37,179 research outputs found
Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration
Testing in Continuous Integration (CI) involves test case prioritization,
selection, and execution at each cycle. Selecting the most promising test cases
to detect bugs is hard if there are uncertainties on the impact of committed
code changes or, if traceability links between code and tests are not
available. This paper introduces Retecs, a new method for automatically
learning test case selection and prioritization in CI with the goal to minimize
the round-trip time between code commits and developer feedback on failed test
cases. The Retecs method uses reinforcement learning to select and prioritize
test cases according to their duration, previous last execution and failure
history. In a constantly changing environment, where new test cases are created
and obsolete test cases are deleted, the Retecs method learns to prioritize
error-prone test cases higher under guidance of a reward function and by
observing previous CI cycles. By applying Retecs on data extracted from three
industrial case studies, we show for the first time that reinforcement learning
enables fruitful automatic adaptive test case selection and prioritization in
CI and regression testing.Comment: Spieker, H., Gotlieb, A., Marijan, D., & Mossige, M. (2017).
Reinforcement Learning for Automatic Test Case Prioritization and Selection
in Continuous Integration. In Proceedings of 26th International Symposium on
Software Testing and Analysis (ISSTA'17) (pp. 12--22). AC
Atari games and Intel processors
The asynchronous nature of the state-of-the-art reinforcement learning
algorithms such as the Asynchronous Advantage Actor-Critic algorithm, makes
them exceptionally suitable for CPU computations. However, given the fact that
deep reinforcement learning often deals with interpreting visual information, a
large part of the train and inference time is spent performing convolutions. In
this work we present our results on learning strategies in Atari games using a
Convolutional Neural Network, the Math Kernel Library and TensorFlow 0.11rc0
machine learning framework. We also analyze effects of asynchronous
computations on the convergence of reinforcement learning algorithms
Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond
In this and a set of companion whitepapers, the USQCD Collaboration lays out
a program of science and computing for lattice gauge theory. These whitepapers
describe how calculation using lattice QCD (and other gauge theories) can aid
the interpretation of ongoing and upcoming experiments in particle and nuclear
physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data
In domains like bioinformatics, information retrieval and social network
analysis, one can find learning tasks where the goal consists of inferring a
ranking of objects, conditioned on a particular target object. We present a
general kernel framework for learning conditional rankings from various types
of relational data, where rankings can be conditioned on unseen data objects.
We propose efficient algorithms for conditional ranking by optimizing squared
regression and ranking loss functions. We show theoretically, that learning
with the ranking loss is likely to generalize better than with the regression
loss. Further, we prove that symmetry or reciprocity properties of relations
can be efficiently enforced in the learned models. Experiments on synthetic and
real-world data illustrate that the proposed methods deliver state-of-the-art
performance in terms of predictive power and computational efficiency.
Moreover, we also show empirically that incorporating symmetry or reciprocity
properties can improve the generalization performance
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