Document Type : Original Article

Authors

• Omid Mohammadi Kia ¹
• Mahmood Neshati ²

¹ Shahid Beheshti Faculty of Computer Science and Engineering, Shahid Beheshti University:G.C

² Shahid Beheshti Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran

Abstract

Developer expertise is a critical component of community-based question-answering platforms like Stack Overflow. However, expertise is not monolithic. Developers exhibit different "shapes" of expertise, such as deep specialists (I-shaped) or broad generalists with one specialty (T-shaped). While prior research has examined developer expertise and answer quality independently, how different expertise structures influence preferences for specific answer characteristics remains insufficiently understood. This paper investigates the relationship between a developer's expertise shape and their preference for specific answer characteristics.

We present a large-scale empirical study of over 48,000 Stack Overflow users, classifying them into I-shaped, T-shaped, Pi-shaped, and Comb-shaped profiles based on the distribution of tag-level reputation, following established expertise-shape modeling approaches. We then analyze the types of answers these user profiles tend to upvote and accept as solutions, focusing on characteristics such as answer length, inclusion of code snippets, use of images, and citation of external references.

Using separate analyses for community-level (upvotes) and task-resolution-level (accepted answers) preference signals, our findings reveal distinct and systematic differences across expertise shapes. I-shaped specialists favor technically deep, code-heavy answers, while T-shaped and Comb-shaped experts show a preference for more summarized, conceptual answers that include diagrams or references. These patterns are consistent across robustness checks and sensitivity analyses.

The results highlight that answer usefulness is user-dependent rather than universal, and they can help improve expertise-aware answer recommendation systems and foster more effective knowledge sharing on collaborative platforms.

Keywords

• Stack Overflow
• Expertise Shapes
• T-Shaped
• Answer Quality
• Empirical Software Engineering
• Recommender Systems

Main Subjects

• Software and Information Systems