Qu meeting phd thesis kessentini

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Qu meeting phd thesis kessentini

  1. 1. Transformation by Example Marouane Kessentini International Center of Excellence in Software Engineering 28 February, 2011
  2. 2. Translation Metaphor <ul><li>Different languages </li></ul><ul><ul><li>English to French ? </li></ul></ul><ul><ul><li>Vulcan to Smurf ? </li></ul></ul><ul><li>Same language </li></ul><ul><ul><li>Detection Translation </li></ul></ul><ul><ul><li>Long sentence Split in two sentences </li></ul></ul><ul><li>Translation errors </li></ul>
  3. 3. Transformation Rules conforms Target Model Target Meta-model Source Meta-model Source Model conforms
  4. 4. Context Source model Target model Endogenous transformations Exogenous transformations SMM≠TMM SMM=TMM Model transformation testing
  5. 5. Statement <ul><li>Problem: transformation and testing activities require specific and contextual knowledge </li></ul><ul><ul><li>Not always fully available </li></ul></ul><ul><ul><li>Difficult to express, structure, implement </li></ul></ul><ul><li>Solution : the use of examples to compensate the lack of knowledge </li></ul>
  6. 6. Challenges… <ul><li>Transforming models without specifying rules </li></ul><ul><li>Detecting design defects without specifying them </li></ul><ul><li>Testing transformations without </li></ul><ul><ul><li>Defining expected target models </li></ul></ul><ul><ul><li>Specifying constrains </li></ul></ul>
  7. 7. Outline <ul><li>Defining Exogenous Transformations </li></ul><ul><li>Detecting Elements to Transform for Endogenous Transformations </li></ul><ul><li>Testing Transformations </li></ul><ul><li>Conclusion and Future work </li></ul>
  8. 8. Existing Work <ul><li>Several transformation approaches ( Czarnecki and Helsen ’05 ) </li></ul><ul><ul><li>Graph transformation, Direct manipulation, Structure-driven, Relational , Hybrid… </li></ul></ul><ul><li>Available work based on rules ( Egyed ’02 ) </li></ul><ul><ul><li>VIATRA ( Varro et al. ’04 ) </li></ul></ul><ul><ul><li>AGG ( Taenzer et al. ’03 ) </li></ul></ul><ul><ul><li>ATL ( Jouault et al. ’05 ) </li></ul></ul><ul><ul><li>… </li></ul></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  9. 9. Problem <ul><li>Difficult to define/express transformation rules </li></ul><ul><ul><li>Usually 1-to-1 mappings </li></ul></ul><ul><ul><li>Dynamic model mappings </li></ul></ul><ul><ul><li>State explosion problem for behavioral models </li></ul></ul><ul><li>Difficult to derive consensual rules </li></ul><ul><ul><li>Diverge expert’s opinions </li></ul></ul><ul><ul><li>Need to understand the source and target formalisms </li></ul></ul><ul><ul><li>Easier to describe examples than consistence and complete rule sets </li></ul></ul><ul><li>Idea = Model Transformation by Example </li></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  10. 10. Transformation by Example <ul><li>Limitations </li></ul><ul><ul><li>Formalism and languages dependent </li></ul></ul><ul><ul><li>Mostly 1-to-1 mappings </li></ul></ul><ul><ul><li>Difficult to fully automate </li></ul></ul><ul><ul><li>Strong hypotheses (e.g., representative samples) </li></ul></ul><ul><ul><li>Applied only to static models </li></ul></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion By example approaches Exogenous transformation Endogenous transformation Traceability Rules generation Varrò et al. 06 X X X Wimmer et al. 07 X X X Sun et al. 09 X X Dolques et al. 10 X X X
  11. 11. Overview Transformation (Heuristic search) Base of examples Source Model Target Model Defining Trans. Detecting Elements Testing Trans. Conclusion
