基于可变性模型的可复用与可定制SaaS软件开发方法

云计算环境下，软件通过互联网向租户提供服务，这种基于互联网的软件交付模式称为SaaS（软件即服务）.与传统软件交付模式相比，SaaS软件通常运行于软件供应商的服务器端，同时为多个租户提供服务.由于需要支持不同租户的个性化需求，SaaS软件应具备足够的灵活性，以应对快速变化的租户需求；而且针对某一个租户的变更，不应影响其他租户.通过扩展课题组前期开发的基于可变性管理的适应性服务组装方法及其支持平台，提出了一种云计算环境下可复用、可定制的SaaS软件开发方法，开发了相应的支持平台，包括支持SaaS模式的服务组装引擎和远程定制工具.该方法针对不同租户的共性需求，提供一个抽象服务组装模型，支持平台在运行阶段解释执行抽象服务组装模型，根据租户的个性化需求派生不同的流程实例，这些运行时流程实例多态共存、互不影响.采用一个特定领域的SaaS软件实例来验证该方法的可行性，评估了支持平台的性能.实验结果表明，该方法及其支持平台可以支持多实例多租户的交付模式.

Reusable and Customizable SaaS Software Development Approach Based on Variation Model

In the context of cloud computing, software is delivered as a service to the customer through the Internet, and such a software delivery mode is called SaaS (software as a service). Unlike the traditional software delivery mode, SaaS software is usually running on the server side, which provides services to multiple tenants at the same time. As a result, SaaS software should be designed to meet the individual needs of different tenants, they should be flexible enough to cater for the rapidly changing tenant''s requirements, and the response to a talent''s change should not affect other tenants. Using the adaptive service composition method based on variability management and its supporting platform, a reusable and customizable SaaS software development method for the context of cloud computing is proposed, and a supporting platform is developed to facilitate the adoption of the proposed method. The platform includes a SaaS mode supporting service composition engine and a remote customization tool. The proposed method first creates an abstract service composition model to meet the common requirements of different tenants, and then the supporting platform is used to interpret the model and derive multiple different process instances at runtime, which are concurrently executed and isolated. A case study is conducted to validate the feasibility of the proposed method and evaluate the performance of the supporting platform using a domain specific SaaS software. Experimental results show that the proposed method and platform present a viable alternative for multi-tenant and multi-instance delivery mode for SaaS software.