Strong Agile Metrics

Strong Agile Metrics: Mining Log Data to Determine Predictive Power of Software Metrics for Continuous Delivery Teams

Huijgens, H.K.M. (TU Delft Software Engineering)
Lamping, Robert (ING)
Stevens, Dick (ING)
Rothengatter, Hartger (ING)
Gousios, G. (TU Delft Software Engineering)
Romano, D. (ING)

2017

ING Bank, a large Netherlands-based internationally operating bank, implemented a fully automated continuous delivery pipeline 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.

Software Economics
Agile Metrics
Scrum
Continuous Delivery
Prediction Modelling
DevOps
Data Mining
Software Analytics

http://resolver.tudelft.nl/uuid:7d9a3ec1-ef3b-43f1-8f82-7f9ed6f7dbdc

https://doi.org/10.1145/3106237.3117779

Association for Computing Machinery (ACM), New York, NY

978-1-4503-5105-8

ESEC/FSE 2017: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering

ESEC/FSE 2017, 2017-09-04 → 2017-09-08, Paderborn, Germany

Institutional Repository

conference paper

© 2017 H.K.M. Huijgens, Robert Lamping, Dick Stevens, Hartger Rothengatter, G. Gousios, D. Romano