  12. 12. Illustration <ul><li>Transformation example </li></ul>[SM, TM, MB] TM = relational schema Table(Position). column(Title, Position,_). … SM = class diagram Class(Position). Attribute(Title, Position,_). … Association(0,1,_,n_, Assigned, Position, Employee ). … Generalization (Employee, Operative). Block B32 Class(Position) : Table(Position). Attribute(Title, Position,_) : Column(idPosition, position, pk), Column(title, Position,_). … Class(Employee) : Table(Employee). … Association(0,1,_,n,_, Position, Employee) : Column(idPosition, employee, fk). Defining Trans. Detecting Elements Testing Trans. Conclusion B32
  13. 13. Proposal <ul><li>Transformation problem = search in an n-dimensional space </li></ul><ul><li>Construct = dimension </li></ul><ul><li>Solution = {<construct i ,T j (construct i )>} </li></ul><ul><li>1 construct = m possibilities of transformation </li></ul><ul><li>Complexity = m n possible combinations </li></ul><ul><ul><li>Exp : 60 12 possibilities ! </li></ul></ul><ul><li>Heuristic search </li></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  14. 14. Search-based Model Transformation <ul><li>Used heuristic algorithm </li></ul><ul><ul><li>Particle Swarm Optimization ( Kennedy et al. ’95 ) </li></ul></ul><ul><ul><li>Simulated Annealing ( Kirkpatrick et al. ’83 ) </li></ul></ul>Simulated Annealing Algorithm Initial solution Final solution PSO Algorithm New solution Defining Trans. Detecting Elements Testing Trans. Conclusion
  15. 15. Solution Representation (PSO-SA) <ul><li>Transformation encoding </li></ul>Constructs Block number solution 1 6 3 4 2 5 10 11 8 9 7 12 1 2 5 4 3 7 6 10 9 8 11 12 Transformations blocks Defining Trans. Detecting Elements Testing Trans. Conclusion 20 18 1 17 12 7 15 2 9 3 5 29
  16. 16. Change Operator (SA) Solution i 1 2 5 4 3 7 6 New solution generation 1 2 5 4 3 7 6 Example 1 Example 2 Example 3 Example n Base of examples Solution i+1 Defining Trans. Detecting Elements Testing Trans. Conclusion
  17. 17. Fitness Function (PSO-SA) <ul><li>Good model transformation </li></ul><ul><ul><li>Good transformation for individual constructs </li></ul></ul><ul><ul><li>Consistency between construct transformations </li></ul></ul><ul><ul><li>Temporal constraints preservation (behavioral models) </li></ul></ul><ul><li>Fitness function (to maximize) </li></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  18. 18. Fitness Function Begin b1 Class(Client) : Table(client). Attribute(N_Client, Client, unique) : Column(n_Client, client, pk). … Class(Reservation) : Table(Reservation). Attribute(N_reservation, Reservation, unique) : Column(n_reservation, Reservation, pk). … Association (1,1,0,n,_, Client, Reservation) : Column(n_Client, Reservation, fk). End b1 Association (0,1,_,n,_, Position, Employee) e 3 = 1 p 3 = 5/7 = 0.71 d 3 = 2/2 = 1 Defining Trans. Detecting Elements Testing Trans. Conclusion 20 18 1 17 12 7 15 2 9 3 5
  19. 19. Evaluation <ul><li>CLD-to-RS transformation </li></ul><ul><ul><li>12 examples (industrial projects) </li></ul></ul><ul><li>SD-to-CPN transformation </li></ul><ul><ul><li>10 examples </li></ul></ul><ul><li>n-fold cross-validation </li></ul><ul><ul><li>Transform each example using the n-1 other examples </li></ul></ul><ul><ul><li>Average precision </li></ul></ul><ul><li>Model transformation precision (AC, MC) </li></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  20. 20. CLD-to-RS Results <ul><li>Precision of the 12 generated transformations </li></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion Source Model Number of constructs Fitness AC MC SM 1 72 0.696 0.618 0.882 SM 2 83 0.714 0.682 0.928 SM 3 49 0.762 0.721 0.943 SM 4 53 0.796 0.719 0.931 SM 5 38 0.773 0.789 0.952 SM 6 47 0.746 0.652 0.918 SM 7 78 0.715 0.772 0.957 SM 8 34 0.896 0.822 0.981 SM 9 92 0.61 0.634 0.87 SM 10 28 0.892 0.908 0.969 SM 11 59 0.773 0.717 0.915 SM 12 63 0.805 0.762 0.938 Average 58 0.764 0.733 0.932
