A Bayesian Network for Evaluating Software Project Team Fit: An Industrial Case Study

[Context] Forming effective software development teams requires balancing both technical and collaborative factors. While optimization methods—such as genetic algorithms—have been applied to automate team formation, they typically rely on fitness functions built from fixed weights or handcrafted rules. These ad hoc formulations rarely reflect structured expert reasoning and may misalign with organizational expectations. [Objective] This study focuses on constructing a Bayesian Network (BN) that formally encodes expert knowledge about team-project fit. The BN is intended to serve as a reusable, interpretable fitness function in future optimizationbased team formation systems. [Method] We conducted a case study in a large software organization with over 300 employees. Through expert workshops, we identified relevant developer capabilities, defined qualitative states, and structured the BN accordingly. Conditional Probability Tables (CPTs) were populated using expertlabeled what-if scenarios, then calibrated using the Ranked Nodes method. [Results] The final BN comprises nodes for technological and collaboration fit. Validation against held-out, expert-labeled scenarios yielded consistently low Brier Scores, demonstrating strong alignment with expert expectations. [Conclusion] BNs offer a viable, interpretable mechanism for encoding expert knowledge in team formation. This work establishes a structured and transparent foundation for future integration into automated team formation workflows.