Standard

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: Scientific - peer-reviewConference contribution

Harvard

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. DOI: 10.1145/3106237.3117779

APA

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). New York, NY: Association for Computing Machinery (ACM). DOI: 10.1145/3106237.3117779

Vancouver

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. Available from, DOI: 10.1145/3106237.3117779

Author

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

BibTeX

@inbook{7d9a3ec1ef3b43f18f827f9ed6f7dbdc,
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",
doi = "10.1145/3106237.3117779",
pages = "866--871",
booktitle = "ESEC/FSE 2017",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

RIS

TY - CHAP

T1 - Strong Agile Metrics

T2 - Mining Log Data to Determine Predictive Power of Software Metrics for Continuous Delivery Teams

AU - Huijgens,Hennie

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

SP - 866

EP - 871

BT - ESEC/FSE 2017

PB - Association for Computing Machinery (ACM)

ER -

ID: 41522366