Search Results for author:
Found 10 papers, 9 papers with code
Unicorn: Reasoning about Configurable System Performance through the lens of Causality
Understanding and reasoning about the performance behavior of highly configurable systems, over a vast and variable space, is challenging.
An Expert System for Redesigning Software for Cloud Applications
When services are divided into many independent components, they are easier to update.
Deep Learning based Vulnerability Detection: Are We There Yet?
In this paper, we ask, "how well do the state-of-the-art DL-based techniques perform in a real-world vulnerability prediction scenario?".
Software Engineering
MTFuzz: Fuzzing with a Multi-Task Neural Network
The compact embedding can be used to guide the mutation process effectively by focusing most of the mutations on the parts of the embedding where the gradient is high.
Software Engineering
Methods for Stabilizing Models across Large Samples of Projects (with case studies on Predicting Defect and Project Health)
To the best of our knowledge, STABILIZER is order of magnitude faster than the prior state-of-the-art transfer learners which seek to find conclusion stability, and these case studies are the largest demonstration of the generalizability of quantitative predictions of project quality yet reported in the SE literature.
Whence to Learn? Transferring Knowledge in Configurable Systems using BEETLE
To resolve these problems, we propose a novel transfer learning framework called BEETLE, which is a "bellwether"-based transfer learner that focuses on identifying and learning from the most relevant source from amongst the old data.
Software Engineering
ConEx: Efficient Exploration of Big-Data System Configurations for Better Performance
For cost reduction, we developed and experimentally tested and validated two approaches: using scaled-up big data jobs as proxies for the objective function for larger jobs and using a dynamic job similarity measure to infer that results obtained for one kind of big data problem will work well for similar problems.
Hyperparameter Optimization for Effort Estimation
We test OIL on a wide range of hyperparameter optimizers using data from 945 software projects.
Software Engineering
Transfer Learning with Bellwethers to find Good Configurations
Using this insight, this paper proposes BEETLE, a novel bellwether based transfer learning scheme, which can identify a suitable source and use it to find near-optimal configurations of a software system.
Software Engineering
Learning Effective Changes For Software Projects
The current generation of software analytics tools are mostly prediction algorithms (e. g. support vector machines, naive bayes, logistic regression, etc).
Software Engineering
