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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Navigating the Unfair Running Race: A Quantitative Analysis of Career Success Factors in Software Engineering Using Open Datasets

Authors: Fabiana, Mendes; Fortunato, Júlia; Ribeiro Soares, Luana; Canedo, Edna Dias;

Navigating the Unfair Running Race: A Quantitative Analysis of Career Success Factors in Software Engineering Using Open Datasets

Abstract

Context. Gender diversity in software development teams impacts software quality and team climate; however, women are still underrepresented. In the competitive landscape of software engineering careers, gender disparities often resemble an unequal running race in which women face systemic obstacles that hinder their progression and recognition. To address this imbalance, we need to attract more women to the field and ensure they remain in these roles. One key aspect of retaining women in software development teams is their perception of career success, which can be influenced by numerous factors.Objective. This study aims to understand factors that influence career success in Software engineering, with a particular focus on the subjective perception of career success and how gender impacts it. Research Method. We employed a quantitative approach using open and publicly available datasets produced by other researchers. We began with a literature review to develop a theoretical model and formulate hypotheses. Afterward, we selected appropriate datasets and tested our hypotheses. Results. We developed a model that includes career progress satisfaction (the dependent variable) and three categories of independent variables: human capital (experience, education), socio-demographic status (gender, age), and organizational environment (no organizational difficulties). We confirmed all proposed hypotheses, demonstrating that gender directly affects our dependent variable and moderates all proposed relationships.Conclusions. This research presents a model that companies and individuals can utilize to define strategies for career progress. Additionally, the model can assist researchers in determining the focus of their investigations. We also provide an example of effectively reusing a publicly available dataset.

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Keywords

Gender Diversity, Career Success, Software Engineering

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average