Software Source Code: Statistical Modeling

✍ Scribed by Raghavendra Rao Althar; Debabrata Samanta; Debanjan Konar; Siddhartha Bhattacharyya

• Publisher: De Gruyter
• Year: 2021
• Tongue: English
• Leaves: 358
• Category: Library

✦ Synopsis

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.

• Statistic tool for the software engineer

✦ Table of Contents

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