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--- teaching:reverse:2017:references [2017/12/17 12:47] – [Généralités] blay
+++ teaching:reverse:2017:references [2018/12/07 10:36] (current) – [Etudes] blay
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        - [[http://www.cc.uah.es/drg/jif/2013HerraizRRG_CSUR.pdf|Herraiz I, Rodriguez D, Robles G, Gonzalez-Barahona JM (2013) The Evolution of the Laws of Software Evolution: A Discussion Based on a Systematic Literature Review. ACM Comput Surv 46:28:1--28:28.]]
         - Demeyer S, Ducasse S, Nierstrasz O (2002) Object Oriented Reengineering Patterns. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA
+        - http://blog.cleancoder.com/ relativement au livre : Clean Code: A Handbook of Agile Software Craftsmanship
+by Robert C. Martin
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 <panel  title="Sélection d'articles présentant des études de rétro-ingénierie">
+  -[[ https://research.google.com/pubs/pub45794.html|Who broke the build?: automatically identifying changes that induce test failures in continuous integration at Google scale]]
+       * Auteurs : Celal Ziftci, Jim Reardon
+       * Date de publication :  2017/5/20
+       * Conférence   Proceedings of the 39th International Conference on Software Engineering: Software Engineering in Practice Track
+       * Pages 113-122
+       * Description
+                 * Quickly identifying and fixing code changes that introduce regressions is critical to keep the momentum on software development, especially in very large scale software repositories with rapid development cycles, such as at Google. Identifying and fixing such regressions is one of the most expensive, tedious, and time consuming tasks in the software development life-cycle. Therefore, there is a high demand for automated techniques that can help developers identify such changes while minimizing manual human intervention …
   - [[https://users.uom.gr/~achat/papers/ECSA2017.pdf| The Evolution of Technical Debt in the Apache Ecosystem]]
          * Authors : Georgios Digkas, Mircea Lungu, Alexander Chatzigeorgiou, Paris Avgeriou
@@ Line 102: / Line 111: @@
                           * Code review is systematic examination of a software system's source code. It is intended to find mistakes overlooked in the initial development phase, improving the overall  quality of software and reducing the risk of bugs among other benefits. Reviews are done in various forms such as pair programming, informal walk-through, and formal inspections.  Code review has been found to accelerate and streamline the process of software  development like very few other practices in software development can. In this paper we …
      - Beaucoup d'articles proviennent de [[ http://ase2017.org/accepted_papers| ASE 2017]], d'autres peuvent être intéressants en fonction de vos sujets d'études.
+  - Hebig, R., Quang, T. H., Chaudron, M. R. V., Robles, G., & Fernandez, M. A. (2016). The quest for open source projects that use UML. In Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems - MODELS ’16. https://doi.org/10.1145/2976767.2976778
+  - [[http://salt.ece.ubc.ca/publications/docs/icse18-sabalan.pdf|Inferring Hierarchical Motifs from Execution Traces]]
+  - {{:teaching:reverse:2017:mobilesoft_2018.pdf|}}
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     -  Benomar O, Sahraoui H, Poulin P (2013) Visualizing software dynamicities with heat maps. Softw. Vis. (VISSOFT), 2013 First IEEE Work. Conf. pp 1–10
     - [[https://papyrus.bib.umontreal.ca/xmlui/bitstream/handle/1866/11998/Benomar_Omar_2015_these.pdf?sequence=2&isAllowed=y|A Unified Framework for the Comprehension of Software’s Time Dimension]]
+    - {{:teaching:reverse:2017:7_data_mining_-_practical_machine_learning_tools_and_techniques_3rd_ed_.pdf|Witten IH, Frank E, Hall M a (2011) Data Mining: Practical Machine Learning Tools and Techniques (Google eBook). Complement Lit None. doi: 0120884070, 9780120884070}}
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