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

doi:10.1145/3106237.3117779

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

Software Engineering

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

12 Citations (Scopus)

185 Downloads (Pure)

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.

Original language	English
Title of host publication	ESEC/FSE 2017
Subtitle of host publication	Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering
Place of Publication	New York, NY
Publisher	Association for Computing Machinery (ACM)
Pages	866-871
Number of pages	6
ISBN (Electronic)	978-1-4503-5105-8
DOIs	https://doi.org/10.1145/3106237.3117779
Publication status	Published - 2017
Event	ESEC/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 2017 → 8 Sep 2017 http://esec-fse17.uni-paderborn.de/

Conference

Conference	ESEC/FSE 2017
Abbreviated title	ESEC/FSE 2017
Country/Territory	Germany
City	Paderborn
Period	4/09/17 → 8/09/17
Internet address	http://esec-fse17.uni-paderborn.de/

Keywords

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

Access to Document

10.1145/3106237.3117779

TUD-SERG-2017-010Accepted author manuscript, 2.56 MB

Cite this

Huijgens, H., Lamping, R., Stevens, D., Rothengatter, H., Gousios, G., & Romano, D. (2017). Strong Agile Metrics: Mining Log Data to Determine Predictive Power of Software Metrics for Continuous Delivery Teams. In ESEC/FSE 2017: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering (pp. 866-871). Association for Computing Machinery (ACM). https://doi.org/10.1145/3106237.3117779

Huijgens, Hennie ; Lamping, Robert ; Stevens, Dick ; Rothengatter, Hartger ; Gousios, Georgios ; Romano, Daniele. / Strong Agile Metrics : Mining Log Data to Determine Predictive Power of Software Metrics for Continuous Delivery Teams. ESEC/FSE 2017: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering . New York, NY : Association for Computing Machinery (ACM), 2017. pp. 866-871

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

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.",

keywords = "Software Economics, Agile Metrics, Scrum, Continuous Delivery, Prediction Modelling, DevOps, Data Mining, Software Analytics",

author = "Hennie Huijgens and Robert Lamping and Dick Stevens and Hartger Rothengatter and Georgios Gousios and Daniele Romano",

year = "2017",

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booktitle = "ESEC/FSE 2017",

publisher = "Association for Computing Machinery (ACM)",

address = "United States",

note = "ESEC/FSE 2017 : 11th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE 2017 ; Conference date: 04-09-2017 Through 08-09-2017",

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Huijgens, H, Lamping, R, Stevens, D, Rothengatter, H, Gousios, G & Romano, D 2017, Strong Agile Metrics: Mining Log Data to Determine Predictive Power of Software Metrics for Continuous Delivery Teams. in ESEC/FSE 2017: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering . Association for Computing Machinery (ACM), New York, NY, pp. 866-871, ESEC/FSE 2017, Paderborn, Germany, 4/09/17. https://doi.org/10.1145/3106237.3117779

Strong Agile Metrics : Mining Log Data to Determine Predictive Power of Software Metrics for Continuous Delivery Teams. / Huijgens, Hennie; Lamping, Robert; Stevens, Dick; Rothengatter, Hartger; Gousios, Georgios; Romano, Daniele.

ESEC/FSE 2017: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering . New York, NY : Association for Computing Machinery (ACM), 2017. p. 866-871.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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T1 - Strong Agile Metrics

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AU - Lamping, Robert

AU - Stevens, Dick

AU - Rothengatter, Hartger

AU - Gousios, Georgios

AU - Romano, Daniele

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - Software Economics

KW - Agile Metrics

KW - Scrum

KW - Continuous Delivery

KW - Prediction Modelling

KW - DevOps

KW - Data Mining

KW - Software Analytics

U2 - 10.1145/3106237.3117779

DO - 10.1145/3106237.3117779

M3 - Conference contribution

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BT - ESEC/FSE 2017

PB - Association for Computing Machinery (ACM)

CY - New York, NY

Y2 - 4 September 2017 through 8 September 2017

ER -

Huijgens H, Lamping R, Stevens D, Rothengatter H, Gousios G, Romano D. Strong Agile Metrics: Mining Log Data to Determine Predictive Power of Software Metrics for Continuous Delivery Teams. In ESEC/FSE 2017: Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering . New York, NY: Association for Computing Machinery (ACM). 2017. p. 866-871 https://doi.org/10.1145/3106237.3117779

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

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