The current consistency detection methods of software design and implementation require a large number of template sets and are difficult to generalize. In order to solve these problems, a consistency analysis method of software design and implementation based on control flow was proposed. Firstly, the pseudocode of the design document and the source code of the program were converted into the intermediate representations with the same features, and the design feature and the implementation feature were respectively extracted from the intermediate representations. The features include the function call relationship which can reflect the system structure and the control flow information which can reflect the internal structure of the function. Then, the design feature model and the implementation feature model were respectively established according to the design feature and the implementation feature. Finally, the similarity of the feature model was measured by calculating the feature similarity, so as to obtain the consistency detection result. Experimental results show that this method can correctly detect the inconsistent function call relationship when the function call relationship realized by the software is inconsistent with the design, and can correctly detect the inconsistency of the internal structure of the function when the function call relationship realized by the software is consistent with the design, with the accuracy reached 92.85%. This method can effectively obtain the consistency detection results without any template set, and has superior generality.

With the rapid development of Internet, huge amount of textual information is accessible on the Internet. The task of reliable person-person relation extraction from Web page has become an import research topic in the field of information extraction. To address this problem, this work implemented a blocked person relation recognition system and adopted abundant of features, i.e., bag-of-word, relevant frequency, Dependency Tree (DT), Named Entity Recognition (NER) features, etc. A series of experiments were conducted to select out optimal feature set and classification algorithm for each relation type to improve the performance. This system was performed on two tasks in China Conference of Machine Learn Competition (CCML Competition) of 2015, to recognize person relation from single or a set of news titles in Chinese (Task1 and Task2, respectively). For these two tasks, this system achieved the MacroF1 score of 75.68% and 76.58%, respectively and ranked the 1st on both tasks.