# The standard coder: a machine learning approach to measuring the effort required to produce source code change

## TL;DR

This paper introduces a machine learning model called the 'standard coder' that quantifies the effort involved in producing source code changes by analyzing version control data and labor hours, providing a more nuanced measure than traditional metrics.

## Contribution

It presents a novel data-driven approach to measure coding effort using machine learning trained on developer activity and effort data, replacing traditional effort estimation methods.

## Key findings

- The standard coder effectively quantifies effort for code changes.
- It offers insights into effort variability across different types of code modifications.
- The approach outperforms traditional metrics like lines-of-code in estimating effort.

## Abstract

We apply machine learning to version control data to measure the quantity of effort required to produce source code changes. We construct a model of a `standard coder' trained from examples of code changes produced by actual software developers together with the labor time they supplied. The effort of a code change is then defined as the labor hours supplied by the standard coder to produce that change. We therefore reduce heterogeneous, structured code changes to a scalar measure of effort derived from large quantities of empirical data on the coding behavior of software developers. The standard coder replaces traditional metrics, such as lines-of-code or function point analysis, and yields new insights into what code changes require more or less effort.