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Logo Ateneo
   
Soft Computing Techniques for Software Engineering
Docente
Francesca Arcelli
Data e luogo

Settembre/ottobre 2009

Motivazioni e obiettivi
Programma

“Undoubtedly, software has become a pervasive and critical component.. It is
needless to say that larger and larger spectrum of human individual and social
endeavours depend, and will growingly depend, on software systems and their
infrastructures. The dimension and the complexity of many software systems on
which the correct operation of business and other social processes, and even the
safety of human lives, depend upon have reached a point of criticality at which they
are very difficult to control and maintain.
The discipline of Software Engineering is striving to meet these ever growing
challenges by acquiring new knowledge, devising tools and methods for gathering
requirements, designing architecture and algorithms, implementing the code
verifying and validating systems, and offer suitable means supporting their evolution
over time. Soft Computing technologies have provided us with a unique opportunity
to establish a coherent Software Engineering environment in which uncertainty and
partial data and knowledge are systematically handled. In fact, a traditional view of
Software Engineering has been predominantly, if not exclusively, focused on
quantitative and precise approaches, neglecting the fact that software constructs are
inherently human-centric, and thus uncertainty and partial knowledge are
unavoidable. By seamlessly combining learning, adaptation, evolution, and fuzziness,
Soft Computing complements current approaches based on statistical methods, thus
allowing us develop a more comprehensive and unified framework to the effective
management of uncertainty in Software Engineering.” [1]

  1. Introduction: Why using soft computing techniques in Software Engineering
  2. Introduction to different Soft Computing Techniques: Data Mining, Tecniche di Classificazione, Algoritmi Genetici
  3. Introduction to Fuzzy Logic and Similarity
  4. Reverse Engineering, Design Pattern Detection and Data Mining
  5. Design Pattern Detection and Fuzzy Logic
  6. Software Code Reuse and Similarity
  7. Testing and Similarity
  8. Software Architecture Reconstruction and Data Mining
  9. Software Architecture Reconstruction and Fuzzy Logic
  10. System Modernization
  11. Community Detection Algorithms and Complex Networks for Web Software Engineering
  12. Conclusions and Discussion on the Exam Projects
Modalità di svolgimento

Lezioni frontali.

Modalità d’esame
Da definire
Materiale didattico
[1] Special Issue on Software Engineering and Soft Computing, Ed.G.Canfora and
W.Pedrycz, Springer, Jan 2008
Approfondimenti

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redazioneweb@disco.unimib.it - ultimo aggiornamento di questa pagina 02/05/2012