaries, with the aim of being misclassified by existing state-ofthe-art models. Our methods can help security practitioners and researchers build a more robust model against adversarial evasion attack through the use of ensemble learningwith hyperparameter optimization.