Numerous methods exist to model and analyze the different roles, responsibilities, and process levels of information technology (IT) personnel. However, most methods neglect to account for the rigorous application and evaluation of human errors and their associated risks. This book fills that need.
Modeling, Evaluating, and Predicting IT Human Resources Performance explains why it is essential to account for the human factor when determining the various risks in the software engineering process. The book presents an IT human resources evaluation approach that is rooted in existing research and describes how to enhance existing approaches through strict use of software measurement and statistical principles and criteria.
Discussing IT human factors from a risk assessment point of view, the book identifies, analyzes, and evaluates the basics of IT human performance. It details the IT human factors required to achieve desired levels of human performance prediction. It also provides a rigorous investigation of existing human factors evaluation methods, including IT expertise and Big Five, in combination with powerful statistical methods, such as failure mode and effect analysis (FMEA) and design of experiment (DoE).
- Supplies an overview of existing methods of human risk evaluation
- Provides a detailed analysis of IT role-based human factors using the well-known Big Five method for software engineering
- Models the human factor as a risk factor in the software engineering process
- Summarizes emerging trends and future directions
In addition to applying well-known human factors methods to software engineering, the book presents three models for analyzing psychological characteristics. It supplies profound analysis of human resources within the various software processes, including development, maintenance, and application under consideration of the Capability Maturity Model Integration (CMMI) process level five.
Table of Contents
Structure of the Book
Software Risk Management and Human Factors
Overview of Risk Management Development
Incompleteness of Risk Assessment Methods
Risk Management Summary and Further Research Motivation
Human Factors in Software Engineering
Summary of Human Factors
Software Engineering, Team, and Responsibilities
Software Engineering Background
Summary of Software Engineering and Software Roles
Discovery of IT Human Factors
Classical Failure Mode and Effect Analysis
Adopted FMEA for Software Personnel
Summary of Software Human Factors FMEA
Definition and Evaluation of IT Human Factors
Five Personal Features
Matching Big Five Traits with IT Human Factors
Summary of Definition and Evaluation of IT Human Factors
Model Development for IT Human Performance Prediction
Experimental Design and Analysis
Algorithm for Conducting Experimental Design
Development of the IT Human Performance Prediction Model
Developed Model for IT Human Performance Prediction
Summary of Predictive Model Development
Experimental Validation of Predictive Model for IT Human Performance
Actual Model Application
Software Human Factors Test Web Application
Summary of Experimental Model Validation
Conclusions and Future Directions
Konstantina Richter earned her master’s degree at the University of Magdeburg after receiving her bachelor’s degree at the Technical University of Varna, Bulgaria. Her master’s thesis investigated the testing of aspect-oriented programs and helped to earn her the award for best student of the year in 2007. From 2008 to 2012 she was a PhD student in the software engineering research group at the University of Magdeburg. She has participated in several international conferences on software process, software testing, software measurement, and software quality in Rome, Nara, and Las Vegas, among others. Her PhD thesis on human resources performance is the basis of this book and involved validation in worldwide IT companies including Siemens, Bosch, German Telekom, VW, Alcatel, IBM, and so on. Currently she works in the IT department at the financial government center of Sachsen-Anhalt in Magdeburg.
Reiner R. Dumke is a retired professor from the Otto-von-Guericke-University of Magdeburg with software engineering as his research field. He is one of the founders of the Software Measurement Laboratory ([email protected]) of the computer science department at the University of Magdeburg and coeditor of the Software Measurement News Journal. He is the leader of the German Interest Group on software metrics and works as a member of the COSMIC, DASMA, MAIN, IEEE, and ACM communities. He received a diploma degree (MS) in mathematics in 1970 followed in 1980 by a PhD dissertation in computer science on efficiency of database projects. He is the author and editor of more than 30 books about programming techniques, software metrics, metrics tools, software engineering foundations, component-based software development, and web engineering.