To effectively improve the automation level of legacy software system reconstruction based on the microservice architecture, according to the principle that there is a certain correlation between resource data operated by two classes with dependencies, a microservice identification method based on class dependencies under resource constraints was proposed. Firstly, the class dependency graph was built based on the class dependencies in the legacy software program, and the resource entity label for each class was set. Then, a dividing algorithm was designed for the class dependency graph based on the resource entity label, which was used to divide the original software system and obtain the candidate microservices. Finally, the candidate microservices with higher dependency degrees were combined to obtain the final microservice set. Experimental results based on four open source projects from GitHub demonstrate that, the proposed method achieves the microservice division accuracy of higher than 90%, which proves that it is reasonable and effective to identify microservices by considering both class dependencies and resource constraints.

Considering the characteristics of dyeing vat scheduling problem, such as complex constraints, large task scales, high efficiency request, an incremental dyeing vat scheduling model was established and the Slide Time Window Scheduling heuristic (STWS) algorithm was proposed to improve the applicability of the problem model and the algorithm in real scenario. In order to meet the optimization target of minimizing delay cost, washing cost and the switching cost of dyeing vat, the heuristic scheduling rules were applied to schedule the products according to the priority order. For each product scheduling, the dynamic combination batch algorithm and the batch split algorithm were used to divide batches, and then the batch optimal sorting algorithm was used to schedule the batches. The simulated scheduling results on actual production data provided by a dyeing enterprise show that the algorithm can complete the scheduling for monthly plan within 10 s. Compared with the manual scheduling, the proposed algorithm improves the scheduling efficiency and significantly optimizes three objectives. Additionally, experiments on incremental scheduling show obvious optimization of the algorithm on reducing the washing cost and the switching cost of dyeing vat. All the results indicate that the proposed algorithm has excellent scheduling ability.

To solve the problem of irrelevant and redundant features in harmfulness prediction of clone code, a combination model for harmfulness feature selection of code clone was proposed based on relevance and influence. Firstly, a preliminary sorting for the correlation of feature data was proceeded by the information gain ratio, then the features with high correlation was preserved and other irrelevant features were removed to reduce the search space of features. Next, the optimal feature subset was determined by using the wrapper sequential floating forward selection algorithm combined with six kinds of classifiers including Naive Bayes and so on. Finally, the different feature selection methods were analyzed, and feature data was analyzed, filtered and optimized by using the advantages of various methods in different selection critera. Experimental results show that the prediction accuracy is increased by15.2-34 percentage pointsafter feature selection; and compared with other feature selection methods, F1-measure of this method is increased by 1.1-10.1 percentage points, and AUC measure is increased by 0.7-22.1 percentage points. As a result, this method can greatly improve the accuracy of harmfulness prediction model.

As a widely used recommendation algorithm in the industry, collaborative filtering algorithm can predict the likely favorite items based on the user's historical behavior records. However, the traditional collaborative filtering algorithms do not take into account the drifting of user interests, and there are also some deficiencies when the recommendation's timeliness is considered. To solve these problems, the measure method of similarity was improved by combining with the characteristics of user interests change with time. At the same time, an enhanced time attenuation model was introduced to measure the predictive value. By combining these two ways together, the concept drifting problem of user interests was solved and the timeliness of the recommendation algorithm was also considered. In the simulation experiment, predictive scoring accuracy and Top N recommendation accuracy were compared among the proposed algorithm, UserCF, TCNCF, PTCF and TimesSVD++ algorithm in different data sets. The experimental results show that the improved algorithm can reduce the Root Mean Square Error (RMSE) of the prediction score, and it is better than all the compared algorithms on the accuracy of Top N recommendation.

Focusing on the less quantity and low efficiency problem of Type-3 clone code mapping method, a mapping method based on improved Vector Space Model (VSM) was proposed. Improved VSM was introduced into the clone code analysis to get an effective clone group mapping method for Type-1, Type-2 and Type-3. Firstly, clone group document was pretreated to get the code document with removing useless word, and the file name, function name and other features of clone group document were extracted at the same time. Secondly, word frequency vector space of clone group was extracted and built; the similarity of clone group was calculated by using cosine algorithm. Then mapping of clone group was constructed by clone group similarity and feature matching, and the result of cloning group mapping was obtained finally. Five pieces of open source software was tested and verified by experiments. The proposed method can guarantee the recall and the precision of not less than 96.1% and 97.1% at low time consumption. The experimental results show that the proposed method is feasible, which provides data support for the analysis of software evolution.

Focusing on the problem of imbalanced classification of harmful data and harmless data in the prediction of the harmful effects of clone code, a K-Balance algorithm based on Random Under-Sampling (RUS) was proposed, which could adjust the classification imbalance automatically. Firstly, a sample data set was constructed by extracting static features and evolution features of clone code. Then, a new data set of imbalanced classification with different proportion was selected. Next, the harmful prediction was carried out to the new selected data set. Finally, the most suitable percentage value of classification imbalance was chosen automatically by observing the different performance of the classifier. The performance of the harmfulness prediction model of clone code was evaluated with seven different types of open-source software systems containing 170 versions written in C language. Compared with the other classification imbalance solution methods, the experimental results show that the proposed method is increased by 2.62 percentage points to 36.7 percentage points in the classification prediction effects (Area Under ROC(Receive Operating Characteristic) Curve (AUC)) of harmful and harmless clones. The proposed method can improve the classification imbalance prediction effectively.

During the process of software development, activities of programmers including copy and paste result in a lot of code clones. However, the inconsistent code changes are always harmful to the programs. To solve this problem, and find harmful code clones in programs effectively, a method was proposed to predict harmful code clones by using Bayesian network. First, referring to correlation research on software defects prediction and clone evolution, two software metrics including static metrics and evolution metrics were proposed to characterize the features of clone codes. Then the prediction model was constructed by using core algorithm of Bayesian network. Finally, the probability of harmful code clones occurrence was predicted. Five different types of open-source software system containing 99 versions written in C languages were tested to evaluate the prediction model. The experimental results show that the proposed method can predict harmfulness for clones with better applicability and higher accuracy, and further reduce the threat of harmful code clones while improving software quality.

Mobile sink can efficiently collect data and extend the network lifetime. However, the existing researches about data collection based on mobile sink mainly focus on path-constrained mobile sink. Hence, a path-controlled traversal model for mobile sink data collection was constructed, and a data collection algorithm for mobile sink based on optimal-path traveling was proposed. The algorithm discretized the continuous path problem by local Voronoi grid, used the amount of data collected and system energy consumption as performance metric, combined taboo search algorithm to achieve the maximum amount of data collected and the minimum of network energy consumption traversing. Theoretically and experimentally, it is concluded that the proposed algorithm is able to solve the optimal-path traveling of data collection problem using path-controlled mobile sink.