Constructing Developer Compatibility Graphs for Team Formation: An Industrial Case Study - Cunha, Felipe; Perkusich, Mirko; Albuquerque, Danyllo; Dantas Filho, Emanuel; Gorgônio, Kyller; Perkusich, Angelo (Anais do Workshop Brasileiro de Engenharia de Software Inteligente (ISE); 2025: Anais do IV Workshop Brasileiro de Engenharia de Software Inteligente, 2025)
[Context]. Forming effective software teams remains difficult, especially in large organizations that juggle many parallel projects; existing compatibility models either rely on subjective personality traits or demand extensive historical data. [Objective] We introduce a lightweight, hybrid method that fuses minimal project metrics with expert judgment to build a weighted compatibility graph of developers. [Method] In an industrial case study (300+ professionals), a senior technical leader labeled eight synthetic collaboration scenarios that span the space of Objective Success Factor (OSF) and Subjective Leadership Factor (SLF). Those labels calibrated an ordinary-least-squares regression that also includes a saturation term for repeated pairings; the model was then applied to the organization’s project history. [Results] The regression achieved a Mean Absolute Error (MAE) of 0.11 and a Pearson correlation of 0.86 against the expert ground truth. When rolled out to real data (62 developers, 147 valid pairs), it produced PC weights ranging from 0.12 to 0.93, uncovered four cohesive clusters, and highlighted several bridge developers—information that managers subsequently used to re-balance squads. [Conclusion] By combining three readily available project metrics with a small set of expert-labeled scenarios, our method yields a transparent compatibility graph that matches the utility of data-hungry black-box models. It equips managers with an out-of-the-box aid for data-driven team design and provides researchers with a replicable blueprint for analyzing collaboration in data-constrained environments.