Gender Dynamics in Software Engineering: Insights from Research on Concurrency Bug Reproduction
- URL: http://arxiv.org/abs/2502.20289v1
- Date: Thu, 27 Feb 2025 17:15:23 GMT
- Title: Gender Dynamics in Software Engineering: Insights from Research on Concurrency Bug Reproduction
- Authors: Tarannum Shaila Zaman, Macharla Hemanth Kishan, Lutfun Nahar Lota,
- Abstract summary: We present a literature review to assess the gender ratio in this field.<n>Our findings indicate that female researchers are underrepresented compared to their male counterparts in this area.
- Score: 0.5284425534494986
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Reproducing concurrency bugs is a complex task due to their unpredictable behavior. Researchers, regardless of gender, are contributing to automating this complex task to aid software developers. While some studies have investigated gender roles in the broader software industry, limited research exists on gender representation specifically among researchers working in concurrent bug reproduction. To address this gap, in this paper, we present a literature review to assess the gender ratio in this field. We also explore potential variations in technique selection and bug-type focus across genders. Our findings indicate that female researchers are underrepresented compared to their male counterparts in this area, with a current male-to-female author ratio of 29:6. Through this study, we emphasize the importance of fostering gender equity in software engineering research, ensuring a diversity of perspectives in the development of automated bug reproduction tools.
Related papers
- Interact and React: Exploring Gender Patterns in Development and the Impact on Innovation and Robustness of a User Interface Tool [0.0]
This study investigates React, a JavaScript library for building user interfaces with an active contributor community.<n>I examine gender differences in metrics of robustness and innovation, as well as shifts in contribution patterns leading up to major version releases over 11 years of the React project.<n>My results show that the exclusion of women is detrimental to software as women contribute significantly more to feature enhancement and dependency management.
arXiv Detail & Related papers (2025-10-17T13:33:06Z) - Dynamics of Gender Bias in Software Engineering [0.0]
The field of software engineering is embedded in both engineering and computer science, and may embody gender biases endemic to both.<n>This paper surveys software engineering's origins and its long-running attention to engineering professionalism, profiling five leaders.<n>It then examines the field's recent attention to gender issues and gender bias.
arXiv Detail & Related papers (2025-08-28T17:54:49Z) - Are All Genders Equal in the Eyes of Algorithms? -- Analysing Search and Retrieval Algorithms for Algorithmic Gender Fairness [0.1979158763744267]
This paper introduces and applies a bias-preserving definition of algorithmic gender fairness.<n>We analyse gender differences in metadata completeness, publication retrieval in academic databases, and visibility in Google search results.<n>Male professors are associated with a greater number of search results and more aligned publication records, while female professors display higher variability in digital visibility.
arXiv Detail & Related papers (2025-08-05T15:33:01Z) - Gender Disparities in Contributions, Leadership, and Collaboration: An Exploratory Study on Software Systems Research [1.8049331600471712]
We analyzed 2,000 articles published over the past decade in the Journal of Systems and Software.<n>Our analysis showed that only 32.74% of the total authors are women and female-led or supervised studies were fewer than those of men.<n>Third, we explored the areas of software systems research and found that female authors are more actively involved in human-centric research domains.
arXiv Detail & Related papers (2024-12-20T08:20:23Z) - Beyond Binary Gender: Evaluating Gender-Inclusive Machine Translation with Ambiguous Attitude Words [85.48043537327258]
Existing machine translation gender bias evaluations are primarily focused on male and female genders.
This study presents a benchmark AmbGIMT (Gender-Inclusive Machine Translation with Ambiguous attitude words)
We propose a novel process to evaluate gender bias based on the Emotional Attitude Score (EAS), which is used to quantify ambiguous attitude words.
arXiv Detail & Related papers (2024-07-23T08:13:51Z) - Thinking beyond Bias: Analyzing Multifaceted Impacts and Implications of AI on Gendered Labour [1.5839621757142595]
This paper emphasizes the need to explore AIs broader impacts on gendered labor.
