A machine learning examination of women’s leadership effectiveness in software development processes - Ferreira, Sâmara Ahyeska Alves; Lima, Danielli Araújo (Anais do Women in Information Technology (WIT); 2024: Anais do XVIII Women in Information Technology, 2024)
The inclusion of women in information technology companies and software development processes is vital for fostering diverse perspectives and innovative problem-solving. We analyzed 793 instances representing globally distributed software development teams, aiming to show that female-led teams outperform male-led ones. Through descriptive statistics and Welch’s t-test, we confirmed this hypothesis. Using a decision tree with only three inputs—female leadership presence, total team members, and female team members—we achieved 76.79% accuracy, significantly reducing computational time compared to using all 85 dataset attributes. This approach also informs recommendation systems for assembling development teams, emphasizing the value of gender diversity in enhancing team dynamics and solutions in the tech industry.