Natural Language Processing for Software Engineering

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 today’s rapidly changing technological landscape. Agile development enhances business responsiveness through continuous software delivery, emphasizing iterative methodologies that produce incremental, usable software. Working software is the main measure of progress, and ongoing customer collaboration is essential. Approaches like Scrum, eXtreme Programming (XP), and Crystal share these principles but differ in focus: Scrum reduces documentation, XP improves software quality and adaptability to changing requirements, and Crystal emphasizes people and interactions while retaining key artifacts. Modifying software systems designed with Object-Oriented Analysis and Design can be costly and time-consuming in rapidly changing environments requiring frequent updates. This book explores how natural language processing can enhance agile methodologies, particularly in requirements engineering. It introduces tools that help developers create, organize, and update documentation throughout the agile project process.

Table of Contents:
Preface xvii 1 Machine Learning and Artificial Intelligence for Detecting Cyber Security Threats in IoT Environmment 1 Ravindra Bhardwaj, Sreenivasulu Gogula, Bidisha Bhabani, K. Kanagalakshmi, Aparajita Mukherjee and D. Vetrithangam 1.1 Introduction 2 1.2 Need of Vulnerability Identification 4 1.3 Vulnerabilities in IoT Web Applications 5 1.4 Intrusion Detection System 7 1.5 Machine Learning in Intrusion Detection System 10 1.6 Conclusion 12 References 12 2 Frequent Pattern Mining Using Artificial Intelligence and Machine Learning 15 R. Deepika, Sreenivasulu Gogula, K. Kanagalakshmi, Anshu Mehta, S. J. Vivekanandan and D. Vetrithangam 2.1 Introduction 16 2.2 Data Mining Functions 17 2.3 Related Work 19 2.4 Machine Learning for Frequent Pattern Mining 24 2.5 Conclusion 26 References 26 3 Classification and Detection of Prostate Cancer Using Machine Learning Techniques 29 D. Vetrithangam, Pramod Kumar, Shaik Munawar, Rituparna Biswas, Deependra Pandey and Amar Choudhary 3.1 Introduction 30 3.2 Literature Survey 32 3.3 Machine Learning for Prostate Cancer Classification and Detection 35 3.4 Conclusion 37 References 38 4 NLP-Based Spellchecker and Grammar Checker for Indic Languages 43 Brijesh Kumar Y. Panchal and Apurva Shah 4.1 Introduction 44 4.2 NLP-Based Techniques of Spellcheckers and Grammar Checkers 44 4.2.1 Syntax-Based 44 4.2.2 Statistics-Based 45 4.2.3 Rule-Based 45 4.2.4 Deep Learning-Based 45 4.2.5 Machine Learning-Based 46 4.2.6 Reinforcement Learning-Based 46 4.3 Grammar Checker Related Work 47 4.4 Spellchecker Related Work 58 4.5 Conclusion 66 References 67 5 Identification of Gujarati Ghazal Chanda with Cross-Platform Application 71 Brijeshkumar Y. Panchal Abbreviations 72 5.1 Introduction 72 5.1.1 The Gujarati Language 72 5.2 Ghazal 75 5.3 History and Grammar of Ghazal 77 5.4 Literature Review 78 5.5 Proposed System 85 5.6 Conclusion 92 References 92 6 Cancer Classification and Detection Using Machine Learning Techniques 95 Syed Jahangir Badashah, Afaque Alam, Malik Jawarneh, Tejashree Tejpal Moharekar, Venkatesan Hariram, Galiveeti Poornima and Ashish Jain 6.1 Introduction 96 6.2 Machine Learning Techniques 97 6.3 Review of Machine Learning for Cancer Detection 101 6.4 Methods 103 6.5 Result Analysis 106 6.6 Conclusion 107 References 108 7 Text Mining Techniques and Natural Language Processing 113 Tzu-Chia Chen 7.1 Introduction 113 7.2 Text Classification and Text Clustering 115 7.3 Related Work 116 7.4 Methodology 121 7.5 Conclusion 123 References 123 8 An Investigation of Techniques to Encounter Security Issues Related to Mobile Applications 127 Devabalan Pounraj, Pankaj Goel, Meenakshi, Domenic T. Sanchez, Parashuram Shankar Vadar, Rafael D. Sanchez and Malik Jawarneh 8.1 Introduction 128 8.2 Literature Review 130 8.3 