Software Source Code: Statistical Modeling (De Gruyter STEM)

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 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

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.