1. Software Evolution and Reverse Engineering Founded in 2004 Main aims: Analyze and define approaches, methodologies, techniques, tools able to support software evolution and maintenance ● scientists: 2 faculties, 3 PhD students ● external collaborators Contact: Francesca Arcelli Fontana, arcelli@disco.unimib.it Website: http: //essere.disco.unimib.it/ Evoluzione dei Sistemi Software e Reverse Engineering

2. System evolution carries: –Software Architecture violation –Not up to date documentation –..... Expensive maintenance Software Evolution and Reverse Engineering

3. Main research areas: Design Pattern Detection (DPD) for Reverse Engineering Software Architecture Reconstruction (SAR) – Program Comprehension Legacy System Modernization – Migration of legacy systems to SOA – Model-driven reverse engineering – Function and Data Reengineering

4. DPD for reverse engineering DPD for forward engineering (Design Advisor) Quality design assessment Detection based on DP sub-components Architectural Patterns detection – Pattern detection for data reverse engineering Exploiting both static and dynamic analysis Design Patterns Detection (DPD)

5. We have experimented several tools for SAR and Program Comprehension on exixting systems of different complexity – Expecially for Java, C++ systems – Cobol, RPG We are developing a tool to support Program Comprehension providing: – Views, diagrams on the system – Metrics – High level architectural abstractions – Anti Patterns detection Software Architecture Reconstrution (SAR)

6. We experimented data mining techniques for DPD: –Supervised Classification (SVM, Naive Bayes,..) –Unsupervised Classifcation(Clustering, Weka SimpleKMeans) experimenting other tecniques for DPD and SAR Data Mining Techniques for Reverse Engineering

7. Systems Modernization How applications become legacy? Virtually every company has legacy applications of some form Often companies don’t have enough money for new projects because existing applications cost too much to operate and maintain Only some legacy applications will be reusable: which is the right level of granularity to support reuse? Solutions?? SOA are really the future state target for IT? Archtecture Driven Modernization – ADM (OMG standard)

8. Migration to SOA We are currently working on the analysis and experimentation of tools and methodologies for legacy systems migration towards Service Oriented Architectures. – Program Comprehension and SAR – DP Detection – Anti Pattern Detection (modularity, dependecy relations) – We are experimenting the COMPASS (COde Migration Planning and ASSesment) workbench framework of IBM- Haifa to understand applications and assist in isolating the business component in VB code towards SOA

9. DPD for Migration to SOA Exploiting the knowledge associated to some Design Patterns to provide a kind of guidance in designing SOA-based systems. Identifying the design patterns more relevant: –in the design of a SOA based system; –in the transformation process towards SOA when identified in an existing system or in a system under development, as they provide additional information on the underlying architecture and behaviour. –i.e. DP Mediator as a service or service composer, DP Façade,..

10. Software modernization exploiting architectural models Archtecture Driven Modernization – ADM (OMG standard-2004) Capture architectural aspects of existing applications Consolidate best practices leading to successful modernization Modernization from an analysis and design based perspective to prevent propagation of obsolete concepts and designs in modern language and platforms Aims to create interoperability standards for modernization tools –KDM (Knowledge Discovery Metamodel)-2007: Defines an initial meta-model that allows modernization tools to exchange application meta-data across applications, languages, platforms and environments –Modisco (MOdel DISCOvery): An Eclipse GMT (Generative Modeling Technology) component for model-driven reverse engineering

11. Software modernization and Marple Marple (Metrics and ARchitecture Reconstruction PLugin for Eclipse)

12. 12 AST of the Java language Module that extracts Basic Elements and Metrics Module that detects Design Patterns using Basic Elements contained in the model Module allowing to visualize Metrics and Design Patterns

13. ASTs of the supported languages AST to “MetaAST” transformation function “MetaAST” that allows the representation of “all” object oriented languages Module allowing to analyze the “MetaAST” in order to extract the information to be inserted in the model (e.g. Metrics Collector: calculates the metrics) Module allowing analyses on the model that enrich it with the detected information (e.g. Design Pattern Detection: detects Design Patterns using basic elements within the model) Module allowing to visualize and explore all the information within the model 13

14. Allows to implement new languages AST transformation functions (e.g. Python) Allows to implement new analyses on the “MetaAST” (e.g. DataFlow Analysis) Allows to implement new visualizations and explorations of the model Allows to implement new analyses on the model (e.g. correlation analysis) 14

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16. Collaborations with other DISCo-Laboratories ITIS (Batini, Viscusi), Function and Data Reverse Engineering MAD Data Mining for Reverse Engineering LTA, Dynamic Analysis for DPD and SAR SAL, Detection of architectural patterns, detection of interfaces/services, software evolution through architectural reflection Open-IT, Application of reverse engineering techniques to close software, software protection (decompiling techniques) TangoLab, stage on projects supporting activities in Ciad.

17. Collaborations Research Collaborations activated or to start: Università di Salerno Università del Molise Università di Roma Tre The Technion – Israel Institute of Technology, Unified catalog of Micro architectures University of Szeged, Ungheria,Benchmark for DPD INRIA (ATLAS Group, Nantes), Model driven reverse engineeering Microfocus (APM, Metrics) Replay (System Modernization) IBM Haifa (Migration to SOA-Compass)