Results and Discussions 137 8.4 Conclusion 138 References 139 9 Machine Learning for Sentiment Analysis Using Social Media Scrapped Data 143 Galiveeti Poornima, Meenakshi, Malik Jawarneh, A. Shobana, K.P. Yuvaraj, Urmila R. Pol and Tejashree Tejpal Moharekar 9.1 Introduction 144 9.2 Twitter Sentiment Analysis 146 9.3 Sentiment Analysis Using Machine Learning Techniques 149 9.4 Conclusion 152 References 152 10 Opinion Mining Using Classification Techniques on Electronic Media Data 155 Meenakshi 10.1 Introduction 156 10.2 Opinion Mining 158 10.3 Related Work 159 10.4 Opinion Mining Techniques 161 10.4.1 Naïve Bayes 162 10.4.2 Support Vector Machine 162 10.4.3 Decision Tree 163 10.4.4 Multiple Linear Regression 163 10.4.5 Multilayer Perceptron 164 10.4.6 Convolutional Neural Network 164 10.4.7 Long Short-Term Memory 165 10.5 Conclusion 166 References 166 11 Spam Content Filtering in Online Social Networks 169 Meenakshi 11.1 Introduction 169 11.1.1 E-Mail Spam 170 11.2 E-Mail Spam Identification Methods 171 11.2.1 Content-Based Spam Identification Method 171 11.2.2 Identity-Based Spam Identification Method 172 11.3 Online Social Network Spam 172 11.4 Related Work 173 11.5 Challenges in the Spam Message Identification 177 11.6 Spam Classification with SVM Filter 178 11.7 Conclusion 179 References 180 12 An Investigation of Various Techniques to Improve Cyber Security 183 Shoaib Mohammad, Ramendra Pratap Singh, Rajiv Kumar, Kshitij Kumar Rai, Arti Sharma and Saloni Rathore 12.1 Introduction 184 12.2 Various Attacks 185 12.3 Methods 189 12.4 Conclusion 190 References 191 13 Brain Tumor Classification and Detection Using Machine Learning by Analyzing MRI Images 193 Chandrima Sinha Roy, K. Parvathavarthini, M. Gomathi, Mrunal Pravinkumar Fatangare, D. Kishore and Anilkumar Suthar 13.1 Introduction 194 13.2 Literature Survey 197 13.3 Methods 200 13.4 Result Analysis 202 13.5 Conclusion 203 References 203 14 Optimized Machine Learning Techniques for Software Fault Prediction 207 Chetan Shelke, Ashwini Mandale (Jadhav), Shaik Anjimoon, Asha V., Ginni Nijhawan and Joshuva Arockia Dhanraj 14.1 Introduction 208 14.2 Literature Survey 211 14.3 Methods 214 14.4 Result Analysis 216 14.5 Conclusion 216 References 217 15 Pancreatic Cancer Detection Using Machine Learning and Image Processing 221 Shashidhar Sonnad, Rejwan Bin Sulaiman, Amer Kareem, S. Shalini, D. Kishore and Jayasankar Narayanan 15.1 Introduction 222 15.2 Literature Survey 225 15.3 Methodology 227 15.4 Result Analysis 228 15.5 Conclusion 228 References 229 16 An Investigation of Various Text Mining Techniques 233 Rajashree Gadhave, Anita Chaudhari, B. Ramesh, Vijilius Helena Raj, H. Pal Thethi and A. Ravitheja 16.1 Introduction 234 16.2 Related Work 236 16.3 Classification Techniques for Text Mining 240 16.3.1 Machine Learning Based Text Classification 240 16.3.2 Ontology-Based Text Classification 241 16.3.3 Hybrid Approaches 241 16.4 Conclusion 241 References 241 17 Automated Query Processing Using Natural Language Processing 245 Divyanshu Sinha, G. Ravivarman, B. Rajalakshmi, V. Alekhya, Rajeev Sobti and R. Udhayakumar 17.1 Introduction 246 17.1.1 Natural Language Processing 246 17.2 The Challenges of NLP 248 17.3 Related Work 249 17.4 Natural Language Interfaces Systems 253 17.5 Conclusion 255 References 256 18 Data Mining Techniques for Web Usage Mining 259 Navdeep Kumar Chopra, Chinnem Rama Mohan, Snehal Dipak Chaudhary, Manisha Kasar, Trupti Suryawanshi and Shikha Dubey 18.1 Introduction 260 18.1.1 Web Usage Mining 260 18.2 Web Mining 263 18.2.1 Web Content Mining 264 18.2.2 Web Structure Mining 264 18.2.3 Web Usage Mining 265 18.2.3.1 Preprocessing 265 18.2.3.2 Pattern Discovery 265 18.2.3.3 Pattern Analysis 266 18.3 Web Usage Data Mining Techniques 266 18.4 Conclusion 268 References 269 19 Natural Language Processing Using Soft Computing 271 M. Rajkumar, Viswanathasarma Ch, Anandhi R. J., D. Anandhasilambarasan, Om