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Natural Language Processing for Software Engineering

Edited by Rajesh Kumar Chakrawarti, Ranjana Sikarwar, Sanjaya Kumar Sarangi, Samson Arun Raj Albert Raj, Shweta Gupta, Krishnan Sakthidasan Sankaran, and Romil Rawat
Copyright: 2025   |   Expected Pub Date:2024//
ISBN: 9781394272433  |  Hardcover  |  
612 pages
Price: $225 USD
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One Line Description
Discover how Natural Language Processing for Software Engineering can transform your understanding of agile development, equipping you with essential tools and insights to enhance software quality and responsiveness in todays rapidly changing technological landscape.

Audience
IT specialists, software developers, government officials, political speakers, scientists, policymakers, academics, researchers, and all levels of students working in software engineering

Description
One of the most significant software paradigms in use today, the software development engineering method, has flourished in the previous two decades. Agile development attempts to make businesses more responsive and adaptable; one of its most crucial pillars is the continuous delivery of software, which is one of the principles outlined in Natural Language Processing for Software Engineering. These concepts indicate that several techniques share fundamental elements, such as the iterative methodology that produces incremental, modest delivery of usable software. One of the guiding ideas is that working software is the main measure of progress.
Another essential value upheld throughout the development is ongoing customer connection. Depending on their priorities, software development teams today adopt agile approaches. An agile technique like Scrum, for instance, uses fewer documentation artifacts than more conventional development methodologies. eXtreme Programming (XP) aims to increase software quality and responsiveness for changing customer requirements, and Crystal is regarded as a lightweight or agile process that focuses on people and interactions. This semi-agile approach also reduces artifacts while keeping significant ones like the robustness diagram.
It can be challenging to make modifications to a software system after it has been created using object-oriented Analysis and design without spending a lot of time and money. This may be a drawback in contexts that change quickly, where the system may need to be altered often to accommodate new technologies or company needs.
Natural Language Processing for Software Engineerings goal is to discuss current trends in applying Natural Language Processing for more agile approaches. It makes the case that these areas will continue to develop with meritable contributions. Additionally, it concentrates on improving requirements for engineering, which is a key component of the agile development process. These tools will combine requirements for improvement, agile software development, and artifact transformation to create tools that can be used by developers to create, organize, and update documentation artifacts throughout the agile project process.

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Author / Editor Details
Rajesh Kumar Chakrawarti, PhD is a dean and professor in the Department of Computer Science and Engineering, Sushila Devi Bansal College, Bansal Group of Institutions, India. He has over 20 years of professional experience in academia and industry. Additionally, has organized and attended over 200 seminars, workshops, and conferences and has published over 100 research papers and book chapters in nationally and internationally revered publications.

Ranjana Sikarwar is currently pursuing a PhD from Amity University, Gwalior. She competed her Bachelor of Engineering in 2006 and Master of Technology in Computer Science and Engineering in 2015. Her research interests include social network analysis, graph mining, machine learning, Internet of Things, and deep learning.

Sanjaya Kumar Sarangi, PhD is an adjunct professor and coordinator at Utkal University with over 23 years of experience in the academic, research, and industry sectors. He has a number of publications in journals and conferences, has authored many textbooks and book chapters, and has more than 30 national and international patents. He is an active member and life member of many associations, as well as an editor, technical program committee member, and reviewer in reputed journals and conferences. He has dedicated his career to taking care of Information and Communication Technology to enhance and optimize the information and worldwide research that can lead to improved student learning and teaching methods.

Samson Arun Raj Albert Raj, PhD is an assistant professor and placement coordinator in the Division of Computer Science and Engineering, School of Computer Science and Technology, Karunya Institute of Technology and Sciences, Tamil Nadu, India. His research is focused on smart city development using drone networks and energy grids with various applications, and his areas of expertise include wireless sensor networks, vehicular ad-hoc networks, and intelligent transportation systems.

Shweta Gupta is an assistant professor in the Computer Science and Engineering Department, Medicaps University, Indore, India with a focus on natural language processing, data mining, and machine learning. She aims to close the knowledge gap between theory and real-world applications in the tech sector through her love of research and teaching. Her approach is centered on encouraging creativity and motivating students to strive for technological excellence.

