FATOC:Bug Isolation Based Multi-Fault Localization by Using OPTICS Clustering

Bug isolation is a popular approach for multi-fault localization(MFL),where all failed test cases are clustered into several groups,and then the failed test cases in each group combined with all passed test cases are used to localize only a single fault.However,existing clustering algorithms cannot always obtain completely correct clustering results,which is a potential threat for bug isolation based MFL approaches.To address this issue,we first analyze the influence of the accuracy of the clustering on the performance of MFL,and the results of a controlled study indicate that using the clustering algorithm with the highest accuracy can achieve the best performance of MFL.Moreover,previous studies on clustering algorithms also show that the elements in a higher density cluster have a higher similarity.Based on the above motivation,we propose a novel approach FATOC(One-Fault-at-a-Time via OPTICS Clustering).In particular,FATOC first leverages the OPTICS(Ordering Points to Identify the Clustering Structure)clustering algorithm to group failed test cases,and then identifies a cluster with the highest density.OPTICS clustering is a density-based clustering algorithm,which can reduce the misgrouping and calculate a density value for each cluster.Such a density value of each cluster is helpful for finding a cluster with the highest clustering effectiveness.FATOC then combines the failed test cases in this cluster with all passed test cases to localize a single-fault through the traditional spectrum-based fault localization(SBFL)formula.After this fault is localized and fixed,FATOC will use the same method to localize the next single-fault,until all the test cases are passed.Our evaluation results show that FATOC can significantly outperform the traditional SBFL technique and a state-of-the-art MFL approach MSeer on 804 multi-faulty versions from nine real-world programs.Specifically,FATOC’s performance is 10.32%higher than that of traditional SBFL when using Ochiai formula in terms of metric A-EXAM.Besides,the results also indicate that,when checking 1%,3%and 5%statements of all subject programs,FATOC can locate 36.91%,48.50%and 66.93%of all faults respectively,which is also better than the traditional SBFL and the MFL approach MSeer.

Time-Dependent Density-Functional Calculations for Optical Spectra of Na2 and Na4 Clusters

With the frame of the time-dependent local density approximation, an efficient description of the optical response of clusters has been used to study the photo-absorption cross section of Na2 and Na4 clusters. It is shown that our calculated results are in good agreement with the experiment. In addition, our calculated spectrum for the Na4 cluster is in better agreement with experiment than the GW absorption spectrum.

Experimental investigation of cluster properties in dense gas-solid fluidized beds of different diameters

The local solid flow structure of a bubbling fluidized bed of sand particles was investigated m three different columns to characterize the properties of clusters. The experiments were performed using a reflective optical fiber probe. The variations in size, velocity, and void fraction of the clusters due to changes in the superficial gas velocity, particle size, and radial positions were studied. The results indicate that the velocity of the clusters remained unchanged while their size increased as the column diameter increased. In addition, the radial profile of the clusters＇ velocity did not depend on the radial position. The results indicate that larger particles form larger clusters, which move slower.

Asymmetric Inducing Synthesis of Optically Active Tetrahedral Cluster Containing SMCoW Core

The chiral clusters (m3-S)MCoW(CO)8[h5-C5H4C(O)OCH3] [M=Ru (2), Fe (3)] were synthesized by asymmetric induction of N-benzylcinchonium chloride as phase-transfer catalyst (PTC). The most suitable amount of PTC is 70 mol%. Cluster 3 was determined by single crystal X-ray diffraction analysis. The best ee of the chiral cluster is over 20%.