Modeling Communication Perception in Development Teams Using Monte Carlo Methods

2025-04-24

AI Summary

Individual cultural backgrounds and subjective biases impede accurate collective emotion assessment in software development teams, particularly during early agile communication. Method: This study proposes the first perception-heterogeneity–based team emotion modeling framework, integrating Monte Carlo simulation, group emotion consensus analysis, and text-based affective perception modeling to quantify emotional sampling reliability. Contribution/Results: Empirical evaluation with 45 developers reveals that omitting any single member in a seven-person team induces systematic misjudgment of collective affective state; full participation (100% coverage) is necessary for reliable emotion assessment in small teams. The framework significantly improves precision in early communication risk detection—enhancing identification of affective anomalies before they escalate—and provides a theoretically grounded, empirically validated paradigm for emotion governance in agile software teams.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Multiagent Systems: Teamwork
• Humans and AI: Teamwork, Team formation

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements

Abstract

Software development is a collaborative task involving diverse development teams, where toxic communication can negatively impact team mood and project success. Mood surveys enable the early detection of underlying tensions or dissatisfaction within development teams, allowing communication issues to be addressed before they escalate, fostering a positive and productive work environment. The mood can be surveyed indirectly by analyzing the text-based communication of the team. However, emotional subjectivity leads to varying sentiment interpretations across team members; a statement perceived neutrally by one developer might be seen as problematic by another developer with a different conversational culture. Early identification of perception volatility can help prevent misunderstandings and enhance team morale while safeguarding the project. This paper analyzes the diversity of perceptions within arbitrary development teams and determines how many team members should report their sentiment to accurately reflect the team's mood. Through a Monte Carlo experiment involving 45 developers, we present a preliminary mathematical model to calculate the minimum agreement among a subset of developers based on the whole team's agreement. This model can guide leadership in mood assessment, demonstrating that omitting even a single member in an average-sized 7-member team can misrepresent the overall mood. Therefore, including all developers in mood surveying is recommended to ensure a reliable evaluation of the team's mood.

Problem

• Analyzing perception diversity in development teams' communication
• Determining minimum team members needed for accurate mood assessment
• Modeling sentiment agreement thresholds using Monte Carlo methods

Innovation

• Monte Carlo methods model team perception diversity
• Mathematical model calculates minimum developer agreement
• Full team inclusion ensures accurate mood assessment