We draw attention to how the AI industry as an integral component of the larger economic structure is transforming the nature of work.
arXiv Detail & Related papers (2024-06-23T20:09:53Z) - A multitask learning framework for leveraging subjectivity of annotators to identify misogyny [47.175010006458436]
We propose a multitask learning approach to enhance the performance of the misogyny identification systems.
We incorporated diverse perspectives from annotators in our model design, considering gender and age across six profile groups.
This research advances content moderation and highlights the importance of embracing diverse perspectives to build effective online moderation systems.
arXiv Detail & Related papers (2024-06-22T15:06:08Z) - Paths to Testing: Why Women Enter and Remain in Software Testing? [0.8602553195689511]
This study investigates the motivations behind women choosing careers in software testing.
The findings reveal that women enter software testing due to increased entry-level job opportunities, work-life balance, and even fewer gender stereotypes.
However, inclusiveness and career development in the field need improvement for sustained diversity.
arXiv Detail & Related papers (2024-04-20T20:43:29Z) - The Clever Hans Mirage: A Comprehensive Survey on Spurious Correlations in Machine Learning [78.13481522957552]
Machine learning models are sensitive to spurious correlations between non-essential features of the inputs and the corresponding labels.<n>This paper provides a comprehensive survey of this emerging issue, along with a fine-grained taxonomy of existing state-of-the-art methods for addressing spurious correlations in machine learning models.
arXiv Detail & Related papers (2024-02-20T04:49:34Z) - VisoGender: A dataset for benchmarking gender bias in image-text pronoun
resolution [80.57383975987676]
VisoGender is a novel dataset for benchmarking gender bias in vision-language models.
We focus on occupation-related biases within a hegemonic system of binary gender, inspired by Winograd and Winogender schemas.
We benchmark several state-of-the-art vision-language models and find that they demonstrate bias in resolving binary gender in complex scenes.
arXiv Detail & Related papers (2023-06-21T17:59:51Z) - Gender Bias in Transformer Models: A comprehensive survey [1.1011268090482573]
Gender bias in artificial intelligence (AI) has emerged as a pressing concern with profound implications for individuals' lives.
This paper presents a comprehensive survey that explores gender bias in Transformer models from a linguistic perspective.
arXiv Detail & Related papers (2023-06-18T11:40:47Z) - She Elicits Requirements and He Tests: Software Engineering Gender Bias
in Large Language Models [17.837267486473415]
This study uses data mining techniques to investigate the extent to which software development tasks are affected by implicit gender bias.
We translate each task from English into a genderless language and back, and investigate the pronouns associated with each task.
Specifically, requirements elicitation was associated with the pronoun "he" in only 6% of cases, while testing was associated with "he" in 100% of cases.
arXiv Detail & Related papers (2023-03-17T17:16:53Z) - Towards Understanding Gender-Seniority Compound Bias in Natural Language
Generation [64.65911758042914]
We investigate how seniority impacts the degree of gender bias exhibited in pretrained neural generation models.
Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains.
These results suggest that NLP applications built using GPT-2 may harm women in professional capacities.
arXiv Detail & Related papers (2022-05-19T20:05:02Z) - Are Commercial Face Detection Models as Biased as Academic Models? [64.71318433419636]
We compare academic and commercial face detection systems, specifically examining robustness to noise.
We find that state-of-the-art academic face detection models exhibit demographic disparities in their noise robustness.
We conclude that commercial models are always as biased or more biased than an academic model.
arXiv Detail & Related papers (2022-01-25T02:21:42Z) - Multi-Dimensional Gender Bias Classification [67.65551687580552]
Machine learning models can inadvertently learn socially undesirable patterns when training on gender biased text.
We propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions.
Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
arXiv Detail & Related papers (2020-05-01T21:23:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.