MentalGame: Predicting Personality-Job Fitness for Software Developers Using Multi-Genre Games and Machine Learning Approaches
Soroush Elyasi, Arya VarastehNezhad, Fattaneh Taghiyareh
TL;DR
This work demonstrates that software-development suitability can be effectively predicted from implicit behavioral traces captured during a multi-genre serious game, offering a viable alternative to traditional self-report questionnaires. By grounding MBTI-based insights within a broader set of developer-relevant traits and employing a two-phase ML approach, the study shows that gameplay data alone can achieve high predictive accuracy (up to 0.94 overall and 0.97 precision in certain pipelines). The methodology combines robust data collection (two-phase experiments, privacy-conscious logging) with principled feature engineering, dimensionality reduction, and explainable modeling considerations, providing strong evidence for game-based, scalable, and less biased career assessment. Practically, this framework supports early-stage career guidance and candidate screening in software development, while acknowledging limitations related to label validity and domain specificity that warrant further validation and extension to other professions.
Abstract
Personality assessment in career guidance and personnel selection traditionally relies on self-report questionnaires, which are susceptible to response bias, fatigue, and intentional distortion. Game-based assessment offers a promising alternative by capturing implicit behavioral signals during gameplay. This study proposes a multi-genre serious-game framework combined with machine-learning techniques to predict suitability for software development roles. Developer-relevant personality and behavioral traits were identified through a systematic literature review and an empirical study of professional software engineers. A custom mobile game was designed to elicit behaviors related to problem solving, planning, adaptability, persistence, time management, and information seeking. Fine-grained gameplay event data were collected and analyzed using a two-phase modeling strategy where suitability was predicted exclusively from gameplay-derived behavioral features. Results show that our model achieved up to 97% precision and 94% accuracy. Behavioral analysis revealed that proper candidates exhibited distinct gameplay patterns, such as more wins in puzzle-based games, more side challenges, navigating menus more frequently, and exhibiting fewer pauses, retries, and surrender actions. These findings demonstrate that implicit behavioral traces captured during gameplay is promising in predicting software-development suitability without explicit personality testing, supporting serious games as a scalable, engaging, and less biased alternative for career assessment.