Often underlying discussions with those who are not professional computer scientists is the idea that computer scientists are programmers and programmers make tools. Yet many computer science professionals view themselves primarily as scientists, with programs being used to create and express a deeper understanding of computation. This Birds of a Feather session discusses how participants can effectively bridge the gap between toolmaker and scientist views, either through modifying their own view or changing the viewpoint of others. Topics include: participants' personal balance between how they view themselves as scientists and toolmakers; how to encourage students to see themselves as scientists and toolmakers in balance; how to effectively conduct group work with a variety of viewpoints present; how student motivation interacts with how they see themselves as computer scientists; and other viewpoints that exist about what it means to be a computer scientist. Computer scientists and non-computer scientists welcome. Laptop not recommended.

Pedagogues such as the Personal Software Process (PSP) shift metrics definition, collection, and analysis from the organizational level to the individual level. While case study research indicates that the PSP can provide software engineering students with empirical support for improving estimation and quality assurance, there is little evidence that many students continue to use the PSP when no longer required to do so. Our research suggests that this "PSP adoption problem" may be due to two problems: the high overhead of PSP-style metrics collection and analysis, and the requirement that PSP users "context switch" between product development and process recording. This paper overviews our initial PSP experiences, our first attempt to solve the PSP adoption problem with the LEAP system, and our current approach called Hackystat. This approach fully automates both data collection and analysis, which eliminates overhead and context switching. However, Hackystat changes the kind of metrics data that is collected, and introduces new privacy-related adoption issues of its own.