54,281 research outputs found
The Co-Evolution of Test Maintenance and Code Maintenance through the lens of Fine-Grained Semantic Changes
Automatic testing is a widely adopted technique for improving software
quality. Software developers add, remove and update test methods and test
classes as part of the software development process as well as during the
evolution phase, following the initial release. In this work we conduct a large
scale study of 61 popular open source projects and report the relationships we
have established between test maintenance, production code maintenance, and
semantic changes (e.g, statement added, method removed, etc.). performed in
developers' commits.
We build predictive models, and show that the number of tests in a software
project can be well predicted by employing code maintenance profiles (i.e., how
many commits were performed in each of the maintenance activities: corrective,
perfective, adaptive). Our findings also reveal that more often than not,
developers perform code fixes without performing complementary test maintenance
in the same commit (e.g., update an existing test or add a new one). When
developers do perform test maintenance, it is likely to be affected by the
semantic changes they perform as part of their commit.
Our work is based on studying 61 popular open source projects, comprised of
over 240,000 commits consisting of over 16,000,000 semantic change type
instances, performed by over 4,000 software engineers.Comment: postprint, ICSME 201
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IMRT QA using machine learning: A multi-institutional validation.
PurposeTo validate a machine learning approach to Virtual intensity-modulated radiation therapy (IMRT) quality assurance (QA) for accurately predicting gamma passing rates using different measurement approaches at different institutions.MethodsA Virtual IMRT QA framework was previously developed using a machine learning algorithm based on 498 IMRT plans, in which QA measurements were performed using diode-array detectors and a 3%local/3 mm with 10% threshold at Institution 1. An independent set of 139 IMRT measurements from a different institution, Institution 2, with QA data based on portal dosimetry using the same gamma index, was used to test the mathematical framework. Only pixels with ≥10% of the maximum calibrated units (CU) or dose were included in the comparison. Plans were characterized by 90 different complexity metrics. A weighted poison regression with Lasso regularization was trained to predict passing rates using the complexity metrics as input.ResultsThe methodology predicted passing rates within 3% accuracy for all composite plans measured using diode-array detectors at Institution 1, and within 3.5% for 120 of 139 plans using portal dosimetry measurements performed on a per-beam basis at Institution 2. The remaining measurements (19) had large areas of low CU, where portal dosimetry has a larger disagreement with the calculated dose and as such, the failure was expected. These beams need further modeling in the treatment planning system to correct the under-response in low-dose regions. Important features selected by Lasso to predict gamma passing rates were as follows: complete irradiated area outline (CIAO), jaw position, fraction of MLC leafs with gaps smaller than 20 or 5 mm, fraction of area receiving less than 50% of the total CU, fraction of the area receiving dose from penumbra, weighted average irregularity factor, and duty cycle.ConclusionsWe have demonstrated that Virtual IMRT QA can predict passing rates using different measurement techniques and across multiple institutions. Prediction of QA passing rates can have profound implications on the current IMRT process
Structure-preserving mesh coupling based on the Buffa-Christiansen complex
The state of the art for mesh coupling at nonconforming interfaces is
presented and reviewed. Mesh coupling is frequently applied to the modeling and
simulation of motion in electromagnetic actuators and machines. The paper
exploits Whitney elements to present the main ideas. Both interpolation- and
projection-based methods are considered. In addition to accuracy and
efficiency, we emphasize the question whether the schemes preserve the
structure of the de Rham complex, which underlies Maxwell's equations. As a new
contribution, a structure-preserving projection method is presented, in which
Lagrange multiplier spaces are chosen from the Buffa-Christiansen complex. Its
performance is compared with a straightforward interpolation based on Whitney
and de Rham maps, and with Galerkin projection.Comment: 17 pages, 7 figures. Some figures are omitted due to a restricted
copyright. Full paper to appear in Mathematics of Computatio
MLPerf Inference Benchmark
Machine-learning (ML) hardware and software system demand is burgeoning.
Driven by ML applications, the number of different ML inference systems has
exploded. Over 100 organizations are building ML inference chips, and the
systems that incorporate existing models span at least three orders of
magnitude in power consumption and five orders of magnitude in performance;
they range from embedded devices to data-center solutions. Fueling the hardware
are a dozen or more software frameworks and libraries. The myriad combinations
of ML hardware and ML software make assessing ML-system performance in an
architecture-neutral, representative, and reproducible manner challenging.
There is a clear need for industry-wide standard ML benchmarking and evaluation
criteria. MLPerf Inference answers that call. In this paper, we present our
benchmarking method for evaluating ML inference systems. Driven by more than 30
organizations as well as more than 200 ML engineers and practitioners, MLPerf
prescribes a set of rules and best practices to ensure comparability across
systems with wildly differing architectures. The first call for submissions
garnered more than 600 reproducible inference-performance measurements from 14
organizations, representing over 30 systems that showcase a wide range of
capabilities. The submissions attest to the benchmark's flexibility and
adaptability.Comment: ISCA 202
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