92,362 research outputs found
A Survey on Compiler Autotuning using Machine Learning
Since the mid-1990s, researchers have been trying to use machine-learning
based approaches to solve a number of different compiler optimization problems.
These techniques primarily enhance the quality of the obtained results and,
more importantly, make it feasible to tackle two main compiler optimization
problems: optimization selection (choosing which optimizations to apply) and
phase-ordering (choosing the order of applying optimizations). The compiler
optimization space continues to grow due to the advancement of applications,
increasing number of compiler optimizations, and new target architectures.
Generic optimization passes in compilers cannot fully leverage newly introduced
optimizations and, therefore, cannot keep up with the pace of increasing
options. This survey summarizes and classifies the recent advances in using
machine learning for the compiler optimization field, particularly on the two
major problems of (1) selecting the best optimizations and (2) the
phase-ordering of optimizations. The survey highlights the approaches taken so
far, the obtained results, the fine-grain classification among different
approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our
Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated
quarterly here (Send me your new published papers to be added in the
subsequent version) History: Received November 2016; Revised August 2017;
Revised February 2018; Accepted March 2018
Fault Detection Effectiveness of Metamorphic Relations Developed for Testing Supervised Classifiers
In machine learning, supervised classifiers are used to obtain predictions
for unlabeled data by inferring prediction functions using labeled data.
Supervised classifiers are widely applied in domains such as computational
biology, computational physics and healthcare to make critical decisions.
However, it is often hard to test supervised classifiers since the expected
answers are unknown. This is commonly known as the \emph{oracle problem} and
metamorphic testing (MT) has been used to test such programs. In MT,
metamorphic relations (MRs) are developed from intrinsic characteristics of the
software under test (SUT). These MRs are used to generate test data and to
verify the correctness of the test results without the presence of a test
oracle. Effectiveness of MT heavily depends on the MRs used for testing. In
this paper we have conducted an extensive empirical study to evaluate the fault
detection effectiveness of MRs that have been used in multiple previous studies
to test supervised classifiers. Our study uses a total of 709 reachable mutants
generated by multiple mutation engines and uses data sets with varying
characteristics to test the SUT. Our results reveal that only 14.8\% of these
mutants are detected using the MRs and that the fault detection effectiveness
of these MRs do not scale with the increased number of mutants when compared to
what was reported in previous studies.Comment: 8 pages, AITesting 201
Ways of Applying Artificial Intelligence in Software Engineering
As Artificial Intelligence (AI) techniques have become more powerful and
easier to use they are increasingly deployed as key components of modern
software systems. While this enables new functionality and often allows better
adaptation to user needs it also creates additional problems for software
engineers and exposes companies to new risks. Some work has been done to better
understand the interaction between Software Engineering and AI but we lack
methods to classify ways of applying AI in software systems and to analyse and
understand the risks this poses. Only by doing so can we devise tools and
solutions to help mitigate them. This paper presents the AI in SE Application
Levels (AI-SEAL) taxonomy that categorises applications according to their
point of AI application, the type of AI technology used and the automation
level allowed. We show the usefulness of this taxonomy by classifying 15 papers
from previous editions of the RAISE workshop. Results show that the taxonomy
allows classification of distinct AI applications and provides insights
concerning the risks associated with them. We argue that this will be important
for companies in deciding how to apply AI in their software applications and to
create strategies for its use
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