As crowdsourcing has been applied to a variety of disciplines, e.g. marketing and operationalization, more and more scientists turn their sights to how the crowd innovate software engineering to produce high quality software. However, they mainly focus on the impacts brought by domain experts or experienced developers on developing and managing open source softwares, whereas how softwares are influenced by the ordinary people e.g. end users is seldom discussed and easily omitted. To fill up the research gaps, we investigate into commercial application improvement paradigm with assistance of user crowd. The approach focuses on end users by proposing a workflow loop to form a healthy cycle between them and applications. Especially, the approach propose a suggestion model to encourage users to participate into application runtime adaptation. So far, a prototype is developed to enable the crowd to raise and modify their advices, and our prior work has proven the effectiveness in which applications consider the users' advices to adapt themselves.

With the popularity of pads, large touch screens, notebooks and smart mobiles, it is a reasonable requirement that modelers use such devices to models. However, there are few modeling tools that take full advantage of the devices. This paper discusses a Web based UML modeling tool with touch screens, based on our full analysis of human-machine interaction modes for software modeling. The tool provides more input means, i.e. combining gesture input and traditional keyboard and mouse input, and supports cross-platforms modeling by using HTML 5.

as multi-tenant applications spring up in clouds, more and more people advocate using Service Level Agreement (SLA) in service delivery to fit tenants' non-functional needs e.g. response time and budget limit. However, most of the present application optimizations based on SLA focuses on virtual machine-based (VM-based) computing service, while other services such as storage and cache are often neglected. In this paper, we propose an SLA-driven application optimization for cache service to help to meet tenants' needs better and improve cost-effectiveness, which can be taken as complementary to the existing work. The proposed approach, built on top of Platform-as-a-Service (PaaS), pays attention to evicted data. It considers both tenant SLA-evaluated status and data performance when weighting the evicted data with re-cache likelihoods, and then adjusts their re-cache priorities. At the beginning of every cycle it predicts tenant status and evicted data performance for the coming cycle by Holt-Winters double exponential smoothing. Our simulation experiments demonstrate the optimization effectiveness in improving cache cost-effectiveness and satisfying tenant SLAs.

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