Effect of cenospheres addition on microstructure and properties of AZ91D alloy

The cenospheres/AZ91D composites were fabricated by melt stir method. The phases, microstructure and tensile fracture morphology of the composites were analyzed using XRD, Olympus metallurgical microscopy and SEM methods. The thermal expansion coefficient(CTE) and tensile properties were measured. The results showed that the cenospheres distribute uniformly in the Mg alloy matrix and refine the matrix microstructure. Mg2 Si and MgO were found in addition to α-Mg and β-Mg17Al12 phases using XRD. The CTE of the composites reduced after the cenospheres are added. The yield strength of the composites increases significantly with an increase in the mass fraction and a decrease in the size of the cenospheres. The tensile strength of the composites achieves maximum when the mass fraction of cenospheres is 9wt.% and the size of cenospheres is 80 μm. The fracture mechanism of the composites is cleavage fracture.

Using information flow analysis to detect implicit information leaks for web service composition

Information leak,which can undermine the compliance of web-service-composition business processes for some policies,is one of the major concerns in web service composition.We present an automated and effective approach for the detection of implicit information leaks in business process execution language(BPEL)based on information flow analysis.We introduce an adequate meta-model for BPEL representation based on a Petri net for transformation and analysis.Building on the concept of Petri net place-based noninterference,the core contribution of this paper is the application of a Petri net reachability graph to estimate Petri net interference and thereby to detect implicit information leaks in web service composition.In addition,a case study illustrates the application of the approach on a concrete workflow in BPEL notation.

保持隐私信息流安全性的服务组合演化方法（英文）

隐私信息流安全的服务组合部署后,用户隐私需求与成员服务的信誉度等级均可能发生变化,保持隐私信息流安全性是非常重要的演化需求。提出一种保持隐私信息流安全性的服务组合演化方法。分析基于Petri网的隐私数据项依赖关系,计算各成员服务的隐私数据使用集及安全范围,最后提出保持隐私信息流安全性的演化操作集。通过操作实施演化,避免演化后隐私信息流安全性的完整验证过程,提高演化效率。通过实例分析说明该方法的有效性,并对其性能进行实验分析。结果表明,与完整安全性验证过程相比,该方法有较高演化效率,能大大降低演化代价。

Discovering API Directives from API Specifications with Text Classification

Application programming interface(API)libraries are extensively used by developers.To correctly program with APIs and avoid bugs,developers shall pay attention to API directives,which illustrate the constraints of APIs.Unfortunately,API directives usually have diverse morphologies,making it time-consuming and error-prone for developers to discover all the relevant API directives.In this paper,we propose an approach leveraging text classification to discover API directives from API specifications.Specifically,given a set of training sentences in API specifications,our approach first characterizes each sentence by three groups of features.Then,to deal with the unequal distribution between API directives and non-directives,our approach employs an under-sampling strategy to split the imbalanced training set into several subsets and trains several classifiers.Given a new sentence in an API specification,our approach synthesizes the trained classifiers to predict whether it is an API directive.We have evaluated our approach over a publicly available annotated API directive corpus.The experimental results reveal that our approach achieves an F-measure value of up to 82.08%.In addition,our approach statistically outperforms the state-of-the-art approach by up to 29.67%in terms of F-measure.

Automatic traceability link recovery via active learning

Traceability link recovery(TLR)is an important and costly software task that requires humans establish relationships between source and target artifact sets within the same project.Previous research has proposed to establish traceability links by machine learning approaches.However,current machine learning approaches cannot be well applied to projects without traceability information(links),because training an effective predictive model requires humans label too many traceability links.To save manpower,we propose a new TLR approach based on active learning(AL),which is called the AL-based approach.We evaluate the AL-based approach on seven commonly used traceability datasets and compare it with an information retrieval based approach and a state-ofthe-art machine learning approach.The results indicate that the AL-based approach outperforms the other two approaches in terms of F-score.