Lessons Learned from a Decade of FLOSS Data Collection

Crowston, Kevin; Squire, Megan

doi:10.1007/978-3-319-59186-5_7

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Although Gitter is similar to Instant Relay Chat (IRC), Gitter's integration with Github repositories has made it popular among recent FOSS projects. Since prior research found toxic interactions among FOSS IRC channels [1], [35], we considered chat messages as one of our sources. We selected the gitter channel of the Ethereum project 6 , since it is one of the most active channels on Gitter.…”

Section: A Data Source Selectionmentioning

confidence: 99%

A Benchmark Study of the Contemporary Toxicity Detectors on Software Engineering Interactions

Sarker¹,

Turzo²,

Bosu³

2020

Preprint

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Automated filtering of toxic conversations may help an Open-source software (OSS) community to maintain healthy interactions among the project participants. Although, several general purpose tools exist to identify toxic contents, those may incorrectly flag some words commonly used in the Software Engineering (SE) context as toxic (e.g., 'junk', 'kill', and 'dump') and vice versa. To encounter this challenge, an SE specific tool has been proposed by the CMU Strudel Lab (referred as the 'STRUDEL' hereinafter) by combining the output of the Perspective API with the output from a customized version of the Stanford's Politeness detector tool. However, since STRUDEL's evaluation was very limited with only 654 SE text, its practical applicability is unclear. Therefore, this study aims to empirically evaluate the Strudel tool as well as four state-of-the-art general purpose toxicity detectors on a large scale SE dataset. On this goal, we empirically developed a rubric to manually label toxic SE interactions. Using this rubric, we manually labeled a dataset of 6,533 code review comments and 4,140 Gitter messages. The results of our analyses suggest significant degradation of all tools' performances on our datasets. Those degradations were significantly higher on our dataset of formal SE communication such as code review than on our dataset of informal communication such as Gitter messages. Two of the models from our study showed significant performance improvements during 10fold cross validations after we retrained those on our SE datasets. Based on our manual investigations of the incorrectly classified text, we have identified several recommendations for developing an SE specific toxicity detector.

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Section: A Data Source Selectionmentioning

confidence: 99%