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Ownership at Large: Open Problems and Challenges in Ownership Management

Authors:

Maria Eugenia Berezin,

Kinga Bojarczuk,

Elena Dulskyte,

Inna Dvortsova,

Natalija Gucevska,

Justin Spahr-SummersAuthors Info & Claims

ICPC '20: Proceedings of the 28th International Conference on Program Comprehension

Pages 406 - 410

https://doi.org/10.1145/3387904.3389293

Published: 12 September 2020 Publication History

Abstract

Software-intensive organizations rely on large numbers of software assets of different types, e.g., source-code files, tables in the data warehouse, and software configurations. Who is the most suitable owner of a given asset changes over time, e.g., due to reorganization and individual function changes. New forms of automation can help suggest more suitable owners for any given asset at a given point in time. By such efforts on ownership health, accountability of ownership is increased. The problem of finding the most suitable owners for an asset is essentially a program comprehension problem: how do we automatically determine who would be best placed to understand, maintain, evolve (and thereby assume ownership of) a given asset. This paper introduces the Facebook Ownesty system, which uses a combination of ultra large scale data mining and machine learning and has been deployed at Facebook as part of the company's ownership management approach. Ownesty processes many millions of software assets (e.g., source-code files) and it takes into account workflow and organizational aspects. The paper sets out open problems and challenges on ownership for the research community with advances expected from the fields of software engineering, programming languages, and machine learning.

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Cited By

Thongtanunam PTantithamthavorn C(2024)Code Ownership: The Principles, Differences, and Their Associations with Software Quality2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE62328.2024.00044(379-390)Online publication date: 28-Oct-2024
https://doi.org/10.1109/ISSRE62328.2024.00044
Harman MKechagia MTan SMechtaev STan L(2022)Scaling genetic improvement and automated program repairProceedings of the Third International Workshop on Automated Program Repair10.1145/3524459.3527353(1-7)Online publication date: 19-May-2022
https://dl.acm.org/doi/10.1145/3524459.3527353
Fucci DAlégroth EAxelsson T(2022)When traceability goes awry: An industrial experience reportJournal of Systems and Software10.1016/j.jss.2022.111389192(111389)Online publication date: Oct-2022
https://doi.org/10.1016/j.jss.2022.111389
Show More Cited By

Index Terms

Ownership at Large: Open Problems and Challenges in Ownership Management
1. Software and its engineering
  1. Software creation and management
    1. Software development process management

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cover image ACM Conferences

ICPC '20: Proceedings of the 28th International Conference on Program Comprehension

July 2020

481 pages

ISBN:9781450379588

DOI:10.1145/3387904

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 September 2020

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Short-paper
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Refereed limited

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ICPC '20

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SIGSOFT

ICPC '20: 28th International Conference on Program Comprehension

July 13 - 15, 2020

Seoul, Republic of Korea

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ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

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Cited By

Thongtanunam PTantithamthavorn C(2024)Code Ownership: The Principles, Differences, and Their Associations with Software Quality2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE)10.1109/ISSRE62328.2024.00044(379-390)Online publication date: 28-Oct-2024
https://doi.org/10.1109/ISSRE62328.2024.00044
Harman MKechagia MTan SMechtaev STan L(2022)Scaling genetic improvement and automated program repairProceedings of the Third International Workshop on Automated Program Repair10.1145/3524459.3527353(1-7)Online publication date: 19-May-2022
https://dl.acm.org/doi/10.1145/3524459.3527353
Fucci DAlégroth EAxelsson T(2022)When traceability goes awry: An industrial experience reportJournal of Systems and Software10.1016/j.jss.2022.111389192(111389)Online publication date: Oct-2022
https://doi.org/10.1016/j.jss.2022.111389
Cito JDillig IKim SMurali VChandra SSpinellis DGousios GChechik MDi Penta M(2021)Explaining mispredictions of machine learning models using rule inductionProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3468264.3468614(716-727)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3468264.3468614

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