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

Hennie Huijgens, Robert Lamping, Dick Stevens, Hartger Rothengatter, Georgios Gousios, Daniele Romano

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

12 Citations (Scopus)
185 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationESEC/FSE 2017
Subtitle of host publicationProceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Number of pages6
ISBN (Electronic)978-1-4503-5105-8
Publication statusPublished - 2017
EventESEC/FSE 2017: 11th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering - Paderborn, Germany
Duration: 4 Sep 20178 Sep 2017


ConferenceESEC/FSE 2017
Abbreviated titleESEC/FSE 2017
Internet address


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


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