Relationship between Gender and Code Reading Speed in Software Development
September 08, 2022 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yuriko Takatsuka, Yukasa Murakami, Masateru Tsunoda, Masahide Nakamura
arXiv ID
2209.03516
Category
cs.SE: Software Engineering
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
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.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted