Concerning the problem that traditional lithology identification methods have low recognition accuracy and are difficult to integrate with geological experience organically, a multi-category Active Learning algorithm based on multiple Clustering algorithms and multivariate Linear regression algorithm (ALCL) was proposed. Firstly, the category matrix corresponding to each algorithm was obtained through multiple heterogeneous clustering algorithms, and the category matrices were labeled and pre-classified by querying common points. Secondly, the key examples used to train the weight coefficient model of the clustering algorithm were selected through the proposed priority largest search strategy and the most confusing query strategy. Thirdly, the objective solving function was defined, and the weight coefficients of clustering algorithms were obtained by training the key examples. Finally, the samples with high confidence in the results were classified by performing the classification calculation combined with the weight coefficient. Six public lithology datasets of oil wells in Daqing oilfield were used to carry out experiments. Experimental results show that when the classification accuracy of ALCL is the highest, it is improved by 2.07%-14.01% compared with those of the traditional supervised learning algorithms and other active learning algorithms. The results of hypothesis test and significance analysis prove that ALCL has better classification effect in lithology identification.

As the low ability of triggering the data racing fault of the existing mutation operators for concurrent program in mutation testing, some new mutation strategies based on data racing fault were proposed. From the viewpoint of mutation operator designing, Lock-oriented Mutation Strategy (LMS) and Shared-variable-oriented Mutation Strategy (SMS) were introduced, and two new mutation operators that named Synchronized Lock Resting Operator (SLRO) and Move Shared Variable Operator (MSVO) were designed. From the viewpoint of mutation point selection, also a new mutation point selection strategy named Synchronized relationship pair Mutation Point Selection Strategy (SMPSS) was proposed. SLRO and MSVO mutation operators were used to inject the faults which generated by SMPSS strategy on 12 Java current libraries, and then the ability of mutants to trigger the data racing fault was checked by using Java Path Finder (JPF). The results show that the SLRO and MSVO for 12 Java libs can generate 121 and 122 effective mutants respectively, and effectiveness rates are 95.28% and 99.19% respectively. In summary, the new current mutation operators and mutation strategies can effectively trigger the data racing fault.