357 research outputs found
What Causes My Test Alarm? Automatic Cause Analysis for Test Alarms in System and Integration Testing
Driven by new software development processes and testing in clouds, system
and integration testing nowadays tends to produce enormous number of alarms.
Such test alarms lay an almost unbearable burden on software testing engineers
who have to manually analyze the causes of these alarms. The causes are
critical because they decide which stakeholders are responsible to fix the bugs
detected during the testing. In this paper, we present a novel approach that
aims to relieve the burden by automating the procedure. Our approach, called
Cause Analysis Model, exploits information retrieval techniques to efficiently
infer test alarm causes based on test logs. We have developed a prototype and
evaluated our tool on two industrial datasets with more than 14,000 test
alarms. Experiments on the two datasets show that our tool achieves an accuracy
of 58.3% and 65.8%, respectively, which outperforms the baseline algorithms by
up to 13.3%. Our algorithm is also extremely efficient, spending about 0.1s per
cause analysis. Due to the attractive experimental results, our industrial
partner, a leading information and communication technology company in the
world, has deployed the tool and it achieves an average accuracy of 72% after
two months of running, nearly three times more accurate than a previous
strategy based on regular expressions.Comment: 12 page
Inducible expression of Wnt7b promotes bone formation in aged mice and enhances fracture healing
Ultra-broadband Microwave Metamaterial Absorber
A microwave ultra-broadband polarization-independent metamaterial absorber is
demonstrated. It is composed of a periodic array of metal-dielectric
multilayered quadrangular frustum pyramids. These pyramids possess resonant
absorption modes at multi-frequencies, of which the overlapping leads to the
total absorption of the incident wave over an ultra-wide spectral band. The
experimental absorption at normal incidence is above 90% in the frequency range
of 7.8-14.7GHz, and the absorption is kept large when the incident angle is
smaller than 60 degrees. The experimental results agree well with the numerical
simulation.Comment: 10 pages, 5 figures,submitted to AP
Unveiling hidden stellar aggregates in the Milky Way: 1656 new star clusters found in Gaia EDR3
We report 1,656 new star clusters found in the Galactic disk (|b|<20 degrees)
beyond 1.2 kpc, using Gaia EDR3 data. Based on an unsupervised machine learning
algorithm, DBSCAN, and followed our previous studies, we utilized a unique
method to do the data preparation and obtained the clustering coefficients,
which proved to be an effective way to search blindly for star clusters. We
tabulated the physical parameters and member stars of the new clusters, and
presented some interesting examples, including a globular cluster candidate.
The cluster parameters and member stars are available at CDS via anonymous ftp
to https://cdsarc.cds.unistra.fr/ftp/vizier.submit//he22c. We examined the new
discoveries and discussed their statistical properties. The proper motion
dispersions and radii of the new clusters were the same as the previously
reported ones. The new star clusters beyond 1.2 kpc were older than those in
the solar neighborhood, and the new objects found in the third Galactic
quadrant presented the lowest line-of-sight extinctions. Combined with our
previous results, the total population of new clusters detected through our
method was 2,541, corresponding to 55% of all newly published clusters in the
Gaia era. The number of cataloged Gaia star clusters was also increased to
nearly six thousand. In the near future, it is necessary to make a unified
confirmation and member star determination for all reported clusters.Comment: 16 pages, 11 figures, 3 tables with full clusters/members data link
in CDS, accepted for publication in ApJ
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