abstract

Data scientists in software teams: state of the art and challenges

Authors:

Miryung Kim,

Thomas Zimmermann,

Robert DeLine,

Andrew BegelAuthors Info & Claims

ICSE '18: Proceedings of the 40th International Conference on Software Engineering

Page 585

https://doi.org/10.1145/3180155.3182515

Published: 27 May 2018 Publication History

Get Access

Abstract

The demand for analyzing large scale telemetry, machine, and quality data is rapidly increasing in software industry. Data scientists are becoming popular within software teams. For example, Face-book, LinkedIn and Microsoft are creating a new career path for data scientists. In this paper, we present a large-scale survey with 793 professional data scientists at Microsoft to understand their educational background, problem topics that they work on, tool usages, and activities. We cluster these data scientists based on the time spent for various activities and identify 9 distinct clusters of data scientists and their corresponding characteristics. We also discuss the challenges that they face and the best practices they share with other data scientists. Our study finds several trends about data scientists in the software engineering context at Microsoft, and should inform managers on how to leverage data science capability effectively within their teams.

Reference

[1]

Miryung Kim, Thomas Zimmermann, Robert DeLine, and Andrew Begel. 2016. Data Scientists in Software Teams: State of the Art and Challenges. IEEE Transactions in Software Engineering. To appear.

Google Scholar

Cited By

View all

Wan CLiu YDu KHoffmann HJiang JMaire MLu S(2023)Run-Time Prevention of Software Integration Failures of Machine Learning APIsProceedings of the ACM on Programming Languages10.1145/36228067:OOPSLA2(264-291)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3622806
Ramos DMitchell HLynce IManquinho VMartins RGoues C(2023)MELT: Mining Effective Lightweight Transformations from Pull Requests2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)10.1109/ASE56229.2023.00117(1516-1528)Online publication date: 11-Sep-2023
https://doi.org/10.1109/ASE56229.2023.00117
Takenouchi KIshio TOkada JSakata Y(2021)PATSQLProceedings of the VLDB Endowment10.14778/3476249.347625314:11(1937-1949)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.14778/3476249.3476253
Show More Cited By

Recommendations

Analyze this! 145 questions for data scientists in software engineering
ICSE 2014: Proceedings of the 36th International Conference on Software Engineering

In this paper, we present the results from two surveys related to data science applied to software engineering. The first survey solicited questions that software engineers would like data scientists to investigate about software, about software ...
The emerging role of data scientists on software development teams
ICSE '16: Proceedings of the 38th International Conference on Software Engineering

Creating and running software produces large amounts of raw data about the development process and the customer usage, which can be turned into actionable insight with the help of skilled data scientists. Unfortunately, data scientists with the ...
Questions for data scientists in software engineering: a replication
ESEC/FSE 2020: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering

In 2014, a Microsoft study investigated the sort of questions that data science applied to software engineering should answer. This resulted in 145 questions that developers considered relevant for data scientists to answer, thus providing a research ...

Comments

Information & Contributors

Information

Published In

ICSE '18: Proceedings of the 40th International Conference on Software Engineering

May 2018

1307 pages

ISBN:9781450356381

DOI:10.1145/3180155

Conference Chair:
Michel Chaudron
Chalmers University of Technology, University of Gothenburg, Sweden
,
General Chair:
Ivica Crnkovic
Chalmers University of Technology, University of Gothenburg, Sweden
,
Program Chairs:
Marsha Chechik
University of Toronto, Canada
,
Mark Harman
Facebook and University College London, United Kingdom

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2018

Check for updates

Author Tags

Qualifiers

Abstract

Conference

ICSE '18

Sponsor:

SIGSOFT
IEEE-CS

ICSE '18: 40th International Conference on Software Engineering

May 27 - June 3, 2018

Gothenburg, Sweden

Acceptance Rates

Overall Acceptance Rate 276 of 1,856 submissions, 15%

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
273
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)1

Reflects downloads up to 15 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Wan CLiu YDu KHoffmann HJiang JMaire MLu S(2023)Run-Time Prevention of Software Integration Failures of Machine Learning APIsProceedings of the ACM on Programming Languages10.1145/36228067:OOPSLA2(264-291)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3622806
Ramos DMitchell HLynce IManquinho VMartins RGoues C(2023)MELT: Mining Effective Lightweight Transformations from Pull Requests2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)10.1109/ASE56229.2023.00117(1516-1528)Online publication date: 11-Sep-2023
https://doi.org/10.1109/ASE56229.2023.00117
Takenouchi KIshio TOkada JSakata Y(2021)PATSQLProceedings of the VLDB Endowment10.14778/3476249.347625314:11(1937-1949)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.14778/3476249.3476253
Almahmoud JDeLine RDrucker S(2021)How Teams Communicate about the Quality of ML Models: A Case Study at an International Technology CompanyProceedings of the ACM on Human-Computer Interaction10.1145/34639345:GROUP(1-24)Online publication date: 13-Jul-2021
https://dl.acm.org/doi/10.1145/3463934
Wan CLiu SHoffmann HMaire MLu S(2021)Are Machine Learning Cloud APIs Used Correctly?Proceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00024(125-137)Online publication date: 22-May-2021
https://dl.acm.org/doi/10.1109/ICSE43902.2021.00024
Krasteva IIlieva S(2020)Adopting Agile Software Development Methodologies in Big Data Projects – a Systematic Literature Review of Experience Reports2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378118(2028-2033)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378118
Aghabayli APfahl DMartínez-Fernández STrendowicz ASentilles SBoehm BTrubiani CKoziolek A(2019)Integrating runtime data with development data to monitor external quality: challenges from practiceProceedings of the 2nd ACM SIGSOFT International Workshop on Software Qualities and Their Dependencies10.1145/3340495.3342752(20-26)Online publication date: 26-Aug-2019
https://dl.acm.org/doi/10.1145/3340495.3342752
Diosan LMotogna SVanderose BFrénay BHenry JDevroey X(2019)Artificial intelligence meets software engineering in the classroomProceedings of the 1st ACM SIGSOFT International Workshop on Education through Advanced Software Engineering and Artificial Intelligence10.1145/3340435.3342718(35-38)Online publication date: 26-Aug-2019
https://dl.acm.org/doi/10.1145/3340435.3342718
Souibgui MAtigui FZammali SCherfi SYahia S(2019)Data quality in ETL processProcedia Computer Science10.1016/j.procs.2019.09.223159:C(676-687)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1016/j.procs.2019.09.223

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Recommendations

Analyze this! 145 questions for data scientists in software engineering

The emerging role of data scientists on software development teams

Questions for data scientists in software engineering: a replication