Computational intelligence in software engineering
This unique volume is the first publication on software engineering and computational intelligence (CI) viewed as a synergistic interplay of neurocomputing, granular computation (including fuzzy sets and rough sets), and evolutionary methods. It presents a unified view of CI in the context of softwa...
| Други автори: | Pedrycz, Witold, 1953-, Peters, James F. |
|---|---|
| Формат: | Електронен |
| Език: | English |
| Публикувано: |
Singapore ; River Edge, N.J. :
World Scientific,
℗♭1998.
|
| Серия: |
Advances in fuzzy systems ;
vol. 16. |
| Предмети: | |
| Онлайн достъп: |
http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=532613 |
| Подобни документи: |
Print version::
Computational intelligence in software engineering. |
Съдържание:
- PREFACE; References; NEUROCOMPUTING IN SOFTWARE ENGINEERING; ON THE PROMISE OF NEURAL NETWORKS TO SUPPORT SOFTWARE TESTING; 1 Introduction; 2 Background on Neural Networks; 2.1 Training; 2.2 The Overfitting Problem; 3 System Testing; 3.1 Input Representation; 3.2 Test Data Generation; 3.3 Test Oracle; 3.4 Neural Network Training; 3.5 Training Results; 4 White Box Testing; 4.1 VHDL Branch Coverage; 4.2 Experiment with 3 Clock-ticks; 4.3 Higher Number of Clock Ticks; 4.4 Neural Net Training; 5 Assessment and Conclusions; Acknowledgements; References; Exercises.
- NEURAL NETWORKS FOR SOFTWARE QUALITY PREDICTION1 Introduction; 2 Neural Networks; 3 Case Studies; 3.1 Example 1: Military System; 3.2 Example 2: Telecommunication System; 4 Methodology; 4.1 Transformation of Raw Data; 4.2 Prepare Data Sets; 4.3 Develop Models; 4.4 Making Predictions; 4.5 Evaluating Predictive Quality; 5 Conclusions; Acknowledgments; References; Problems; SELF-ORGANIZING MAPS AND SOFTWARE REUSE; 1 Introduction; 2 Software Reuse; 3 Organization of Software Libraries; 4 Self-Organizing Maps; 5 The Experimental Software Library; 5.1 The NIH class library.
- 5.2 Cluster analysis: The baseline for comparison6 Unsupervised Learning in Software Library Organization; 6.1 Results with the standard self-organizing map model; 6.2 Adaptive coordinates for improved similarity representation in self-organizing maps; 6.3 Results with the growing grid model; 7 Conclusions; Acknowledgments; References; Exercises; THE CASE FOR AN INDUCTIVE COMPUTING SCIENCE; 1 Introduction; 2 Deduction as the Framework of Computing; 3 Examples of Inductive Methods; 4 The Neglect of Inductive Methods; 5 Reasons for the Neglect; 6 The Relative Merits of Inductive Programming.
- 7 Concluding RemarksAcknowledgments; References; Exercises; Exercise 1; Exercise 2; EVOLUTIONARY COMPUTING IN SOFTWARE ENGINEERING; AUTOMATIC CREATION OF COMPUTER PROGRAMS FOR DESIGNING ELECTRICAL CIRCUITS USING GENETIC PROGRAMMING; 1. Introduction; 2. Five Problems of Analog Circuit Design; 3. Design by Genetic Programming; 3.1. The Embryonic Circuit; 3.2. Component-Creating Functions; 3.3. Connection-Modifying Functions; 4. Preparatory Steps; 4.1. Embryonic Circuit; 4.2. Program Architecture; 4.3. Function and Terminal Sets; 4.4. Fitness Measure; 4.4.1 Lowpass Filter.
- 4.4.2 Tri-state Frequency Discriminator4.4.3 Computational Circuit; 4.4.4 Robot Controller Circuit; 4.4.5 60 dB Amplifier; 4.5. Control Parameters; 4.6. Implementation on Parallel Computer; 5. Results; 5.1. Lowpass Filter; 5.2. Tri-state Frequency Discriminator; 5.3. Computational Circuit; 5.4. Robot Controller Circuit; 5.5. 60 dB Amplifier; 6. Other Circuits; 7. Conclusion; References; THE COMPUTER ZOO
- EVOLUTION IN A BOX; 1 Modeling Life; 2 Defining the Zoo; 2.1 Language Principles; 2.2 The Environment; 2.3 What The Zoo Can Do; 2.4 Relations to Biology; 3 Running the Zoo.