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 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).Comment: Japanese letter version is available at: https://www.jstage.jst.go.jp/article/jssst/35/4/35_37/_article/-char/e

Relationship between Gender and Code Reading Speed in Software Development

Recently, workforce shortage has become a popular issue in information technology (IT). One solution to increasing the workforce supply is to increase the number of female IT professionals. This is because there is gender imbalance in information technology area. To accomplish this, it is important to suppress the influence of biases, such as the belief that men are more suited for careers in science and technology than women, and to increase the choice of careers available to female professionals. To help suppress the influence of gender bias, we analyzed the relationship between gender and code reading speed in the field of software development. Certain source codes require developers to use substantial memory to properly understand them, such as those with many variables that frequently change values. Several studies have indicated that the performance of memory differs in males and females. To test the veracity of this claim, we analyzed the influence of gender on code-reading speed through an experiment. Pursuant to this, we prepared four programs that required varied amounts of memory to properly understand them. Then, we measured the time required by each of the 17 male and 16 female subjects (33 subjects in total) to comprehend the different programs. The results suggest that there is no explicit difference between male and female subjects in this regard, even in the case of programs that require high memory capacities for proper understanding.Comment: Japanese letter version is available at: https://search.ieice.org/bin/summary.php?id=j104-d_5_521&category=D&year=2021&lang=J&abst