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

Context. Gender diversity in software development teams impacts software quality and team climate; however, women are still underrepresented. 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 can be utilized by companies and individuals 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.