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Table of Contents
Preface
1. Machine Learning and Artificial Intelligence for Detecting Cyber Security Threats in IoT Environmment
Ravindra Bhardwaj, Sreenivasulu Gogula, Bidisha Bhabani, K. Kanagalakshmi, Aparajita Mukherjee and D. Vetrithangam
1.1 Introduction
1.2 Need of Vulnerability Identification
1.3 Vulnerabilities in IoT Web Applications
1.4 Intrusion Detection System
1.5 Machine Learning in Intrusion Detection System
1.6 Conclusion
References
2. Frequent Pattern Mining Using Artificial Intelligence and Machine Learning
R. Deepika, Sreenivasulu Gogula, K. Kanagalakshmi, Anshu Mehta, S. J. Vivekanandan and D. Vetrithangam
2.1 Introduction
2.2 Data Mining Functions
2.3 Related Work
2.4 Machine Learning for Frequent Pattern Mining
2.5 Conclusion
References
3. Classification and Detection of Prostate Cancer Using Machine Learning Techniques
D. Vetrithangam, Pramod Kumar, Shaik Munawar, Rituparna Biswas, Deependra Pandey and Amar Choudhary
3.1 Introduction
3.2 Literature Survey
3.3 Machine Learning for Prostate Cancer Classification and Detection
3.4 Conclusion
References
4. NLP-Based Spellchecker and Grammar Checker for Indic Languages
Brijesh Kumar Y. Panchal and Apurva Shah
4.1 Introduction
4.2 NLP-Based Techniques of Spellcheckers and Grammar Checkers
4.2.1 Syntax-Based
4.2.2 Statistics-Based
4.2.3 Rule-Based
4.2.4 Deep Learning-Based
4.2.5 Machine Learning-Based
4.2.6 Reinforcement Learning-Based
4.3 Grammar Checker Related Work
4.4 Spellchecker Related Work
4.5 Conclusion
References
5. Identification of Gujarati Ghazal Chanda with Cross-Platform Application
Brijeshkumar Y. Panchal
Abbreviations
5.1 Introduction
5.1.1 The Gujarati Language
5.2 Ghazal
5.3 History and Grammar of Ghazal
5.4 Literature Review
5.5 Proposed System
5.6 Conclusion
References
6. Cancer Classification and Detection Using Machine Learning Techniques
Syed Jahangir Badashah, Afaque Alam, Malik Jawarneh, Tejashree Tejpal Moharekar, Venkatesan Hariram, Galiveeti Poornima and Ashish Jain
6.1 Introduction
6.2 Machine Learning Techniques
6.3 Review of Machine Learning for Cancer Detection
6.4 Methods
6.5 Result Analysis
6.6 Conclusion
References
7. Text Mining Techniques and Natural Language Processing
Tzu-Chia Chen
7.1 Introduction
7.2 Text Classification and Text Clustering
7.3 Related Work
7.4 Methodology
17.5 Conclusion
References
8. An Investigation of Techniques to Encounter Security Issues Related to Mobile Applications
Devabalan Pounraj, Pankaj Goel, Meenakshi, Domenic T. Sanchez, Parashuram Shankar Vadar, Rafael D. Sanchez and Malik Jawarneh
8.1 Introduction
8.2 Literature Review
8.3 Results and Discussions
8.4 Conclusion
References
9. Machine Learning for Sentiment Analysis Using Social Media Scrapped Data
Galiveeti Poornima, Meenakshi, Malik Jawarneh, A. Shobana, K.P. Yuvaraj, Urmila R. Pol and Tejashree Tejpal Moharekar
9.1 Introduction
9.2 Twitter Sentiment Analysis
9.3 Sentiment Analysis Using Machine Learning Techniques
9.4 Conclusion
References
10. Opinion Mining Using Classification Techniques on Electronic Media Data
Meenakshi
10.1 Introduction
10.2 Opinion Mining
10.3 Related Work
10.4 Opinion Mining Techniques
10.4.1 Naïve Bayes
10.4.2 Support Vector Machine
10.4.3 Decision Tree
10.4.4 Multiple Linear Regression
10.4.5 Multilayer Perceptron
10.4.6 Convolutional Neural Network
10.4.7 Long-Short Term Memory Network
10.5 Conclusion
References
11. Spam Content Filtering in Online Social Networks
Meenakshi
11.1 Introduction
11.1.1 E-Mail Spam
11.2 E-Mail Spam Identification Methods
11.2.1 Content-Based Spam Identification Method
11.2.2 Identity-Based Spam Identification Method
11.3 Online Social Network Spam
11.4 Related Work
11.5 Challenges in the Spam Message Identification
11.6 Spam Classification with SVM Filter
11.7 Conclusion
References
12. An Investigation of Various Techniques to Improve Cyber Security
