Strong Agile Metrics

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

Conference paper (2017)

Authors

H.K.M. Huijgens Software Engineering -

Robert Lamping ING

Dick Stevens ING

Hartger Rothengatter ING

G. Gousios Software Engineering -

D. Romano ING

Research Group

Software Engineering () (TU Delft)

DOI: https://doi.org/10.1145/3106237.3117779

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

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

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Published Date

2017

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Software Engineering

Abstract

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.

Files

TUD_SERG_2017_010.pdf

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