The data collected from open source projects provide means to
model large software ecosystems, but often suffer from data quality
issues, specifically, multiple author identification strings in code
commits might actually be associated with one developer. While
many methods have been proposed for addressing this problem,
they are either heuristics requiring manual tweaking, or require
too much calculation time to do pairwise comparisons for 38M
author IDs in, for example, the World of Code collection. In this
paper, we propose a method that finds all author IDs belonging
to a single developer in this entire dataset, and share the list of
all author IDs that were found to have aliases. To do this, we first
create blocks of potentially connected author IDs and then use
a machine learning model to predict which of these potentially
related IDs belong to the same developer. We processed around
38 million author IDs and found around 14.8 million IDs to have
an alias, which belong to 5.4 million different developers, with the
median number of aliases being 2 per developer. This dataset can
be used to create more accurate models of developer behaviour at
the entire OSS ecosystem level and can be used to provide a service
to rapidly resolve new author IDs.
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Developer Reputation Estimator (DRE)
Evidence shows that developer reputation is extremely important when accepting pull requests or resolving reported issues. It is particularly salient in Free/Libre Open Source Software since the developers are distributed around the world, do not work for the same organization and, in most cases, never meet face to face. The existing solutions to expose developer reputation tend to be forge specific (GitHub), focus on activity instead of impact, do not leverage social or technical networks, and do not correct often misspelled developer identities. We aim to remedy this by amalgamating data from all public Git repositories, measuring the impact of developer work, expose developer's collaborators, and correct notoriously problematic developer identity data. We leverage World of Code (WoC), a collection of an almost complete (and continuously updated) set of Git repositories by first allowing developers to select which of the 34 million(M) Git commit author IDs belong to them and then generating their profiles by treating the selected collection of IDs as that single developer. As a side-effect, these selections serve as a training set for a supervised learning algorithm that merges multiple identity strings belonging to a single individual. As we evaluate the tool and the proposed impact measure, we expect to build on these findings to develop reputation badges that could be associated with pull requests and commits so developers could easier trust and prioritize them.
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- NSF-PAR ID:
- 10177383
- Date Published:
- Journal Name:
- ASE '19: Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering
- Page Range / eLocation ID:
- 1082–1085
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ASE.2019.00107