  21. 21. Example-size variation (CLD-to-RS) Defining Trans. Detecting Elements Testing Trans. Conclusion
  22. 22. SD-to-CPN Results Defining Trans. Detecting Elements Testing Trans. Conclusion
  23. 23. CPN Size Comparison Defining Trans. Detecting Elements Testing Trans. Conclusion Size(WebSPN) Size(dMOTOE) Variation 22 13 41% 36 22 39% 39 24 38% 43 31 28% 51 36 30% 50 39 22% 56 39 30% 53 44 16% 58 52 10% 76 54 29% Average Reduction :% 28.3%
  24. 24. Outline <ul><li>Defining Exogenous Transformations </li></ul><ul><li>Detecting Elements to Transform for Endogenous Transformations </li></ul><ul><li>Testing Transformations </li></ul><ul><li>Conclusion and Future work </li></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  25. 25. Endogenous Transformation <ul><li>Endogenous transformation to improve software quality </li></ul><ul><ul><li>Detecting design defects : situations that adversely affect the development of a software </li></ul></ul><ul><ul><li>Applying refactoring operations (transformation) </li></ul></ul><ul><li>The Blob example </li></ul><ul><ul><li>Detect “God” classes (number of : methods, relations, …) </li></ul></ul><ul><ul><li>Transformation (move methods, …) </li></ul></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  26. 26. Existing Work <ul><li>Usual approach ( Moha et al. ’10, Marinescu et al. ’04, ...) </li></ul><ul><ul><li>Definition  symptoms  detection algorithm </li></ul></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  27. 27. Problem <ul><li>Detection issues </li></ul><ul><ul><li>Need an exhaustive design defects list </li></ul></ul><ul><ul><li>No consensual definition of symptoms </li></ul></ul><ul><ul><li>Difficulty to automate symptom’s evaluation </li></ul></ul><ul><ul><li>Difficulty to evaluate the risk to guide the manual inspection of the defect candidates </li></ul></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  28. 28. Endogenous Transformation by Example Deviance from perfection Detection rules generation Endogenous Transformation Defining Trans. Detecting Elements Testing Trans. Conclusion <ul><li>Two perspectives : </li></ul>
  29. 29. Overview Rules generation (Harmony search) Base of examples Quality metrics Generated rules Defining Trans. Detecting Elements Testing Trans. Conclusion
  30. 30. Harmony Search <ul><li>Intuition </li></ul><ul><ul><li>Music composition </li></ul></ul><ul><li>Algorithm </li></ul><ul><ul><li>Generate some rules randomly </li></ul></ul><ul><ul><ul><li>rule = metrics composition </li></ul></ul></ul><ul><ul><li>Evaluate the quality of these rules </li></ul></ul><ul><ul><ul><li>Comparing between the detected defects and expected ones </li></ul></ul></ul><ul><ul><li>Repeat step 1 and 2 Until (stopping criteria) </li></ul></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  31. 31. Validation <ul><li>Defects detection in three open source projects Xerces, Quick UML et Gantt </li></ul><ul><li>Validation data </li></ul><ul><ul><li>3-fold cross validation </li></ul></ul><ul><ul><li>Occurrences of Blob, Spaghetti code (SC), and Function decomposition (FD) </li></ul></ul><ul><ul><li>Found manually </li></ul></ul><ul><ul><li>Used in rule-based detection DECOR ( Moha et al. ’10 ) </li></ul></ul><ul><li>Comparison with DECOR </li></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion Systems Number of classes KLOC Quick UML 142 19 Xerces 991 240 Gantt 471 91
  32. 32. Gantt Results Defining Trans. Detecting Elements Testing Trans. Conclusion
  33. 33. Comparison with DECOR Defining Trans. Detecting Elements Testing Trans. Conclusion HS DECOR Precision-Gantt 87% 59% Precision-Quick UML 86% 42% Precision-Xerces 81% 67%
