To solve the problem that the influencing factors of crowdsourced testing are complex and diverse, and the test quality is difficult to assess, a method for analyzing the quality influencing factors based on Spearman correlation coefficient was proposed. Firstly, the potential quality influencing factors were obtained through the analysis of test platforms, tasks, and testers. Secondly, Spearman correlation coefficient was used to calculate the correlation degrees between potential factors and test quality and to screen out key factors. Finally, the multiple stepwise regression was used to establish a linear evaluation relationship between key factors and test quality. The experimental results show that compared with the traditional expert artificial evaluation method, the proposed method can maintain smaller fluctuations in evaluation error when facing a large number of test tasks. Therefore, the method can accurately screen out the key influencing factors of mobile application crowdsourced test quality.

The existing watermarking algorithm based on image moments has the disadvantages of small capacity, large complexity and its robustness should be improved in further study. A new zero-watermarking against geometric attacks was proposed. Using the image normalization and the features of Tchebichef moments, the rotation normalized Tchebichef moments of original image was calculated in the unit circle, and the upper-left corner of Tchebichef moments was scanned into numerical matrix. Afterwards, binary secret key was generated according to numerical matrix and watermark image, and saved to zero-watermarking information database. In detection, the same process was executed to draw out numerical matrix from the unauthenticated image, and watermark image was extracted by using the secret key and numerical matrix. The experimental results show that this algorithm is robust against rotation attacks of random angles, scaling attacks and common signal processing operations, even combined attacks.