Identifying Source Code File Experts

TL;DR

This study investigates methods to improve the identification of source code file experts by analyzing development history and applying machine learning, finding comparable performance between linear techniques and classifiers.

Contribution

It explores new variables and machine learning approaches to enhance expert identification accuracy in software projects.

Findings

First authorship correlates positively with source code knowledge.

Recency of modification correlates negatively with source code knowledge.

Machine learning classifiers outperform linear techniques in public datasets.

Abstract

In software development, the identification of source code file experts is an important task. Identifying these experts helps to improve software maintenance and evolution activities, such as developing new features, code reviews, and bug fixes. Although some studies have proposed repository mining techniques to automatically identify source code experts, there are still gaps in this area that can be explored. For example, investigating new variables related to source code knowledge and applying machine learning aiming to improve the performance of techniques to identify source code experts. The goal of this study is to investigate opportunities to improve the performance of existing techniques to recommend source code files experts. We built an oracle by collecting data from the development history and surveying developers of 113 software projects. Then, we use this oracle to: (i) analyze the correlation between measures extracted from the development history and the developers source code knowledge and (ii) investigate the use of machine learning classifiers by evaluating their performance in identifying source code files experts. First Authorship and Recency of Modification are the variables with the highest positive and negative correlations with source code knowledge, respectively. Machine learning classifiers outperformed the linear techniques (F-Measure = 71% to 73%) in the public dataset, but this advantage is not clear in the private dataset, with F-Measure ranging from 55% to 68% for the linear techniques and 58% to 67% for ML techniques. Overall, the linear techniques and the machine learning classifiers achieved similar performance, particularly if we analyze F-Measure. However, machine learning classifiers usually get higher precision while linear techniques obtained the highest recall values.