Shoaib Mohammad, Ramendra Pratap Singh, Rajiv Kumar, Kshitij Kumar Rai, Arti Sharma and Saloni Rathore
12.1 Introduction
12.2 Various Attacks
12.3 Methods
12.4 Conclusion
References
13. Brain Tumor Classification and Detection Using Machine Learning by Analyzing MRI Images
Chandrima Sinha Roy, K. Parvathavarthini, M. Gomathi, Mrunal Pravinkumar Fatangare, D. Kishore and Anilkumar Suthar
13.1 Introduction
13.2 Literature Survey
13.3 Methods
13.4 Result Analysis
13.5 Conclusion
References
14. Optimized Machine Learning Techniques for Software Fault Prediction
Chetan Shelke, Ashwini Mandale (Jadhav), Shaik Anjimoon, Asha V., Ginni Nijhawan and Joshuva Arockia Dhanraj
14.1 Introduction
14.2 Literature Survey
14.3 Methods
14.4 Result Analysis
14.5 Conclusion
References
15. Pancreatic Cancer Detection Using Machine Learning and Image Processing
Shashidhar Sonnad, Rejwan Bin Sulaiman, Amer Kareem, S. Shalini, D. Kishore and Jayasankar Narayanan
15.1 Introduction
15.2 Literature Survey
15.3 Methodology
15.4 Result Analysis
15.5 Conclusion
References
16. An Investigation of Various Text Mining Techniques
Rajashree Gadhave, Anita Chaudhari, B. Ramesh, Vijilius Helena Raj, H. Pal Thethi and A. Ravitheja
16.1 Introduction
16.2 Related Work
16.3 Classification Techniques for Text Mining
16.3.1 Machine Learning Based Text Classification
16.3.2 Ontologybased Text Classification
16.3.3 Hybrid Approaches
16.4 Conclusion
References
17. Automated Query Processing Using Natural Language Processing
Divyanshu Sinha, G. Ravivarman, B. Rajalakshmi, V. Alekhya, Rajeev Sobti and R. Udhayakumar
17.1 Introduction
17.1.1 Natural Language Processing
17.2 The Challenges of NLP
17.3 Related Work
17.4 Natural Language Interfaces Systems
17.5 Conclusion
References
18. Data Mining Techniques for Web Usage Mining
Navdeep Kumar Chopra, Chinnem Rama Mohan, Snehal Dipak Chaudhary, Manisha Kasar, Trupti Suryawanshi and Shikha Dubey
18.1 Introduction
18.1.1 Web Usage Mining
18.2 Web Mining
18.2.1 Web Content Mining
18.2.2 Web Structure Mining
18.2.3 Web Usage Mining
18.2.3.1 Preprocessing
18.2.3.2 Pattern Discovery
18.2.3.3 Pattern Analysis
18.3 Web Usage Data Mining Techniques
18.4 Conclusion
References
19. Natural Language Processing Using Soft Computing
M. Rajkumar, Viswanathasarma Ch, Anandhi R. J., D. Anandhasilambarasan, Om Prakash Yadav and Joshuva Arockia Dhanraj
19.1 Introduction
19.2 Related Work
19.3 NLP Soft Computing Approaches
19.4 Conclusion
References
20. Sentiment Analysis Using Natural Language Processing
Brijesh Goswami, Nidhi Bhavsar, Soleman Awad Alzobidy, B. Lavanya, R. Udhayakumar and Rajapandian K.
20.1 Introduction
20.2 Sentiment Analysis Levels
20.2.1 Document Level
20.2.2 Sentence Level
20.2.3 Aspect Level
20.3 Challenges in Sentiment Analysis
20.4 Related Work
20.5 Machine Learning Techniques for Sentiment Analysis
20.6 Conclusion
References
21. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data
C. V. Guru Rao, Nagendra Prasad Krishnam, Akula Rajitha, Anandhi R. J., Atul Singla and Joshuva Arockia Dhanraj
21.1 Introduction
21.2 Web Mining
21.3 Taxonomy of Web Data Mining
21.3.1 Web Usage Mining
21.3.2 Web Structure Mining
21.3.3 Web Content Mining
21.4 Web Content Mining Methods
21.4.1 Unstructured Text Data Mining
21.4.2 Structured Data Mining
21.4.3 Semi-Structured Data Mining
21.5 Efficient Algorithms for Web Data Extraction
21.6 Machine Learning Based Web Content Extraction Methods
21.7 Conclusion
References
22. Intelligent Pattern Discovery Using Web Data Mining
Vidyapati Jha, Chinnem Rama Mohan, T. Sampath Kumar, Anandhi R.J., Bhimasen Moharana and P. Pavankumar
22.1 Introduction
22.2 Pattern Discovery from Web Server Logs
22.2.1 Subsequently Accessed Interesting Page Categories
22.2.2 Subsequent Probable Page of Visit
22.2.3 Strongly and Weakly Linked Web Pages
22.2.4 User Groups
22.2.5 Fraudulent and Genuine Sessions
22.2.6 Web Traffic Behavior
