Predicting long time contributors with knowledge units of programming languages: an empirical study

TL;DR

This empirical study introduces KULTC, a model leveraging programming language knowledge units to predict long-time contributors in Java projects, outperforming existing models and aiding retention strategies.

Contribution

The paper presents a novel KU-based prediction model for LTCs, demonstrating its effectiveness and superiority over state-of-the-art approaches in Java open-source projects.

Findings

KULTC achieves median AUC of at least 0.75.

KULTC outperforms BAOLTC significantly.

Combining KULTC with BAOLTC improves performance by 16.5%.

Abstract

Predicting potential long-time contributors (LTCs) early allows project maintainers to effectively allocate resources and mentoring to enhance their development and retention. Mapping programming language expertise to developers and characterizing projects in terms of how they use programming languages can help identify developers who are more likely to become LTCs. However, prior studies on predicting LTCs do not consider programming language skills. This paper reports an empirical study on the usage of knowledge units (KUs) of the Java programming language to predict LTCs. A KU is a cohesive set of key capabilities that are offered by one or more building blocks of a given programming language. We build a prediction model called KULTC, which leverages KU-based features along five different dimensions. We detect and analyze KUs from the studied 75 Java projects (353K commits and 168K pull requests) as well as 4,219 other Java projects in which the studied developers previously worked (1.7M commits). We compare the performance of KULTC with the state-of-the-art model, which we call BAOLTC. Even though KULTC focuses exclusively on the programming language perspective, KULTC achieves a median AUC of at least 0.75 and significantly outperforms BAOLTC. Combining the features of KULTC with the features of BAOLTC results in an enhanced model (KULTC+BAOLTC) that significantly outperforms BAOLTC with a normalized AUC improvement of 16.5%. Our feature importance analysis with SHAP reveals that developer expertise in the studied project is the most influential feature dimension for predicting LTCs. Finally, we develop a cost-effective model (KULTC_DEV_EXP+BAOLTC) that significantly outperforms BAOLTC. These encouraging results can be helpful to researchers who wish to further study the developers' engagement/retention to FLOSS projects or build models for predicting LTCs.