Enhancing Task In-Progress Time Predictions through Affective and Personality Factors
Leo Silva, Cephas Alves da Silveira Barreto, Margarida Pedroso de Lima, Henrique Madeira · ACM Transactions on Software Engineering and Methodology · 2025
Software developers’ personality traits, emotional states, and stress levels are crucial in their task performance. This study aims to enhance the prediction of task in-progress time by integrating traditional features, such as developers’ experience and task estimates, with affective states and personality traits. This article reports a long-term empirical study across seven agile projects in four software development companies, applying various machine learning algorithms to assess the predictive power of these combined features, evaluating them primarily through validation accuracy score. Also, we investigated the impact of weighting developers’ affective states based on their personality traits on model performance. Incorporating developers’ affective states and personality traits improved the task in-progress time prediction in 42.59% of classifier, dataset, and scaling combinations, with oversampled combinations achieving up to 8.4% higher validation accuracy than traditional feature models. The innovative weighting strategy improved 31.48% of the combinations. Our best model achieved a validation accuracy of 0.85. These findings suggest that integrating affective and personality data can significantly improve task in-progress time predictions, with significant implications for project planning and task allocation in software development.