Applications built on reusable component frameworks are subject to two independent, and potentially conflicting, evolution processes. The application evolves in response to the specific requirements and desired qualities of the application's stakeholders. On the other hand, the evolution of the component framework is driven by the need to improve the framework functionality and quality while maintaining its generality. Thus, changes to the component framework frequently change its API on which its client applications rely and, as a result, these applications break. To date, there has been some work aimed at supporting the migration of client applications to newer versions of their underlying frameworks, but it usually requires that the framework developers do additional work for that purpose or that the application developers use the same tools as the framework developers. In this paper, we discuss our approach to tackle the API-evolution problem in the context of reuse-based software development, which automatically recognizes the API changes of the reused framework and proposes plausible replacements to the "obsolete" API based on working examples of the framework code base. This approach has been implemented in the Diff-CatchUp tool. We report on two case studies that we have conducted to evaluate the effectiveness of our approach with its Diff-CatchUp prototype.

What is the relationship between avatars and the people they represent in terms of appearance and behavior? In this paper, we hypothesize that people (balancing motives of self-verification and self-enhancement) customize the image of their avatars to bear similarity to their real selves, but with moderate enhancements. We also hypothesize that virtual-world behavior (due to deindividuation in computer-mediated communication environments) is less restrained by normal inhibitions than real-world behavior. Lastly, we hypothesize that people with more attractive avatars than their real selves will be somewhat more confident and extraverted in virtual worlds than they are in the real world. We examine these issues using data collected from Second Life residents using an in-world intercept method that involved recruiting respondents’ avatars from a representative sample of locations. Our quantitative data indicate that, on average, people report making their avatars similar to themselves, but somewhat more attractive. And, compared to real-world behavior, respondents indicate that their virtual-world behavior is more outgoing and risk-taking and less thoughtful/more superficial. Finally, people with avatars more attractive than their real selves state that they are more outgoing, extraverted, risk-taking, and loud than their real selves (particularly if they reported being relatively low on these traits in the real world). Qualitative data from open-ended questions corroborate our hypotheses.