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Automated Software Engineering: A Deep Learning-Based Approach 2020 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 118 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, XI, 118 p., 1 Paperback / softback
  • Sari: Learning and Analytics in Intelligent Systems 8
  • Ilmumisaeg: 08-Jan-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030380084
  • ISBN-13: 9783030380083
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Automated Software Engineering: A Deep Learning-Based Approach 2020 ed.Suurem pilt
  • Formaat: Paperback / softback, 118 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, XI, 118 p., 1 Paperback / softback
  • Sari: Learning and Analytics in Intelligent Systems 8
  • Ilmumisaeg: 08-Jan-2021
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030380084
  • ISBN-13: 9783030380083
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030380083.html
Märksõnad:

This book discusses various open issues in software engineering, such as the efficiency of automated testing techniques, predictions for cost estimation, data processing, and automatic code generation. Many traditional techniques are available for addressing these problems. But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software’s complexity. Hence, many previously used methods are proving insufficient to solve the problems now arising in software development.

The book highlights a number of unique problems and effective solutions that reflect the state-of-the-art in software engineering. Deep learning is the latest computing technique, and is now gaining popularity in various fields of software engineering. This book explores new trends and experiments that have yielded promising solutions to current challenges in software engineering. As such, it offers a valuable reference guide for a broad audience including systems analysts, software engineers, researchers, graduate students and professors engaged in teaching software engineering.


1 Selection of Significant Metrics for Improving the Performance of Change-Proneness Modules
1(18)
1.1 Introduction
1(3)
1.2 Experiment Dataset
4(1)
1.3 Framework for Software Metrics Validation
4(2)
1.4 Classification Techniques
6(1)
1.5 Experimental Setup
7(1)
1.6 Analysis of Results
8(8)
1.6.1 Artificial Neural Network (ANN) Model
9(3)
1.6.2 Ensembles of Classification Models
12(1)
1.6.3 Comparison of Results
13(3)
1.7 Conclusion
16(3)
References
17(2)
2 Effort Estimation of Web Based Applications Using ERD, Use Case Point Method and Machine Learning
19(20)
2.1 Introduction
19(2)
2.2 Literature Review
21(2)
2.3 Overview of the
Chapter
23(1)
2.3.1 Problem Statement
24(1)
2.3.2 Data Set Used
24(1)
2.4 Proposed Work with Estimating the Size
24(6)
2.4.1 DSPBERD Metric
24(3)
2.4.2 UCPt Method
27(3)
2.5 Effort Estimation of the Web-Based Application
30(2)
2.6 Effort Estimation Using Machine Learning
32(3)
2.6.1 Data Set Used for Training Purposes
32(1)
2.6.2 Support Vector Machine (SVM)
32(2)
2.6.3 Nearest Neighbour Algorithm
34(1)
2.7 Conclusion and Future Work
35(4)
References
36(3)
3 Usage of Machine Learning in Software Testing
39(16)
3.1 Introduction
40(1)
3.2 Background: Software Vulnerability Analysis and Discovery
40(4)
3.2.1 Definition
40(1)
3.2.2 Completeness, Soundness and Undecidable
41(1)
3.2.3 Conventional Approaches
42(1)
3.2.4 Categorizing Previous Work
43(1)
3.3 Vulnerability Prediction Based on Software Metrics
44(1)
3.4 Anomaly Detection Approaches
44(1)
3.5 Vulnerable Code Pattern Recognition
45(1)
3.6 System and Method for Automated Software Testing Based on Machine Learning
46(6)
3.6.1 System for Automated Software Testing
46(2)
3.6.2 Method for Automated Software Testing
48(4)
3.7 Summary of the Techniques
52(1)
3.8 Conclusion
53(2)
References
53(2)
4 Test Scenarios Generation Using Combined Object-Oriented Models
55(18)
4.1 Introduction
55(2)
4.2 Basic Terminology
57(3)
4.2.1 Test Scenarios
57(1)
4.2.2 Sequence Diagram
57(1)
4.2.3 State Machine Diagram
58(1)
4.2.4 Test Coverage Criteria
59(1)
4.2.5 Bio Inspired Meta-heuristic Algorithm for Object-Oriented Testing
60(1)
4.3 Literature Survey
60(1)
4.4 Proposed Model
61(3)
4.4.1 Construction of the Sequence and State-Machine Diagram of System
62(1)
4.4.2 Generate Individual Graphs of the Sequence and State-Machine Diagram
62(1)
4.4.3 Generate Combined Intermediate Graph Named as State-Sequence Intermediate Graph
63(1)
4.4.4 Generate the Test Scenarios SSIG
63(1)
4.5 Implementation and Result Analysis
64(6)
4.5.1 Generate the Test Scenarios SSIG
66(1)
4.5.2 Construction of Combined State-Sequence Intermediate Graph (SSIG)
67(1)
4.5.3 Generation of Test Scenarios
67(1)
4.5.4 Observed Test Cases
68(1)
4.5.5 Analysis of Result
68(2)
4.6 Conclusion
70(3)
References
70(3)
5 A Novel Approach of Software Fault Prediction Using Deep Learning Technique
73(20)
5.1 Introduction
74(1)
5.2 Related Work
75(1)
5.3 Basic Concepts
76(6)
5.3.1 Deep Learning
76(1)
5.3.2 Basic Deep Learning Terminologies
77(3)
5.3.3 Deep Learning Models
80(2)
5.4 Fault Localization Using CNN
82(7)
5.4.1 Framework Overview
83(1)
5.4.2 Experimental Setup and Example Program
84(3)
5.4.3 Procedure of Fault Localization Using CNN
87(2)
5.5 Conclusion and Future Work
89(4)
References
90(3)
6 Feature-Based Semi-supervised Learning to Detect Malware from Android
93
6.1 Introduction
94(1)
6.2 Related Work
95(5)
6.2.1 Research Questions
99(1)
6.3 Data Set Description
100(2)
6.4 Feature Sub-set Selection Approaches
102(2)
6.4.1 Consistency Sub-set Evaluation Approach
102(1)
6.4.2 Filtered Sub-set Evaluation
103(1)
6.4.3 Rough Set Analysis Approach
103(1)
6.4.4 Feature Sub-set Selection Approach Based on Correlation
104(1)
6.5 Machine Learning Classifiers
104(2)
6.6 Proposed Detection Framework
106(1)
6.7 Evaluation of Parameters
106(1)
6.8 Experimental Setup
107(1)
6.9 Outcomes of the Experiment
108
6.9.1 Feature Sub-set Selection Approaches
108(1)
6.9.2 Machine Learning Classifier
109(3)
6.9.3 Comparison of Outcomes
112(1)
6.9.4 Evaluation of Proposed Framework Using Proposed Detection Framework
113(1)
6.9.5 Experimental Finding
114(1)
6.9.6 Conclusion
114(1)
References
115