Analyzing and Calibrating Risk Assessment by Software Developers

In software project management, risk management is a critical factor. Project managers use existing lists of risk or perform brainstorming to identify the risks. However, it is not easy to perceive all the risks objectively. As a result, some risks are perceived based on subjective impression, which...

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Bibliographic Details
Published inarXiv.org
Main Authors Murakami, Yukasa, Tsunoda, Masateru, Campos, Eduardo C
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 08.09.2022
Subjects
Calibration
Evaluation
Factor analysis
Mathematical analysis
Mathematical models
Project management
Questionnaires
Reliability analysis
Risk analysis
Risk assessment
Risk management
Risk perception
Software development
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Summary:In software project management, risk management is a critical factor. Project managers use existing lists of risk or perform brainstorming to identify the risks. However, it is not easy to perceive all the risks objectively. As a result, some risks are perceived based on subjective impression, which leads to risk biases. So, our goals are (i) We clarify the risk perception of developers to enhance the reliability of the brainstorming, and (ii) we calibrate the risk assessment based on a mathematical model to make more accurate risk list. In the analysis, we collected data concerning the risk perception of 69 professional software developers via a questionnaire. The average number of years of experience among these professionals was 18.3. Using the dataset, we applied factor analysis to clarify the factors that affect the evaluation of risk impact. The questionnaire was based on the risk perception theory established by Slovic, in which "dread" and "unknown" are the major factor of risk perception. The analysis result shows that (i) risk experience (i.e., whether a developer actually faced the risk or not) sometimes affects risk assessment (evaluation of risk impact), (ii) risk perception is considered to be based on unknown and dread factors, and (iii) risk assessment can be calibrated by a mathematical model (the average absolute error was 0.20).
ISSN:2331-8422