Computational Intelligence Techniques and Their Applications to Software Engineering Problems

✍ Scribed by Bansal, Ankita(Editor);Jain, Abha(Editor);Jain, Sarika(Editor);Jain, Vishal(Editor)

• Publisher: CRC Press
• Year: 2020
• Tongue: English
• Leaves: 267
• Category: Library

✦ Synopsis

Computational Intelligence Techniques and Their Applications to Software Engineering Problemsfocuses on computational intelligence approaches as applicable in varied areas of software engineering such as software requirement prioritization, cost estimation, reliability assessment, defect prediction, maintainability and quality prediction, size estimation, vulnerability prediction, test case selection and prioritization, and much more. The concepts of expert systems, case-based reasoning, fuzzy logic, genetic algorithms, swarm computing, and rough sets are introduced with their applications in software engineering. The field of knowledge discovery is explored using neural networks and data mining techniques by determining the underlying and hidden patterns in software data sets. Aimed at graduate students and researchers in computer science engineering, software engineering, information technology, this book:



Covers various aspects of in-depth solutions of software engineering problems using computational intelligence techniques



Discusses the latest evolutionary approaches to preliminary theory of different solve optimization problems under software engineering domain



Covers heuristic as well as meta-heuristic algorithms designed to provide better and optimized solutions



Illustrates applications including software requirement prioritization, software cost estimation, reliability assessment, software defect prediction, and more



Highlights swarm intelligence-based optimization solutions for software testing and reliability problems

