2017 11th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE 2017), September 4–8, 2017, Paderborn, Germany

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The State of the Practice
Industrial Papers
S4, Chair: Natalia Juristo
Strong Agile Metrics: Mining Log Data to Determine Predictive Power of Software Metrics for Continuous Delivery Teams
Publisher's Version
Abstract: ING Bank, a large Netherlands-based internationally operating bank, implemented a fully automated continuous delivery pipe-line for its software engineering activities in more than 300 teams, that perform more than 2500 deployments to production each month on more than 750 different applications. Our objective is to examine how strong metrics for agile (Scrum) DevOps teams can be set in an iterative fashion. We perform an exploratory case study that focuses on the classification based on predictive power of software metrics, in which we analyze log data derived from two initial sources within this pipeline. We analyzed a subset of 16 metrics from 59 squads. We identified two lagging metrics and assessed four leading metrics to be strong.

Time stamp: 2020-09-21T16:59:08+02:00