Standard

Software Analytics in Continuous Delivery: A Case Study on Success Factors. / Huijgens, Hennie; Spadini, Davide; Stevens, Dick; Visser, Niels; van Deursen, Arie.

12th International Symposium on Empirical Software Engineering and Measurement (ESEM 2018). Association for Computing Machinery (ACM), 2018. 25.

Research output: Scientific - peer-reviewConference contribution

Harvard

Huijgens, H, Spadini, D, Stevens, D, Visser, N & van Deursen, A 2018, Software Analytics in Continuous Delivery: A Case Study on Success Factors. in 12th International Symposium on Empirical Software Engineering and Measurement (ESEM 2018)., 25, Association for Computing Machinery (ACM). DOI: 10.1145/3239235.3240505

APA

Huijgens, H., Spadini, D., Stevens, D., Visser, N., & van Deursen, A. (2018). Software Analytics in Continuous Delivery: A Case Study on Success Factors. In 12th International Symposium on Empirical Software Engineering and Measurement (ESEM 2018) [25] Association for Computing Machinery (ACM). DOI: 10.1145/3239235.3240505

Vancouver

Huijgens H, Spadini D, Stevens D, Visser N, van Deursen A. Software Analytics in Continuous Delivery: A Case Study on Success Factors. In 12th International Symposium on Empirical Software Engineering and Measurement (ESEM 2018). Association for Computing Machinery (ACM). 2018. 25. Available from, DOI: 10.1145/3239235.3240505

Author

Huijgens, Hennie ; Spadini, Davide ; Stevens, Dick ; Visser, Niels ; van Deursen, Arie. / Software Analytics in Continuous Delivery: A Case Study on Success Factors. 12th International Symposium on Empirical Software Engineering and Measurement (ESEM 2018). Association for Computing Machinery (ACM), 2018.

BibTeX

@inbook{9c5d1df6e2444fdc933d8bc36f9e3bd4,
title = "Software Analytics in Continuous Delivery: A Case Study on Success Factors",
abstract = "Background: During the period of one year, ING developed an approach for software analytics within an environment of a large number of software engineering teams working in a Continuous Delivery as a Service setting. Goal: Our objective is to examine what factors helped and hindered the implementation of software analytics in such an environment, in order to improve future software analytics activities. Method: We analyzed artifacts delivered by the software analytics project, and performed semi-structured interviews with 15 stakeholders. Results: We identified 16 factors that helped the implementation of software analytics, and 20 factors that hindered the project. Conclusions: Upfront defining and communicating the aims, standardization of data at an early stage, build efficient visualizations, and an empirical approach help companies to improve software analytics projects.",
author = "Hennie Huijgens and Davide Spadini and Dick Stevens and Niels Visser and {van Deursen}, Arie",
note = "Acknowledgments: European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642954",
year = "2018",
doi = "10.1145/3239235.3240505",
booktitle = "12th International Symposium on Empirical Software Engineering and Measurement (ESEM 2018)",
publisher = "Association for Computing Machinery (ACM)",
address = "United States",

}

RIS

TY - CHAP

T1 - Software Analytics in Continuous Delivery: A Case Study on Success Factors

AU - Huijgens,Hennie

AU - Spadini,Davide

AU - Stevens,Dick

AU - Visser,Niels

AU - van Deursen,Arie

N1 - Acknowledgments: European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642954

PY - 2018

Y1 - 2018

N2 - Background: During the period of one year, ING developed an approach for software analytics within an environment of a large number of software engineering teams working in a Continuous Delivery as a Service setting. Goal: Our objective is to examine what factors helped and hindered the implementation of software analytics in such an environment, in order to improve future software analytics activities. Method: We analyzed artifacts delivered by the software analytics project, and performed semi-structured interviews with 15 stakeholders. Results: We identified 16 factors that helped the implementation of software analytics, and 20 factors that hindered the project. Conclusions: Upfront defining and communicating the aims, standardization of data at an early stage, build efficient visualizations, and an empirical approach help companies to improve software analytics projects.

AB - Background: During the period of one year, ING developed an approach for software analytics within an environment of a large number of software engineering teams working in a Continuous Delivery as a Service setting. Goal: Our objective is to examine what factors helped and hindered the implementation of software analytics in such an environment, in order to improve future software analytics activities. Method: We analyzed artifacts delivered by the software analytics project, and performed semi-structured interviews with 15 stakeholders. Results: We identified 16 factors that helped the implementation of software analytics, and 20 factors that hindered the project. Conclusions: Upfront defining and communicating the aims, standardization of data at an early stage, build efficient visualizations, and an empirical approach help companies to improve software analytics projects.

U2 - 10.1145/3239235.3240505

DO - 10.1145/3239235.3240505

M3 - Conference contribution

BT - 12th International Symposium on Empirical Software Engineering and Measurement (ESEM 2018)

PB - Association for Computing Machinery (ACM)

ER -

ID: 45856937