Peldszus, SvenSvenPeldszusKulcsár, GézaGézaKulcsárLochau, MalteMalteLochauSchulze, SandroSandroSchulze2020-04-302020-04-302016ASE 2016 - Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering : 578-589 (2016)http://hdl.handle.net/11420/5993Design aws in object-oriented programs may seriously corrupt code quality thus increasing the risk for introducing subtle errors during software maintenance and evolution. Most recent approaches identify design aws in an ad-hoc manner, either focusing on software metrics, locally restricted code smells, or on coarse-grained architectural antipatterns. In this paper, we utilize an abstract program model capturing high-level object-oriented code entities, further augmented with qualitative and quantitative designrelated information such as coupling/cohesion. Based on this model, we propose a comprehensive methodology for specifying object-oriented design aws by means of compound rules integrating code metrics, code smells and antipatterns in a modular way. This approach allows for ef-ficient, automated design-aw detection through incremental multi-pattern matching, by facilitating systematic information reuse among multiple detection rules as well as between subsequent detection runs on continuously evolving programs. Our tool implementation comprises well-known anti-patterns for Java programs. The results of our experimental evaluation show high detection precision, scalability to real-size programs, as well as a remarkable gain in effi-ciency due to information reuse.enContinuous software evolutionDesign-aw detectionObjectoriented software architectureInformatikConference Paper10.1145/2970276.2970338Other