Accurate software effort prediction is one of the most challenging tasks in the software engineering domain.Due to the inherent uncertainty and risk of software development process,it is insufficient to predict the whole effort just at the early stage of the project.In contrast,it is important to predict the effort of each stage during the software development process.This enables the managers to reallocate resources according to the variation of the project development and ensures the project to be completed with the prescribed schedule and under the budget.Therefore,this paper presented a new method for software physical time stage-effort prediction based on both analogy method and grey mo-del.The proposed hybrid method obtains prediction results by combining the values predicted by both analogy and grey model.At the same time,this method can avoid the limitations of using either of them.The experimental results on real world software engineering dataset indicate that the prediction accuracy obtained by the proposed method is better than that obtained by analogy method,GM (1,1) model,GV,Kalman filter and linear regression,showing great potential.

In order to carry out mutation analysis of software testing,this paper proposed a method generating mutation test case based on model checking.Using formalized analysis of UPPAAL and test framework,the system under test is firstly described as a timed automata which conforms to its specifications.Then a set of mutation operation following timed automata’s basic structure and grammar are injected into the original model to simulate some implementation error which may occur.The mutated models and accessibility property are used as inputs to UPPAAL Yggdrasil to gene-rate test cases covering mutation area.Lastly,test cases are executed on the mutated model and a set of valid test cases is selected on the basis of test execution result (whether the test case can kill the mutation).Experimental results show that the generating test case of the proposed method is valid.Moreover,the mutation score of test case set is higher than that of the existing one based on state machine duplication and model’s transition coverage.

A new method called soft and hard integrated method was put forward,and it was applied to solve the pro-blem of technical support equipments’ software invalidation.With analysis,it was validated that developing software reliability and maintainability testing system based on software architecture is good for insuring the quality of technical support equipment’s software.Finally,the emphasis of this research was fixed on designing software architecture of software reliability and maintainability testing system.

Long method is a software design problem that requires refactoring because it is too long.In order to improve the detection rate of traditional machine learning approaches on long method,a cost-sensitive integrated classifier algorithm was proposed from the viewpoint of unbalanced sample data of code smell.Based on the traditional decision tree algorithm,the under-sampling startegy is used for resampling,then a plurality of balanced subsets are generated.These subsets are trained to generate a plurality of same base classifiers.Finally,the mistaken classification cost determined by the cognitive complexity is complemented to the integrated classifier.The cost makes the classifier inclined to the accuracy rate of the minority categories.Compared with the traditional machine learning algorithm,this method has improved the precision and recall for detection result of long methods.

Software cost estimation is one of the most important issues in the cycle of development,management decision,and in the quality of software project.Aiming at the common problems of software cost estimation in the software industry,such as inaccuracy of cost estimation and estimation difficulty,this paper presented a weighted analogy-based software cost estimation method.In this method,the similarity distance is defined as the Mahalanobis distance with correlation,and the weight is obtained by particle swarm optimization.The software cost is estimated by analogy method.The result shows that this method has high accuracy compared with non-computational based model methods such as non-weighted analogy and neural networks.At the same time,the actual cases show that this method is more accurate than expert estimation in software cost estimation based on demand analysis at the early stage of software development.

Emerging non-volatile memory(NVM) is the promising next-generation storage for its advanced characteristics.However,the low endurance of NVM cells makes them vulnerable to frequent fine-grained data updates.This paper proposed a novel in-page wear-leveling memory management (IWMM) for NVM-based storage.IWMM divides pages into basic memory units to support fine-grained updates.IWMM alternatively allocates the memory units of a page with directional order allocation algorithm to distribute fine-grained updates evenly on memory cells.Experimental results show that the wear counts of IWMM can achieve 52.6% reduction over that of NVMalloc,a wear-conscious allocator.Meanwhile,the preformance of IWMM is 27.6% betterthan glibc malloc when the ratio of memory deallocation is less than 50%.

All kinds of dependencies exist among entities.Especially in the process of software development,the depen-dencies between software entities has a big impact on the impact analysis of software and risk analysis.Dependency graph is the most commonly used dependency representation method,the definition of nodes and edges is different from attribute comptation,while the temporal properties of nodes and edges are seldom taken into account in existing depen-dency graph methods.This paper presented formal definition and analysis of temporal characteristics of temporal depen-dencies,and also analyzed the importance of four measures including node center,node importance,node dependency and edge importance.Finally,test dataset was extracted form MAVEN data,and experimental results showregulation of indictors varying with the time.

Based on systematic analysis on the characteristics of traditional models,a new model of software testing process called parallel YU model was put forward,and the corresponding activities were laid out clearly.Parallel YU model describes clearly the complicit relationship among software testing activities,hierarchy,parallelism,time sequence and iterations between software developing activities and testing activities.