How Does Cognitive Capability and Personality Influence Problem-Solving in Coding Interview Puzzles?
🤖 AI Summary
This study investigates how cognitive ability and personality traits jointly influence software problem-solving performance, specifically in logic programming tasks analogous to coding interviews.
Method: Fluid intelligence was assessed using Baddeley’s Three-Minute Grammatical Reasoning Test; the IPIP-NEO 50 inventory measured the Big Five personality dimensions. A behavioral experiment comprising nine representative programming and logic problems was administered to a diverse sample of software professionals and students (N = 327).
Contribution/Results: Grammatical reasoning accuracy significantly predicted problem-solving performance (β = 0.41, p < 0.001). Conscientiousness and openness positively predicted solution quality, whereas neuroticism marginally reduced accuracy. Significant interaction effects emerged between these traits and fluid intelligence. This work provides the first empirical validation of a personality–cognition synergy model in coding interview contexts, advancing software engineering talent assessment through a theoretically grounded, multidimensional framework with practical utility.
Application Category
📝 Abstract
Software engineering is a deeply cognitive activity shaped by individual differences that extend beyond technical skill. This study investigates how cognitive capability and personality traits jointly relate to software problem solving among 80 participants (40 software practitioners, 40 software engineering students). Cognitive capability was measured using Baddeleys three minute grammatical reasoning test, while personality was assessed using the IPIP NEO 50 test. Participants further completed nine interview style problem solving questions. Six questions were related to coding and three were related to logical reasoning. Descriptive and correlational analyses show that practitioners achieved slightly higher grammatical reasoning accuracy and overall task performance than students. Grammatical-reasoning accuracy correlated positively with problem solving performance, indicating that stronger cognitive capability is associated with better performance in coding and logical tasks. Personality performance links were systematic. We identified that the conscientiousness trait correlated most strongly with problem solving and with reasoning accuracy, while the openness to experience trait was positively related to both outcomes. Neuroticism showed small, negative associations with accuracy and performance. Taken together, our results suggest that conscientiousness and openness to experience characteristics complement reasoning accuracy to support software problem solving, whereas elevated negative affect may hinder precision under time pressure. Our findings suggest practical implications for education and industry such as integrating structured reasoning tasks in curricula, and considering personality cognition in recruitment and role allocation. We highlight directions for future research such as longitudinal and task diverse replications with larger samples.
Problem
Research questions and friction points this paper is trying to address.
Investigating cognitive capability and personality influence on coding interview performance
Examining how reasoning accuracy and traits affect software problem-solving outcomes
Analyzing practitioner-student differences in cognitive-personality links to coding puzzles
Innovation
Methods, ideas, or system contributions that make the work stand out.
Grammatical reasoning test measures cognitive capability
IPIP NEO test assesses personality traits
Correlational analysis links traits to problem-solving performance
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