  34. 34. Endogenous Transformation by Example Deviance from perfection Detection rules generation Endogenous Transformation Defining Trans. Detecting Elements Testing Trans. Conclusion <ul><li>Two perspectives : </li></ul>
  35. 35. Artificial Immune System <ul><li>Intuition : </li></ul><ul><ul><li>Biological Immune system </li></ul></ul><ul><li>Negative selection principal ( Forrest et al., ’ 95 ) </li></ul><ul><ul><li>Each deviation from the normal cell behaviour is considered as a risk </li></ul></ul><ul><li>Deviance from perfection is a better criterion than closeness to evil when identifying risky code </li></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  36. 36. Negative Selection Detector Non-self self self Affinity Defining Trans. Detecting Elements Testing Trans. Conclusion Foreign element
  37. 37. Overview Negative Selection Base of examples Risky Candidates Defining Trans. Detecting Elements Testing Trans. Conclusion
  38. 38. Detection With Negative Selection Detector generation Risk estimation Reference code (Self) Detectors Code to evaluate Risky code (Non-self) Defining Trans. Detecting Elements Testing Trans. Conclusion
  39. 39. Detector Generation and Refinement <ul><li>Heuristic search using a genetic algorithm </li></ul><ul><ul><li>Initial population of detectors (artificial code) </li></ul></ul><ul><ul><li>Evaluate the quality of detectors </li></ul></ul><ul><ul><ul><li>Maximise the generality of detectors to cover non-self : LG(d i ) </li></ul></ul></ul><ul><ul><ul><li>Minimise the overlap between detectors : O(d i ) </li></ul></ul></ul>Detectors d 1 d 2 d 3 Self s 1 s 2 s 3 s 4 s 5 Defining Trans. Detecting Elements Testing Trans. Conclusion
  40. 40. Risk Estimation Detectors Similarity distance: Risk ei d 1 d 2 d 3 d 4 d 5 d 6 d 7 d 8 d 9 d 10 Defining Trans. Detecting Elements Testing Trans. Conclusion Code fragment to evaluate
  41. 41. Validation <ul><li>Defects detection in two open source projects Xerces et Gantt </li></ul><ul><ul><li>Reference system : JHotDraw </li></ul></ul><ul><li>Validation data </li></ul><ul><ul><li>Occurrences of Blob, Spaghetti code (SC), and Function decomposition (FD) </li></ul></ul><ul><ul><li>Found manually </li></ul></ul><ul><ul><li>Used in rule-based detection DECOR ( Moha et al. ’10 ) </li></ul></ul><ul><li>Comparison with DECOR </li></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion Systems Number of classes KLOC Gantt 245 31 Xerces 991 240 JHotdraw 471 91
  42. 42. Xerces results Defining Trans. Detecting Elements Testing Trans. Conclusion
  43. 43. Comparison with DECOR Defining Trans. Detecting Elements Testing Trans. Conclusion AIS DECOR Precision-Gantt 95% 59% Precision-Xerces 90% 67%
  44. 44. Outline <ul><li>Defining Exogenous Transformations </li></ul><ul><li>Detecting Elements to Transform for Endogenous Transformations </li></ul><ul><li>Testing Transformations </li></ul><ul><li>Conclusion and Future work </li></ul>Defining Trans. Detecting Elements Testing Trans . Conclusion
  45. 45. Transformation Testing <ul><li>Model transformation testing </li></ul><ul><ul><li>Test cases generation </li></ul></ul><ul><ul><ul><li>Source models </li></ul></ul></ul><ul><ul><li>Oracle function definition </li></ul></ul>Transformation mechanism Results verification Source models Target models Oracle function Detected errors Defining Trans. Detecting Elements Testing Trans. Conclusion
