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Selecting an appropriate task is challenging for contributors to Open SourceSoftware (OSS), mainly for those who are contributing for the first time.Therefore, researchers and OSS projects have proposed various strategies to aidnewcomers, including labeling tasks. We investigate the automatic labeling ofopen issues strategy to help the contributors to pick a task to contribute. Welabel the issues with API-domains--categories of APIs parsed from the sourcecode used to solve the issues. We plan to add social network analysis metricsfrom the issues conversations as new predictors. By identifying the skills, weclaim the contributor candidates should pick a task more suitable. We analyzedinterview transcripts and the survey's open-ended questions to comprehend thestrategies used to assist in onboarding contributors and used to pick up anissue. We applied quantitative studies to analyze the relevance of the labelsin an experiment and compare the strategies' relative importance. We also minedissue data from OSS repositories to predict the API-domain labels withcomparable precision, recall, and F-measure with the state-of-art. We plan touse a skill ontology to assist the matching process between contributors andtasks. By analyzing the confidence level of the matching instances inontologies describing contributors' skills and tasks, we might recommend issuesfor contribution. So far, the results showed that organizing the issues--whichincludes assigning labels is seen as an essential strategy for diverse roles inOSS communities. The API-domain labels are relevant for experiencedpractitioners. The predictions have an average precision of 75.5%. Labeling theissues indicates the skills involved in an issue. The labels represent possibleskills in the source code related to an issue. By investigating this researchtopic, we expect to assist the new contributors in finding a task.

Supporting the Task-driven Skill Identification in Open Source Project Issue Tracking Systems