Prakash Yadav and Joshuva Arockia Dhanraj 19.1 Introduction 272 19.2 Related Work 273 19.3 NLP Soft Computing Approaches 276 19.4 Conclusion 279 References 279 20 Sentiment Analysis Using Natural Language Processing 283 Brijesh Goswami, Nidhi Bhavsar, Soleman Awad Alzobidy, B. Lavanya, R. Udhayakumar and Rajapandian K. 20.1 Introduction 284 20.2 Sentiment Analysis Levels 285 20.2.1 Document Level 285 20.2.2 Sentence Level 285 20.2.3 Aspect Level 286 20.3 Challenges in Sentiment Analysis 286 20.4 Related Work 288 20.5 Machine Learning Techniques for Sentiment Analysis 290 20.6 Conclusion 292 References 292 21 Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data 295 C. V. Guru Rao, Nagendra Prasad Krishnam, Akula Rajitha, Anandhi R. J., Atul Singla and Joshuva Arockia Dhanraj 21.1 Introduction 296 21.2 Web Mining 298 21.3 Taxonomy of Web Data Mining 299 21.3.1 Web Usage Mining 300 21.3.2 Web Structure Mining 301 21.3.3 Web Content Mining 301 21.4 Web Content Mining Methods 302 21.4.1 Unstructured Text Data Mining 302 21.4.2 Structured Data Mining 303 21.4.3 Semi-Structured Data Mining 303 21.5 Efficient Algorithms for Web Data Extraction 304 21.6 Machine Learning Based Web Content Extraction Methods 305 21.7 Conclusion 307 References 307 22 Intelligent Pattern Discovery Using Web Data Mining 311 Vidyapati Jha, Chinnem Rama Mohan, T. Sampath Kumar, Anandhi R.J., Bhimasen Moharana and P. Pavankumar 22.1 Introduction 312 22.2 Pattern Discovery from Web Server Logs 313 22.2.1 Subsequently Accessed Interesting Page Categories 314 22.2.2 Subsequent Probable Page of Visit 314 22.2.3 Strongly and Weakly Linked Web Pages 314 22.2.4 User Groups 315 22.2.5 Fraudulent and Genuine Sessions 315 22.2.6 Web Traffic Behavior 315 22.2.7 Purchase Preference of Customers 315 22.3 Data Mining Techniques for Web Server Log Analysis 316 22.4 Graph Theory Techniques for Analysis of Web Server Logs 318 22.5 Conclusion 319 References 320 23 A Review of Security Features in Prominent Cloud Service Providers 323 Abhishek Mishra, Abhishek Sharma, Rajat Bhardwaj, Romil Rawat, T.M.Thiyagu and Hitesh Rawat 23.1 Introduction 324 23.2 Cloud Computing Overview 324 23.3 Cloud Computing Model 326 23.4 Challenges with Cloud Security and Potential Solutions 327 23.5 Comparative Analysis 332 23.6 Conclusion 332 References 332 24 Prioritization of Security Vulnerabilities under Cloud Infrastructure Using AHP 335 Abhishek Sharma and Umesh Kumar Singh 24.1 Introduction 336 24.2 Related Work 338 24.3 Proposed Method 341 24.4 Result and Discussion 346 24.5 Conclusion 352 References 352 25 Cloud Computing Security Through Detection & Mitigation of Zero-Day Attack Using Machine Learning Techniques 357 Abhishek Sharma and Umesh Kumar Singh 25.1 Introduction 358 25.2 Related Work 360 25.2.1 Analysis of Zero-Day Exploits and Traditional Methods 364 25.3 Proposed Methodology 367 25.4 Results and Discussion 376 25.4.1 Prevention & Mitigation of Zero Day Attacks (ZDAs) 381 25.5 Conclusion and Future Work 383 References 384 26 Predicting Rumors Spread Using Textual and Social Context in Propagation Graph with Graph Neural Network 389 Siddharath Kumar Arjaria, Hardik Sachan, Satyam Dubey, Ayush Pandey, Mansi Gautam, Nikita Gupta and Abhishek Singh Rathore 26.1 Introduction 390 26.2 Literature Review 391 26.3 Proposed Methodology 393 26.3.1 Tweep Tendency Encoding 394 26.3.2 Network Dynamics Extraction 395 26.3.3 Extracted Information Integration 396 26.4 Results and Discussion 398 26.5 Conclusion 399 References 400 27 Implications, Opportunities, and Challenges of Blockchain in Natural Language Processing 403 Neha Agrawal, Balwinder Kaur Dhaliwal, Shilpa Sharma, Neha Yadav and Ranjana Sikarwar 27.1 Introduction 404 27.2 Related Work 406 27.3 Overview on Blockchain Technology and NLP 409 27.3.1 Blockchain Technology, Features, and Applications 409 27.3.2 Natural Language Processing 410 27.3.3 Challenges