An Expert-Guided Method for Developer Compatibility Graphs and Bayesian Team-Fit Evaluation

Abstract

[Context]. Forming effective software teams remains challenging, particularly in large organizations managing multiple concurrent projects. Existing approaches to modeling developer compatibility often rely on subjective personality traits, purely structural proxies, or large volumes of historical interaction data, limiting both their interpretability and their alignment with how staffing decisions are made in practice. [Objective]. This paper proposes a lightweight, expert-guided method for modeling developer compatibility and operationalizing collaboration evidence for transparent and explainable team-fit evaluation. [Method]. We introduce a regression-calibrated Pair Compatibility (PC) model that combines two project-level indicators, Objective Success Factor (OSF) and Subjective Leadership Factor (SLF), with an expert-defined saturation effect for repeated collaborations. During multiple expert workshops, the expert validated the relevance and conceptual interpretation of OSF and SLF, their normalized scales, and their use in compatibility modeling. The model is calibrated using eight synthetic collaboration scenarios labeled by the expert and is then applied to an organizational analysis base parameterized from anonymized project-participation records and project-level OSF/SLF indicators, thereby producing a weighted developer compatibility graph. Pairwise compatibility evidence is then transformed into a size-agnostic collaboration profile and integrated into an expert-calibrated Bayesian Network (BN) that estimates overall Team Fit. [Results]. In an industrial organization with more than 300 professionals, the calibrated PC regression achieved a Mean Absolute Error (MAE) of 0.0812 and a Pearson correlation of 0.80 with expert expectations on the eight labeled scenarios. When applied to the organizational dataset of 192 developers, and after the PC > 0.3 filter, 773 valid developer pairs were retained; the resulting compatibility graph illustrates how the method produces cohesive collaboration clusters and identifies bridge developers to support managerial staffing decisions. The BN-based team evaluator produced Brier scores of 0.0788 and 0.0782 for Team Fit (AE) in scenario-based validation, reflecting the concentrated posterior distributions of the calibrated model (σ = 0.05) relative to the smoother expert-provided reference distributions. For Collaboration Fit (AC), the PC-tier distribution representation ranked collaboration quality in closer agreement with expert judgment than a mean-based encoding (Spearman ρ = 0.882 vs. ρ = 0.812). [Conclusion]. By combining expert-validated indicator definitions, organizational co-participation records, project-level OSF/SLF indicators, and a small set of synthetic expert-labeled scenarios, the proposed method yields an interpretable compatibility graph and a transparent probabilistic evaluator that bridges pairwise collaboration evidence and team-level decision-making. The results support the feasibility of expert-guided regression and Bayesian reasoning as a practical and explainable alternative to data-intensive black-box models for software team formation (STF), particularly in settings where interpretability and alignment with local staffing authority are central requirements.