Adopting Automated Bug Assignment in Practice -- A Registered Report of an Industrial Case Study
[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 in...
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| creator | Borg, Markus Jonsson, Leif Engström, Emelie Bartalos, Béla Szabo, Attila |
| description | [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. |
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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.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Case studies ; Context ; Data analysis ; Machine learning ; Prototypes ; Tracking systems</subject><ispartof>arXiv.org, 2021-09</ispartof><rights>2021. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). 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| subjects | Case studies Context Data analysis Machine learning Prototypes Tracking systems |
| title | Adopting Automated Bug Assignment in Practice -- A Registered Report of an Industrial Case Study |
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