in NLP 411 27.3.4 Data Integration and Accuracy in NLP 411 27.4 Integration of Blockchain into NLP 412 27.5 Applications of Blockchain in NLP 414 27.6 Blockchain Solutions for NLP 417 27.7 Implications of Blockchain Development Solutions in NLP 418 27.8 Sectors That can be Benified from Blockchain and NLP Integration 419 27.9 Challenges 420 27.10 Conclusion 422 References 422 28 Emotion Detection Using Natural Language Processing by Text Classification 425 Jyoti Jayal, Vijay Kumar, Paramita Sarkar and Sudipta Kumar Dutta 28.1 Introduction 426 28.2 Natural Language Processing 427 28.3 Emotion Recognition 429 28.4 Related Work 430 28.4.1 Emotion Detection Using Machine Learning 430 28.4.2 Emotion Detection Using Deep Learning 432 28.4.3 Emotion Detection Using Ensemble Learning 435 28.5 Machine Learning Techniques for Emotion Detection 437 28.6 Conclusion 439 References 439 29 Alzheimer Disease Detection Using Machine Learning Techniques 443 M. Prabavathy, Paramita Sarkar, Abhrendu Bhattacharya and Anil Kumar Behera 29.1 Introduction 444 29.2 Machine Learning Techniques to Detect Alzheimer’s Disease 445 29.3 Pre-Processing Techniques for Alzheimer’s Disease Detection 446 29.4 Feature Extraction Techniques for Alzheimer’s Disease Detection 448 29.5 Feature Selection Techniques for Diagnosis of Alzheimer’s Disease 449 29.6 Machine Learning Models Used for Alzheimer’s Disease Detection 451 29.7 Conclusion 453 References 454 30 Netnographic Literature Review and Research Methodology for Maritime Business and Potential Cyber Threats 457 Hitesh Rawat, Anjali Rawat and Romil Rawat 30.1 Introduction 458 30.2 Criminal Flows Framework 460 30.3 Oceanic Crime Exchange and Categorization 462 30.4 Fisheries Crimes and Mobility Crimes 469 30.5 Conclusion 470 30.6 Discussion 470 References 470 31 Review of Research Methodology and IT for Business and Threat Management 475 Hitesh Rawat, Anjali Rawat, Sunday Adeola Ajagbe and Yagyanath Rimal Abbreviation Used 476 31.1 Introduction 477 31.2 Conclusion 484 References 485 About the Editors 487 Index 489

About the Author :
Rajesh Kumar Chakrawarti, PhD, is a dean and professor in the Department of Computer Science and Engineering at Sushila Devi Bansal College, Bansal Group of Institutions, India. He has over 20 years of professional experience in academia and industry. Additionally, he 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 completed 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 advancing information and communication technology to enhance and optimize worldwide research and information dissemination, leading 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 at Medicaps University, Indore (M.P.), India. She focuses 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 passion for research and teaching. Her approach centers on encouraging creativity and motivating students to strive for technological excellence. Krishnan Sakthidasan Sankaran, PhD, is a professor in the Department of Electronics and Communication Engineering at Hindustan Institute of Technology and Science, India. He has been a senior member of the Institute of Electrical and Electronics Engineers for the past ten years and has published more than 70 papers in refereed journals and international conferences. He has also published three books to his credit. His research interests include image processing, wireless networks, cloud computing, and antenna design. Romil Rawat has attended several research programs and received research grants from the United States, Germany, Italy, and the United Kingdom. He has chaired international conferences and hosted several research events, in addition to publishing several research patents. His research interests include cybersecurity, Internet of Things, dark web crime analysis and investigation techniques, and working towards tracing illicit anonymous contents of cyber terrorism and criminal activities.