Aiming at the problem of high running power consumption and slow operation of Convolutional Neural Network (CNN) on resource-constrained hardware devices, a method for accelerating fixed-point computation of CNN based on Field Programmable Gate Array (FPGA) was proposed. First, a fixed-point processing method was proposed. In order to reduce the storage space of the CNN parameters, different scale parameters were designed for different convolution layers and the relative divergence was used to determine the bit width length. The effect of different quantization intervals on the accuracy of CNN was studied. Then, the parameter multiplexing method and the pipeline calculation method were designed to accelerate the convolution calculation. In order to verify the acceleration effect of CNN after fixed-point processing, two datasets of face and ship were used for verification. Compared with the traditional floating-point convolution computation, on the premise of ensuring that the accuracy loss of the CNN is small, when the weight parameters and the input feature map parameters are quantized to 7-bit, on the face recognition CNN model, the proposed method has the compressed weight parameter file size of about 22% of the origin, and the convolution calculation speedup is 18.69. At the same time, the method makes the utilization rate of the multiplier-accumulator in FPGA reach 94.5%. Experimental results show that the proposed method can improve the speed of convolution calculation, and efficiently use FPGA hardware resources.

To solve the problem of fixed individual scale and high execution cost of weak mutation test case set generation based on Set Evolutionary Algorithm (SEA), a generation method of weak mutation test case set based on Dynamic Set Evolutionary Algorithm (DSEA) was proposed. The test case sets were used as individuals to generate some weak mutations to cover all mutant branches. In the evolutionary process, according to the minimum subset of the optimal individuals and the number of uncovered mutation branches, the minimum scale of the required test case set was calculated by the set compact operator. And the size of all individuals in the population was adjusted based on the minimum scale to generate the smallest scale of the weak mutation test case set. At the same time, a fitness function for assessing a use case set as an individual was designed. The experimental results show that when the dynamic ensemble evolution algorithm is used to guide the generation of weak mutation test cases, and the scale of the test cases was 50.15% lower than the initial size of the individuals, and the execution time is lower than that of SEA by 74.58% at most. Thus, the dynamic ensemble evolution algorithm provides a solution for generating of the weak mutation test case set with minimum scale and enhancing the algorithm speed.

In Many-objective Optimization Problems (MaOP), the Pareto dominance has exponential increase of non-dominated solutions and the decrease of selection pressure with increasing optimization objectives. To solve these issues, a new type of dominance, namely Linear Weighted Minimal/Maximal dominance (LWM-dominance) was proposed based on the ideas of comparing multi-objective solutions by using linear weighted aggregation and Pareto dominance. It is theoretically proved that LWM non-dominated solution set is a subset of Pareto non-dominated solution set, meanwhile the important corner solutions are reserved. Furthermore, an MaOP algorithm based on LWM dominance was presented. The empirical studies proved the corollaries of the proposed LWM dominance. In detail, the experimental results in random objective space show that the LWM dominance is suitable for the MaOPs with 5-15 objectives; the experiment on comparing the number of LWM non-dominated solutions and Pareto non-dominated solutions with subjects of DTLZ1-DTLZ7 shows that the proportion of non-dominated solutions decreases by about 17% on average when the number of optimization objectives is 10 and 15.

The Ant Colony Optimization (ACO) has slow convergence and is easily trapped in local optimum when solving Multi-Objective Test Case Prioritization (MOTCP). Thus, a pheromone updating strategy based on Epistatic-domain Test case Segment (ETS) was proposed. In the scheme, ETS existed in the test case sequence was selected as a pheromone updating scope, because ETS can determine the fitness value. Then, according to the fitness value increment between test cases and execution time of test cases in ETS, the pheromone on the trail was updated. In order to further improve the efficiency of ACO and reduce time consumption when ants visited test cases one by one, the end of ants' visiting was reset by estimating the length of ETS using optimized ACO. The experimental results show that compared with the original ACO and NSGA-Ⅱ, the optimized ACO has faster convergence and obtains better Pareto optimal solution sets in MOTCP.

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.

Aiming at the problem of low efficiency on generating Thread Scheduling Sequence (TSS) that cover synchronization statements in multi-thread concurrent program, a TSS Generation Based on All synchronization pairs coverage criteria (TGBA) method was proposed. First, according to the synchronization statements in concurrent program, the synchronization pair and All Synchronization Pairs Coverage Criteria (APSC) were defined. Second, a construction method of Synchronization Pair Thread Graph (SPTG) was given. On that basis, TSSs that satisfied APSC were generated. Finally, by using JPF (Java PathFinder) detection tool, TSS generation experiments were conducted on four Java Library concurrent programs, and the comparison analysis of generation efficiency was conducted with general sequence generation methods of Default Scheduling (DS), Preemptive Scheduling (PS) and Cross Scheduling (CS). The experimental results illustrate that TSSs generated by TGBA method can cover all synchronization pairs compared to the DS and CS method. Moreover, when satisfying APSC, TGBA method decreases at least 19889 states and 44352 transitions compared to the PS method, and the average generation efficiency increases by 1.95 times. So TGBA method can reduce cost of state space and improve the efficiency of TSS generation.