Software Source Code : : Statistical Modeling / / Siddhartha Bhattacharyya, Raghavendra Rao Althar, Debabrata Samanta, Debanjan Konar.

• Superior document: Title is part of eBook package: De Gruyter DG Ebook Package English 2021
• VerfasserIn: Rao Althar, Raghavendra,; Bhattacharyya, Siddhartha,; Konar, Debanjan,; Samanta, Debabrata,
• Place / Publishing House: Berlin ;, Boston : : De Gruyter, , [2021]; ©2021
• Year of Publication: 2021
• Language: English
• Series: De Gruyter STEM
• Physical Description: 1 online resource (XVI, 342 p.)
• Other title: Frontmatter --; Preface --; Contents --; Chapter 1. Software development processes evolution --; Chapter 2. A probabilistic model for fault prediction across functional and security aspects --; Chapter 3. Establishing traceability between software development domain artifacts --; Chapter 4. Auto code completion facilitation by structured prediction-based auto-completion model --; Chapter 5. Transfer learning and one-shot learning to address software deployment issues --; Chapter 6. Enabling intelligent IDEs with probabilistic models --; Chapter 7. Natural language processing–based deep learning in the context of statistical modeling for software source code --; Chapter 8. Impact of machine learning in cognitive computing --; Chapter 9. Predicting rainfall with a regression model --; Chapter 10. Understanding programming language structure at its full scale --; References --; Index
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9783110703399; 9783110750720; 9783110750706; 9783110754001; 9783110753776; 9783110754070; 9783110753837
• DOI: 10.1515/9783110703399
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: Siddhartha Bhattacharyya, Raghavendra Rao Althar, Debabrata Samanta, Debanjan Konar.

This book will focus on utilizing statistical modelling of the software source code, in order to resolve issues associated with the software development processes. Writing and maintaining software source code is a costly business; software developers need to constantly rely on large existing code bases. Statistical modelling identifies the patterns in software artifacts and utilize them for predicting the possible issues.