22.2.7 Purchase Preference of Customers
22.3 Data Mining Techniques for Web Server Log Analysis
22.4 Graph Theory Techniques for Analysis of Web Server Logs
22.5 Conclusion
References
23. A Review of Security Features in Prominent Cloud Service Providers
Abhishek Mishra, Abhishek Sharma, Rajat Bhardwaj, Romil Rawat, T.M.Thiyagu and Hitesh Rawat
23.1 Introduction
23.2 Cloud Computing Overview
23.3 Cloud Computing Model
23.4 Challenges with Cloud Security and Potential Solutions
23.5 Comparative Analysis
23.6 Conclusion
References
24. Prioritization of Security Vulnerabilities under Cloud Infrastructure Using AHP
Abhishek Sharma and Umesh Kumar Singh
24.1 Introduction
24.2 Related Work
24.3 Proposed Method
24.4 Result & Discussion
24.5 Conclusion
References
25. Cloud Computing Security Through Detection & Mitigation of Zero-Day Attack Using Machine Learning Techniques
Abhishek Sharma and Umesh Kumar Singh
25.1 Introduction
25.2 Related Work
25.2.1 Analysis of Zero-Day Exploits and Traditional Methods
25.3 Proposed Methodology
25.4 Results & Discussion
25.4.1 Prevention & Mitigation of Zero Day Attacks (ZDAs)
25.5 Conclusion & Future Work
References
26. Predicting Rumors Spread Using Textual and Social Context in Propagation Graph with Graph Neural Network
Siddharath Kumar Arjaria, Hardik Sachan, Satyam Dubey, Ayush Pandey, Mansi Gautam, Nikita Gupta and Abhishek Singh Rathore
26.1 Introduction
26.2 Literature Review
26.3 Proposed Methodology
26.3.1 Tweep Tendency Encoding
26.3.2 Network Dynamics Extraction
26.3.3 Extracted Information Integration
26.4 Results and Discussion
26.5 Conclusion
References
27. Implications, Opportunities, and Challenges of Blockchain in Natural Language Processing
Neha Agrawal, Balwinder Kaur Dhaliwal, Shilpa Sharma, Neha Yadav and Ranjana Sikarwar
27.1 Introduction
27.2 Related Work
27.3 Overview on Blockchain Technology and NLP
27.3.1 Blockchain Technology, Features, and Applications
27.3.2 Natural Language Processing
27.3.3 Challenges in NLP
27.3.4 Data Integration and Accuracy in NLP
27.4 Integration of Blockchain into NLP
27.5 Applications of Blockchain in NLP
27.6 Blockchain Solutions for NLP
27.7 Implications of Blockchain Development Solutions in NLP
27.8 Sectors That can be Benified from Blockchain and NLP Integration
27.9 Challenges
27.10 Conclusion
References
28. Emotion Detection Using Natural Language Processing by Text Classification
Jyoti Jayal, Vijay Kumar, Paramita Sarkar and Sudipta Kumar Dutta
28.1 Introduction
28.2 Natural Language Processing
28.3 Emotion Recognition
28.4 Related Work
28.4.1 Emotion Detection Using Machine Learning
28.4.2 Emotion Detection Using Deep Learning
28.4.3 Emotion Detection Using Ensemble Learning
28.5 Machine Learning Techniques for Emotion Detection
28.6 Conclusion
References
29. Alzheimer Disease Detection Using Machine Learning Techniques
M. Prabavathy, Paramita Sarkar, Abhrendu Bhattacharya and Anil Kumar Behera
29.1 Introduction
29.2 Machine Learning Techniques to Detect Alzheimer’s Disease
29.3 Pre-Processing Techniques for Alzheimer’s Disease Detection
29.4 Feature Extraction Techniques for Alzheimer’s Disease Detection
29.5 Feature Selection Techniques for Diagnosis of Alzheimer’s Disease
29.6 Machine Learning Models Used for Alzheimer’s Disease Detection
29.7 Conclusion
References
30. Netnographic Literature Review and Research Methodology for Maritime Business and Potential Cyber Threats
Hitesh Rawat, Anjali Rawat and Romil Rawat
30.1 Introduction
30.2 Criminal Flows Framework
30.3 Oceanic Crime Exchange and Categorization
30.4 Fisheries Crimes and Mobility Crimes
30.5 Conclusion
30.6 Discussion
References
31. Review of Research Methodology and IT for Business and Threat Management
Hitesh Rawat, Anjali Rawat, Sunday Adeola Ajagbe and Yagyanath Rimal
Abbreviation Used
31.1 Introduction
31.2 Conclusion
References
Index

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