Enriching Reverse Engineering with Annotations Export

@incollection{citeulike:3336214, abstract = {Much of the knowledge about software systems is implicit, and therefore difficult to recover by purely automated techniques. Architectural layers and the externally visible features of software systems are two examples of information that can be difficult to detect from source code alone, and that would benefit from additional human knowledge. Typical approaches to reasoning about data involve encoding an explicit meta-model and expressing analyses at that level. Due to its informal nature, however, human knowledge can be difficult to characterize up-front and integrate into such a meta-model. We propose a generic, annotation-based approach to capture such knowledge during the reverse engineering process. Annotation types can be iteratively defined, refined and transformed, without requiring a fixed meta-model to be defined in advance. We show how our approach supports reverse engineering by implementing it in a tool called Metanool and by applying it to (i) analyzing architectural layering, (ii) tracking reengineering tasks, (iii) detecting design flaws, and (iv) analyzing features.}, author = {Br\"{u}hlmann, Andrea and G\^{i}rba, Tudor and Greevy, Orla and Nierstrasz, Oscar}, citeulike-article-id = {3336214}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-3-540-87875-9_46}, citeulike-linkout-1 = {http://dx.doi.org/10.1007/978-3-540-87875-9_46}, citeulike-linkout-2 = {http://dx.doi.org/http://dx.doi.org/10.1007/978-3-540-87875-9_46}, doi = {10.1007/978-3-540-87875-9_46}, journal = {Model Driven Engineering Languages and Systems}, keywords = {annotations, engineering, reverse}, pages = {660--674}, posted-at = {2008-11-19 07:50:35}, priority = {2}, title = {Enriching Reverse Engineering with Annotations}, url = {http://dx.doi.org/10.1007/978-3-540-87875-9_46}, year = {2008} }

TY - CHAP ID - citeulike:3336214 L3 - citeulike-article-id:3336214 N2 - Much of the knowledge about software systems is implicit, and therefore difficult to recover by purely automated techniques. Architectural layers and the externally visible features of software systems are two examples of information that can be difficult to detect from source code alone, and that would benefit from additional human knowledge. Typical approaches to reasoning about data involve encoding an explicit meta-model and expressing analyses at that level. Due to its informal nature, however, human knowledge can be difficult to characterize up-front and integrate into such a meta-model. We propose a generic, annotation-based approach to capture such knowledge during the reverse engineering process. Annotation types can be iteratively defined, refined and transformed, without requiring a fixed meta-model to be defined in advance. We show how our approach supports reverse engineering by implementing it in a tool called Metanool and by applying it to (i) analyzing architectural layering, (ii) tracking reengineering tasks, (iii) detecting design flaws, and (iv) analyzing features. JF - Model Driven Engineering Languages and Systems EP - 674 TI - Enriching Reverse Engineering with Annotations SP - 660 KW - annotations KW - engineering KW - reverse AU - Brühlmann, Andrea AU - Gîrba, Tudor AU - Greevy, Orla AU - Nierstrasz, Oscar PY - 2008/// UR - http://dx.doi.org/10.1007/978-3-540-87875-9_46 ER -