在实践中采用自动错误分派--爱立信纵向案例研究

IF 3.5 2区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Markus Borg, Leif Jonsson, Emelie Engström, Béla Bartalos, Attila Szabó
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引用次数: 0

摘要

[背景] 在大型开发项目中,不断涌入的错误报告是一个相当大的挑战。受当代软件库挖掘工作的启发,我们在 2011-2016 年设计了一个基于机器学习的错误分配解决方案原型。该原型于 2017-2018 年发展成为爱立信内部产品 TRR。2019 年 4 月,TRR 在没有人工干预的情况下完成了首次错误分派。[目标]我们的研究评估了爱立信在工业背景下采用 TRR 的情况,即提供了与公司内部研究原型产品化相关的经验教训。此外,我们还调查了 1) TRR 在现场的表现,2) TRR 为爱立信带来的价值,以及 3) TRR 如何影响了工作方式。[方法] 我们结合对 TRR 利益相关者的访谈、冲刺计划会议记录和错误跟踪数据,开展了一项预先注册的工业案例研究。数据分析包括主题分析、描述性统计和贝叶斯因果分析。[结果] TRR 现已成为错误分派流程的一部分。考虑到电信栈的抽象层次,高层模块更积极,而低层模块则存在一些缺陷。最重要的是,一些错误报告直接到达低层模块,而没有首先通过高层的基本根源分析步骤。平均而言,TRR 自动分配了 30% 的错误报告,准确率为 75%。在爱立信内部,自动路由 TR 的解决速度提高了约 21%,TRR 为经验丰富的工程师节省了许多工作时间。采用 TRR 的间接效果包括流程改进、流程意识、沟通增加和工作满意度提高。[结论] TRR 为爱立信节省了时间,但与其他公司报告的类似工作相比,自动错误分配的采用更为复杂。我们将这种差异主要归因于该公司的庞大规模和复杂的产品。成功采用的关键因素包括逐步引入、产品拥护者和对利益相关者的仔细分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Adopting automated bug assignment in practice — a longitudinal case study at Ericsson

Adopting automated bug assignment in practice — a longitudinal case study at Ericsson

[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 April 2019. [Objective] Our study evaluates the adoption of TRR within its industrial context at Ericsson, i.e., we provide lessons learned related to the productization of a research prototype within a company. Moreover, we investigate 1) how TRR performs in the field, 2) what value TRR provides to Ericsson, and 3) how TRR has influenced the ways of working. [Method] We conduct a preregistered industrial case study combining interviews with TRR stakeholders, minutes from sprint planning meetings, and bug-tracking data. The data analysis includes thematic analysis, descriptive statistics, and Bayesian causal analysis. [Results] TRR is now an incorporated part of the bug assignment process. Considering the abstraction levels of the telecommunications stack, high-level modules are more positive while low-level modules experienced some drawbacks. Most importantly, some bug reports directly reach low-level modules without first having passed through fundamental root-cause analysis steps at higher levels. On average, TRR automatically assigns 30% of the incoming bug reports with an accuracy of 75%. Auto-routed TRs are resolved around 21% faster within Ericsson, and TRR has saved highly seasoned engineers many hours of work. Indirect effects of adopting TRR include process improvements, process awareness, increased communication, and higher job satisfaction. [Conclusions] TRR has saved time at Ericsson, but the adoption of automated bug assignment was more intricate compared to similar endeavors reported from other companies. We primarily attribute the difference to the very large size of the organization and the complex products. Key facilitators in the successful adoption include a gradual introduction, product champions, and careful stakeholder analysis.

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来源期刊
Empirical Software Engineering
Empirical Software Engineering 工程技术-计算机:软件工程
CiteScore
8.50
自引率
12.20%
发文量
169
审稿时长
>12 weeks
期刊介绍: Empirical Software Engineering provides a forum for applied software engineering research with a strong empirical component, and a venue for publishing empirical results relevant to both researchers and practitioners. Empirical studies presented here usually involve the collection and analysis of data and experience that can be used to characterize, evaluate and reveal relationships between software development deliverables, practices, and technologies. Over time, it is expected that such empirical results will form a body of knowledge leading to widely accepted and well-formed theories. The journal also offers industrial experience reports detailing the application of software technologies - processes, methods, or tools - and their effectiveness in industrial settings. Empirical Software Engineering promotes the publication of industry-relevant research, to address the significant gap between research and practice.
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