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

Sales Model Selection for Second-hand vehicle E-commerce

The online second-hand vehicle sales models now include: auction model, consignment sales model, purchase and sales model, third party evaluation platform model and information consultant platform model. So choose a right sales model is important for sellers. We use AHP method to confirm key factors and built a score model base for different sales models. Though analysis, we can the conclusion that the best order of choice for online second-hand vehicle business model is: auction model, consignment sales model, purchase and sale model, information consultant platform and third party evaluation platform

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