  46. 46. Transformation Testing <ul><li>Existing oracle function definitions </li></ul><ul><ul><li>Model comparison ( Lin et al. ’ 05, Baudry et al. ’08, ... ) </li></ul></ul><ul><ul><ul><li>Target models vs expected models </li></ul></ul></ul><ul><ul><li>Specification conformance : pre- and post- conditions ( Baudry et al. ’ 07, Giner et al. ’09, ... ) </li></ul></ul><ul><ul><ul><li>Design by contract </li></ul></ul></ul><ul><ul><ul><li>Pattern matching </li></ul></ul></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  47. 47. Transformation Testing <ul><li>Oracle function definition is difficult </li></ul><ul><ul><li>Model comparison </li></ul></ul><ul><ul><ul><li>Expected target model for each test case </li></ul></ul></ul><ul><ul><ul><li>Graph isomorphism problem </li></ul></ul></ul><ul><ul><li>Specification conformance </li></ul></ul><ul><ul><ul><li>Large number of constraints to define </li></ul></ul></ul><ul><ul><ul><li>Difficult to write in practice </li></ul></ul></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  48. 48. Approach Overview Defining Trans. Detecting Elements Testing Trans. Conclusion
  49. 49. Evaluation <ul><li>CLD-to-RS transformation </li></ul><ul><ul><li>12 examples (industrial projects) </li></ul></ul><ul><li>SD-to-CPN transformation </li></ul><ul><ul><li>10 examples </li></ul></ul><ul><li>n-fold cross validation </li></ul><ul><ul><li>Average precision and recall </li></ul></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  50. 50. CLD-to-RS Results Defining Trans. Detecting Elements Testing Trans. Conclusion Source Model Number of elements Number of transormation errors introduced manually Precision Recall SM1 72 13 82% 93% SM2 83 14 93% 94% SM3 49 11 92% 100% SM4 53 16 88% 100% SM5 38 9 90% 100% SM6 47 12 100% 100% SM7 78 16 84% 95% SM8 34 8 100% 100% SM9 92 14 82% 93% SM10 28 9 100% 100% SM11 59 13 93% 100% SM12 63 15 94% 100% Average 58 12 91% 98%
  51. 51. SD-to-CPN Results Defining Trans. Detecting Elements Testing Trans. Conclusion Source Model Number of elements Number of transormation errors introduced manually Precision Recall SM1 16 14 93% 93% SM2 18 12 94% 95% SM3 27 11 85% 95% SM4 28 11 88% 100% SM5 36 8 75% 100% SM6 36 9 100% 100% SM7 42 17 88% 100% SM8 49 10 91% 100% SM9 53 14 100% 100% SM10 57 9 100% 96% Average 36 11 91% 97%
  52. 52. Errors detected Defining Trans. Detecting Elements Testing Trans. Conclusion Test unit Risk Meta-model error Transformation logic (rules) error UC 26 0.98 X X UC 24 0.95 X X UC 22 0.94 X UC 23 0.90 X UC 21 0.90 X UC 27 0.85 X UC 25 0.78 UC 28 0.76
  53. 53. Tool Defining Trans. Detecting Elements Testing Trans. Conclusion
  54. 54. Outline <ul><li>Defining Exogenous Transformations </li></ul><ul><li>Detecting Elements to Transform for Endogenous Transformations </li></ul><ul><li>Testing Transformations </li></ul><ul><li>Conclusion and Future work </li></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  55. 55. Conclusion <ul><li>Novel “by example” solutions for </li></ul><ul><ul><li>Model transformation </li></ul></ul><ul><ul><ul><li>MODELS08, SOSYM Journal, ECMFA10, BMFA10, MPM10, LMO09 </li></ul></ul></ul><ul><ul><li>Design Defects detection </li></ul></ul><ul><ul><ul><li>ASE10, CSMR11, FASE11 </li></ul></ul></ul><ul><ul><li>Transformation testing </li></ul></ul><ul><ul><ul><li>ASE Journal, CASCON10 </li></ul></ul></ul><ul><li>Validation </li></ul><ul><ul><li>Very encouraging results </li></ul></ul><ul><ul><li>Comparison with existing approaches </li></ul></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  56. 56. Future Work <ul><li>Application to other transformation problems </li></ul><ul><ul><li>Transformation rules generation from examples </li></ul></ul><ul><ul><li>Code generation </li></ul></ul><ul><ul><li>Model refinement </li></ul></ul><ul><ul><li>Model evolution </li></ul></ul><ul><ul><li>Completing the three-steps process for design defects and testing </li></ul></ul><ul><ul><ul><li>identification and correction steps </li></ul></ul></ul><ul><li>Validation with larger systems </li></ul>Defining Trans. Detecting Elements Testing Trans. Conclusion