✦ Table of Contents

Cover......Page 1
Half Title......Page 2
Title Page......Page 4
Copyright Page......Page 5
Table of Contents......Page 6
Preface......Page 8
Editors......Page 10
Contributors......Page 12
Chapter 1 Implementation of Artificial Intelligence Techniques for Improving Software Engineering......Page 16
1.1 Introduction......Page 17
1.1.1 Literature Review......Page 18
1.2 Aspects of SE and AI......Page 19
1.2.1 Factors of Interaction between AI and SE......Page 21
1.2.2 Research Areas of Interaction between AI and SE......Page 22
1.3 AI Techniques......Page 23
1.4 Why AI Techniques Are Implemented in SE......Page 27
1.5.1 Requirements Engineering (RE)......Page 28
1.5.2 Software Architecture Design......Page 29
1.5.3 Risk Management (RM)......Page 30
1.6.1 Open Problems That Can Occur during the Application of AI Techniques to SE......Page 32
1.8 Future Scope......Page 34
References......Page 35
2.1 Introduction......Page 36
2.2 Related Work......Page 37
2.4.1.2 Artificial Neural Network......Page 38
2.6 Results and Discussions......Page 39
2.7 Statistical Analysis of the Result......Page 41
References......Page 42
Chapter 3 Implementation of Data Mining Techniques for Software Development Effort Estimation......Page 44
3.1 Introduction......Page 45
3.2 Literature Review......Page 46
3.3 Data Mining......Page 47
3.3.1 Classification......Page 48
3.3.6 Association Rule......Page 51
3.4 Software Engineering......Page 52
3.4.1.4 Mailing Data......Page 53
3.5 Software Estimation......Page 54
3.5.1.2 Function Point Analysis......Page 55
3.5.1.5 COCOMO (Constructive Cost Model)......Page 56
3.5.2 Data Mining Techniques of Software Effort Estimation......Page 57
3.5.2.2 KNN (K Nearest Neighbors)......Page 58
3.5.2.4 CBR (Case Based Reasoning)......Page 59
3.5.2.6 CART (Classification and Regression Tree)......Page 60
References......Page 61
4.1 Introduction......Page 64
4.2 Machine Learning Techniques for Empirical Software Measurements......Page 65
4.2.1.1 Quality Prediction as Classification Problem......Page 66
4.2.1.2 Quantity Prediction as Regression Problem......Page 67
4.2.2 ML Methods at Our Disposal for Solving Software Measurements Learning Problems......Page 69
4.3 Current Trends of Using ML in Software Measurements......Page 70
4.5.1 Software Quality Measurement Model (Classification)......Page 72
4.6 Case Study......Page 73
4.6.1.1 Data Description......Page 74
4.7 Future Scope & Conclusion......Page 75
References......Page 76
Chapter 5 Project Estimation and Scheduling Using Computational Intelligence......Page 80
5.2 Proposed Model......Page 81
5.2.1 Artificial Neural Networks (ANNs)......Page 82
5.2.2 Fuzzy System (FS)......Page 83
5.2.3 Evolutionary Computation (EC)......Page 85
5.3 What is Software Engineering?......Page 87
5.3.2 Software Project Scheduling......Page 88
5.3.3 Software Engineering Project Scheduling Technique......Page 89
5.4.1 Business Implication......Page 91
References......Page 92
6.1 Introduction......Page 94
6.3 Problem Identification......Page 96
6.4 Exponential Intuitionistic Fuzzy Measures of Similarity......Page 97
6.4.1 Effectiveness of the Offered Measures of Similarity in Pattern Recognition......Page 102
6.5 Application of Weighted Exponential Intuitionistic Fuzzy Similarity Measures......Page 104
6.5.1 Computational Approach......Page 105
6.6 Pros and Cons of the Offered Solutions......Page 107
References......Page 109
7.1 Introduction......Page 114
7.3 Nature-Inspired Algorithms......Page 115
7.4.1 Genetic Algorithms (GA)......Page 116
7.4.4 BAT Algorithm......Page 118
7.5 Findings and Future Directions......Page 119
7.6 Conclusion......Page 123
References......Page 124
Chapter 8 Identification and Construction of Reusable Components from Object-Oriented Legacy Systems Using Various Software Artifacts......Page 126
8.1 Introduction......Page 127
8.2 Literature Survey......Page 129
8.3.1 Logical Components Identification......Page 131
8.3.1.1 Relation Identification and Modeling......Page 132
8.3.2 Components Construction......Page 134
8.3.2.1 Provides and Requires Interface Identification of a Component......Page 135
8.4 Experimental Planning......Page 136
8.4.1 Dataset Considered......Page 137
8.4.3 Research Questions and Evaluation Criteria......Page 138
8.5.1 Results for RQ1 and Interpretation......Page 139
8.5.2 Results for RQ2 and Interpretation......Page 140
8.5.3 Results for RQ3 and Interpretation......Page 141
8.5.4 Results for RQ4 and Interpretation......Page 142
8.5.5 Results for RQ5 and Interpretation......Page 145
8.6 Conclusion and Future Works......Page 146
References......Page 147
9.1 Introduction......Page 152
9.2 Literature Survey......Page 154
9.3 Problem Identification......Page 155
9.4 Comparative Study of Available Solution......Page 156
9.5 Proposed Solution......Page 157
9.5.2 Conformance to Non-Functional Requirements (NF)......Page 160
9.7 Conclusion......Page 161
References......Page 162
Bibliography......Page 163
10.1 Introduction......Page 166
10.2 The mzPredictor work flow......Page 167
10.3 The mzInstrumenter......Page 168
10.3.1 The Trace Grammar......Page 169
10.4 State-Based Encoding......Page 170
10.4.1 The Abstract Variables......Page 171
10.4.2.2 The Action Constraint (F[sub(acts)])......Page 172
10.4.2.3 The Scheduling Constraint (F[sub(sched)])......Page 175
10.4.3 Experimental Results......Page 176
10.5 Order-Based Encoding......Page 177
10.5.1 The Abstract Variables......Page 178
10.5.2.2 The Assignment Constraint (F[sub(asgn)])......Page 179
10.5.2.3 The Receive Constraint (F[sub(recv)])......Page 180
10.5.3 Experimental Results......Page 181
10.6 Reporting a Violating Scenario......Page 182
10.7 Conclusion......Page 183
References......Page 184
11.1 Introduction......Page 186
11.2 Agile Practices in the Software Industry......Page 187
11.3 Real-Life Experience of Agile Teams......Page 189
References......Page 193
12.1 Introduction......Page 196
12.4 Agile Methodology......Page 197
12.6 Framework for Agile Practices......Page 198
12.8 Precincts of Using Agile Methodology......Page 199
12.9 Methodology......Page 200
References......Page 202
13.1 Introduction......Page 204
13.2 Literature Survey......Page 205
13.4 Comparative Study of Existing Solutions......Page 206
13.5.1 Image Acquisition and Database......Page 207
13.5.2 Experimentation on a Real-time Database to Identify the Age Group from Iris Biometrics Using Deep Learning......Page 209
13.5.3 Performance Comparison of the Proposed System with Earlier Methods for Age-Group Prediction......Page 212
13.7 Future Scope......Page 213
References......Page 214
Chapter 14 Hybrid Intelligent Decision Support Systems to Select the Optimum Fuel Blend in CI Engines......Page 216
14.1 Introduction......Page 217
14.2 Experimental Setup......Page 218
14.3.1 Fuzzy Logic......Page 219
14.3.2 F-TOPSIS......Page 220
14.3.3 F- VIKOR......Page 222
14.3.4 F- MOORA......Page 223
14.3.5 COPRAS-G......Page 224
14.4 The Proposed Method......Page 225
14.5.1 F-TOPSIS Computations......Page 226
14.5.2 F-VIKOR Computations......Page 239
14.5.4 F-COPRAS-G Computations......Page 243
Conclusion......Page 247
References......Page 250
15.1.1 Cloud Computing Environment......Page 254
15.2 Review of Literature......Page 255
15.4 Proposed Solution......Page 259
15.5.1 Benetfis......Page 260
References......Page 261
Index......Page 264