Adopting Automated Bug Assignment in Practice -- A Registered Report of an Industrial Case Study

TL;DR

This study evaluates the industrial adoption of an automated bug assignment system at Ericsson, analyzing its performance, value, and impact on workflows through interviews and data analysis.

Contribution

It provides insights into the real-world deployment, performance, and organizational impact of a machine learning-based bug assignment tool in an industrial setting.

Findings

TRR achieved first fully automated bug assignment in 2019

The system improved bug handling efficiency at Ericsson

Lessons learned on productizing research prototypes in industry

Abstract

[Background/Context] The continuous inflow of bug reports is a considerable challenge in large development projects. Inspired by contemporary work on mining software repositories, we designed a prototype bug assignment solution based on machine learning in 2011-2016. The prototype evolved into an internal Ericsson product, TRR, in 2017-2018. TRR's first bug assignment without human intervention happened in 2019. [Objective/Aim] Our exploratory study will evaluate the adoption of TRR within its industrial context at Ericsson. We seek to understand 1) how TRR performs in the field, 2) what value TRR provides to Ericsson, and 3) how TRR has influenced the ways of working. Secondly, we will provide lessons learned related to productization of a research prototype within a company. [Method] We design an industrial case study combining interviews with TRR developers and users with analysis of data extracted from the bug tracking system at Ericsson. Furthermore, we will analyze sprint planning meetings recorded during the productization. Our data analysis will include thematic analysis, descriptive statistics, and Bayesian causal analysis.