  57. 57. Publications <ul><li>Book Chapters and Journal Papers : </li></ul><ul><ul><li>Kessentini, M., Sahraoui, H., and Boukadoum, M. 2010. Search-Based Model Transformation by Example, Software and System Modeling Journal-Special Issue of MODELS08 (accepted) </li></ul></ul><ul><ul><li>Kessentini, M., Sahraoui, H., and Boukadoum, M. 2010. Example-based Model Transformation Testing, Automated Software Engineering Journal (accepted) </li></ul></ul><ul><ul><li>Asztalos, M., Kessentini, M., Syriani, E., and Wimmer, M. 2010. Towards Rule Composition. Journal of the Electronic Communications of the EASST, Multi-Paradigm Modeling (accepted) </li></ul></ul><ul><ul><li>Kessentini, M., Sahraoui, H., Boukadoum, M., 2010. Maintenance, Evolution and Reengineering of Software,Models by Example. In ”Emerging Technologies for the Evolution and Maintenance of Software Models” book , edited by Jrg Rech and Christian Bunse (Under review). </li></ul></ul>
  58. 58. Publications <ul><li>Refereed Conference </li></ul><ul><ul><li>Kessentini M., Vaucher S., and Sahraoui H. 2010. Deviance from perfection is a better criterion than closeness to evil when identifying risky code. In Proceedings of the IEEE/ACM international conference on Automated software engineering ASE 2010. </li></ul></ul><ul><ul><li>Kessentini, M., Sahraoui, H., and Boukadoum, M. 2008. Model Transformation as an Optimization Problem. In Proceedings of the 11th international Conference on Model Driven Engineering Languages and Systems MODELS 2008. </li></ul></ul><ul><ul><li>Kessentini, M., Sahraoui, H., Boukadoum, and M. Wimmer, M. 2011. A Music-Inspired Approach for Design Defects Detection Proceedings of the 15th European Conference on Software Maintenance and Reengineering CSMR 2011 </li></ul></ul>
  59. 59. Publications <ul><ul><li>Kessentini, M., Bouchoucha, A.,Sahraoui, H., and Boukadoum, M. 2010. Example-Based Sequence Diagrams to Colored Petri NetsTransformation Using Heuristic Search. In Proceedings of the 6th European Conference on Modelling Foundations and Applications ECMFA 2010 </li></ul></ul><ul><ul><li>Kessentini, M., Sahraoui, H., Boukadoum, and M. Wimmer, M. 2011. Search-based Design Defects Detection by Example. In 14th International Conference on Fundamental Approaches to Software Engineering Conference FASE 2011 </li></ul></ul><ul><ul><li>Kessentini, M., Sahraoui, H., and Boukadoum, M. 2009. Transformation de modèle par l’exemple : approche par méta-heuritique. Actes du 15e conférence francophone sur les Langages et Modéles à Objets , LMO2009. </li></ul></ul>
  60. 60. Publications <ul><ul><li>Kessentini, M., Sahraoui, H., and Boukadoum, M. 2010. Testing Sequence Diagram to Colored Petri Nets Transformation: An Immune System Metaphor. In Proceedings of the 20th Annual International Conference on Computer Science and Software Engineering CASCON2010. (Best Paper Award ) </li></ul></ul><ul><ul><li>Kessentini, M., Wimmer, M.,Sahraoui, H., and Boukadoum, M. 2010. Generating Transformation Rules from Examples for Behavioral Models. In Proceedings of Behavioural Modelling - Foundations and Application BMFA 2010 (Best Paper Award) </li></ul></ul><ul><ul><li>Asztalos, M., Syriani, E., Kessentini, M., Wimmer, M., and Wimmer, M. 2010. Towards Rule Composition. MODELS 2010 MPM Workshops . Springer. (Best Paper Award ) </li></ul></ul>
  61. 61. My long-term project <ul><li>Source : rejected paper </li></ul><ul><li>Target : accepted paper </li></ul><ul><li>Base of examples : good/bad quality of papers </li></ul>

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