All papers as a large zip-file.
Abstract. Being a modeling language rather than a method, UML does
not inherently define a design process. Consequently there is no common
basis for building design process support for UML tools. They often offer
extension interfaces, which allow external applications to access and handle
the UML models constructed using that particular tool. Although they support
extensibility and customizability, such tool-dependent solutions are
typically suitable only for performing single operations or tasks. In this
paper, we propose a tool-independent software platform, which allows the
user to build and combine various kinds of UML processing facilities and use
them from integrated CASE-tools. The proposed solution conforms to the UML
standard metamodel and hides the individual conventions and complexities of
the CASE-tool APIs. We discuss our solution, its architecture and API, and
also illustrate their usage by giving an example.
Abstract. The software engineering communities frequently propose
new software engineering technologies, such as new development techniques,
programming languages and tools, without rigorous scientific evaluation. One
way to evaluate software engineering technologies is through controlled
experiments where the effects of the technology can be isolated from
confounding factors, i.e., establishing cause-effect relationships. For
practical and financial reasons, however, such experiments are often quite
unrealistic, typically involving students in a class-room environment
solving small pen-and- paper tasks. A common criticism of the results of the
experiments is their lack of external validity, i.e., that the results are
not valid outside the experimental conditions. To increase the external
validity of the experimental results, the experiments need to be more
realistic. The realism can be increased using professional developers as
subjects who conduct larger experimental tasks in their normal work
environment. However, the logistics involved in running such experiments are
tremendous. More specifically, the experimental materials (e.g.,
questionnaires, task descriptions, code and tools) must be distributed to
each programmer, the progress of the experiment needs to be controlled and
monitored, and the results of the experiment need to be collected and
analyzed. To support this logistics for large-scale, controlled experiments,
we have developed a web-based experiment support environment called SESE.
This paper describes SESE, its development and the experiences from using it
to conduct a large controlled experiment in industry.