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When large software systems evolve, the quality of source code is essential for successful maintenance. Controlling code quality continuously requires adequate tool support. Current quality analysis tools operate in batch-mode and run up to several hours for large systems, which hampers the integration of quality control into daily development. In this paper, we present the incremental quality analysis tool Teamscale, providing feedback to developers within seconds after a commit and thus enabling real-time software quality control. We evaluated the tool within a development team of a German insurance company. A video demonstrates our tool: http://www.youtube.com/watch?v=nnuqplu75Cg.

Abstract-Program comprehension is a complex task, especially for large software systems. Understanding an unknown system requires a significant amount of time. To speed up the learning process, developers focus on understanding central classes first. If other developers are available, they usually suggest which classes should be read at the beginning. In the absence of this knowledge, an independent algorithm is needed to measure the centrality of a class and give a recommendation for the developer. This paper presents an approach to retrieve central classes by using network analysis on the dependency graph of the system. An empirical study on open source projects evaluates the results of our algorithm based on a survey among the system's developers.

Clones bear the risk of incomplete bugfixes when the bug is fixed in one code fragment but at least one of its copies is not changed and remains faulty. Although we find incompletely fixed clones in almost every system, it is usually time consuming to manually locate these clones inside the results of an ordinary clone detection tool. In this paper, we describe in how far certain features of clones can be used to automatically identify incomplete bugfixes. The results are relevant for developers to locate incomplete bugfixes-that is, defects still existing in the system-and for us as clone researchers to quickly find examples that motivate the use of clone management.

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