Concerning the problem that microservice composition platform is short of analysis and verification of Quality of Service (QoS) indicators, a formal verification method based on model checking was proposed to analyze and evaluate the factors that affect the microservice composite platform performance. First, the service resource configuration process of microservice composition was divided into three phases:service request, configuration and service execution. These three phases were implemented by three modules:service request queue, resource configurator of service requests and virtual machine for providing service resources. After that, the implementation processes of the three modules were modeled as Labelled Markov Reward Models (LMRM), and the global model of microservice composition process was obtained by using a synchronization concept similar to the process algebra. Then, the logic formula of continuous random reward logic was used to describe the expected QoS indicators. Last, the formal model and logic formula were regarded as the input of model detection tool PRISM to obtain the verification results. Experimental results prove that LMRM can realize the QoS verification and analysis as well as the construction of microservice composite platform.

It is a precondition of achieving code reuse to search open source code accurately. The current methods based on keyword search only concern matching function signatures. Considering the source code comments on the semantic description of the method's function, a method based on keyword search was proposed, which took into account code comments. The features of code, such as function signatures and different types of comments, were identified from the generated abstract syntax tree of source code; the code features and query statements were transformed into vectors respectively, and then based on the cosine similarity between the vectors, the scoring mechanism of multi-feature weight assignment to the results was created. According to the scores, an ordered list of relevant functions was obtained that reflects the associations between code features in the functions and a query. The experimental results demonstrate that the accuracy of search results can be improved by using multiple code features with different weights.

To overcome the shortcomings of Cross-Media Relevance Model (CMRM) whose efficiency and effectiveness are low, an improved CMRM was proposed. Based on the improved smoothing method for textual words, the improved CMRM simplified the feature representation and similarity computation which made the measure of relationship between image and image more accurate. The experimental results on the Corel5k dataset show that the proposed approach can significantly improve annotation efficiency. The performance of the improved CMRM is almost three times as good (in terms of mean F1-measure) as original CMRM, also, better than some previously published high quality algorithms such as famous Multiple Bernoulli Relevance Model (MBRM) and Supervised Multiclass Labeling (SML).