Application update at run-time remains a challenging issue in software engineering. There are many techniques with different evaluation metrics, resulting in different behaviours in the application being updated. In this paper, we provide an extensive review of research work on dynamic software updating. A framework for the evaluation of dynamic updating features is developed, and the articles are categorized and discussed based on the provided framework. Areas of online software maintenance requiring further research are also identified and highlighted. This information is deemed to not only assist practitioners in selecting appropriate dynamic updating techniques for their systems, but also to facilitate the ongoing and continuous research in the field of dynamic software updating.

Networked computers are expanding more and more around the world, and digital social networks becoming of great importance for many people's work and leisure. Emails, Weblogs and Instant Messengers are popular instances of social networks. In this paper, the main target is having an advertisement according to user favorites and interests by mining his/her interactions in digital social networks. Briefly, in our method social network users are categorized based on the topic exchanges by them in the network, these topics discovered by mining of flowing data in that environment, considering that these topics shows the user willing, finally relevant advertisements will be represented to them. In fact, by finding people that have more chance to accept the desired advertisement, system will have more success over traditional method at lowered cost.

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