| Description |
1 online resource : illustrations (black and white) |
| Series |
Computational Intelligence Techniques Ser |
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Computational Intelligence Techniques Ser
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| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- Chapter 1 Implementation of Artificial Intelligence Techniques for Improving Software Engineering -- 1.1 Introduction -- 1.1.1 Literature Review -- 1.2 Aspects of SE and AI -- 1.2.1 Factors of Interaction between AI and SE -- 1.2.2 Research Areas of Interaction between AI and SE -- 1.3 AI Techniques -- 1.4 Why AI Techniques Are Implemented in SE -- 1.5 Impact of AI in Different Phases of Software Development -- 1.5.1 Requirements Engineering (RE) |
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1.5.2 Software Architecture Design -- 1.5.3 Risk Management (RM) -- 1.5.4 Testing -- 1.6 Techniques of AI -- 1.6.1 Open Problems That Can Occur during the Application of AI Techniques to SE -- 1.7 Conclusion -- 1.8 Future Scope -- References -- Chapter 2 Software Effort Estimation: Machine Learning vs. Hybrid Algorithms -- 2.1 Introduction -- 2.2 Related Work -- 2.3 Description of Dataset -- 2.4 Methodology Used -- 2.4.1 Machine Learning -- 2.4.1.1 Random Forest -- 2.4.1.2 Artificial Neural Network -- 2.4.1.3 SVR (Support Vector Regression) (Linear) -- 2.4.2 Hybrid Search-Based Algorithm (HSBA) |
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2.5 Metrics Used to Measure Performance -- 2.6 Results and Discussions -- 2.7 Statistical Analysis of the Result -- 2.8 Conclusions and Future Scope of the Work -- References -- Chapter 3 Implementation of Data Mining Techniques for Software Development Effort Estimation -- 3.1 Introduction -- 3.2 Literature Review -- 3.3 Data Mining -- 3.3.1 Classification -- 3.3.2 Regression -- 3.3.3 Time-Series Analysis -- 3.3.4 Clustering -- 3.3.5 Summarization -- 3.3.6 Association Rule -- 3.3.7 Sequence Discovery -- 3.3.8 Text Mining -- 3.4 Software Engineering |
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3.4.1 Software Engineering Data Sources Available for Mining -- 3.4.1.1 Documentation -- 3.4.1.2 Software Configuration Management (SCM) Data -- 3.4.1.3 Source Code -- 3.4.1.4 Mailing Data -- 3.4.2 Role of Data Mining in Improving the Development Process -- 3.5 Software Estimation -- 3.5.1 Traditional Techniques of Software Effort Estimation -- 3.5.1.1 Lines of Code (LOC) -- 3.5.1.2 Function Point Analysis -- 3.5.1.3 Expert Judgment Method -- 3.5.1.4 Estimating by Analogy -- 3.5.1.5 COCOMO (Constructive Cost Model) -- 3.5.2 Data Mining Techniques of Software Effort Estimation -- 3.5.2.1 K-Mean |
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3.5.2.2 KNN (K Nearest Neighbors) -- 3.5.2.3 Support Vector Machine (SVM) -- 3.5.2.4 CBR (Case Based Reasoning) -- 3.5.2.5 MARS (Multivariate Adaptive Regression Splines) -- 3.5.2.6 CART (Classification and Regression Tree) -- 3.5.2.7 OLS (Ordinary Least Square) -- 3.6 Conclusion -- References -- Chapter 4 Empirical Software Measurements with Machine Learning -- 4.1 Introduction -- 4.2 Machine Learning Techniques for Empirical Software Measurements -- 4.2.1 Formulating "Measuring the Software" as a "Learning Problem" -- 4.2.1.1 Quality Prediction as Classification Problem |
| Bibliography |
Includes bibliographical references and index |
| Notes |
<OL><B><LI>Implementation of Artificial Intelligence Techniques for Improving Software Engineering</LI></B><I><P>Sushma Malik, Monika Arora, Anamika Rana and Mamta Bansal</P></I><P></P><B><P><LI>Software effort estimation: Machine learning vs. Hybrid algorithms</LI><P></P></B><I><P>Wasiur Rhmann</P></I><P></P><B><P><LI>Implementation of Data Mining Techniques for Software Development Effort Estimation</LI><P></P></B><I><P>Deepti Gupta and Sushma Malik</P></I><P></P><B><P><LI>Empirical Software Measurements with Machine Learning</LI><P></P></B><I><P>Somya Goyal and Pradeep Kumar Bhatia</P></I><P></P><B><P><LI>Project Estimation And Scheduling Using Computational Intelligence</LI><P></P></B><I><P>Vikram Bali, Shivani Bali and Gaurav Singhania</P></I><P></P><B><P><LI>Application of Intuitionistic Fuzzy Similarity Measures in Strategic Decision-Making</LI><P></P></B><I><P>Anshu Ohlan</P></I><P></P><B><P><LI>Nature-Inspired Approaches to Test Suite Minimization for Regression Testing</LI><P></P></B><I><P>Anu Bajaj and Om Prakash Sangwan</P></I><P></P><B><P><LI>Identification and Construction of Reusable Components from Object-Oriented Legacy Systems using various Software Artifacts</LI><P></P></B><I><P>Amit Rathee and Jitender Kumar Chhabra</P></I><P></P><B><P><LI>A Software Component Evaluation and Selection Approach Using Fuzzy Logic </LI><P></P></B><I><P>Maushumi Lahon and Uzzal Sharma</P></I><P></P><B><P><LI>Smart Predictive Analysis for Testing Messaging-passing Applications</LI><P></P></B><I><P>Mohamed Elwakil</P></I><P></P><B><P><LI>Status of Agile Practices in the Software Industry in 2019</LI><P></P></B><I><P>Ashish Agrawal, Anju Khandelwal and Jitendra Singh</P></I><P></P><B><P><LI>Agile Methodologies: A Performance Analysis To Enhance Software Quality</LI><P></P></B><I><P>Neha Saini and Indu Chhabra</P></I><P></P><B><P><LI>Pre-Trained Deep Neural Networks for Age Prediction from IRIS Biometrics</LI><P></P></B><I><P>Ganesh Sable, Murtaza Mohiuddin Junaid Farooque and Minakshi Rajput</P></I><P></P><B><P><LI>Hybrid Intelligent Decision Support Systems to Select The Optimum Fuel Blend in CI Engine</LI><P></P></B><I><P>Sakthivel and Naveen Kumar P </P></I><P></P><B><P><LI>Understanding the Significant Challenges of Software Engineering in Cloud Environment</LI><P></P></OL></B><I><P>Santhosh S and Narayana Swamy Ramaiah</P></I> |
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Description based on CIP data; resource not viewed |
| Subject |
Software engineering -- Data processing
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Computational intelligence.
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Computational intelligence
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| Form |
Electronic book
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| Author |
Bansal, Ankita, editor
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Nagawat, Abha Jain, editor
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Jain, Sarika, editor
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Jain, Vishal, 1983- editor.
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Choudhary, Ankur, editor
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| ISBN |
9781000191943 |
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100019194X |
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9781000191929 |
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1000191923 |
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9781000191936 |
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1000191931 |
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9781003079996 |
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1003079997 |
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