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A Contextualized Vocabulary Model for identifying technical debt on code comments.

, , , and . MTD@ICSME, page 25-32. IEEE Computer Society, (2015)

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A Contextualized Vocabulary Model for identifying technical debt on code comments., , , and . MTD@ICSME, page 25-32. IEEE Computer Society, (2015)Recovering Valuable Information Behaviour from OSS Contributors: An Exploratory Study., , , , and . SEKE, page 474-477. Knowledge Systems Institute Graduate School, (2014)A Study on Identification of Documentation and Requirement Technical Debt through Code Comment Analysis., , , and . SBQS, page 21-30. ACM, (2018)Triangulating Experiments in an Industrial Setting to Evaluate Preferred Representational Systems of Software Developers., , , , and . SBES, page 71-80. IEEE Computer Society, (2014)A systematic mapping study on mining software repositories., , , , , and . SAC, page 1472-1479. ACM, (2016)Identifying Technical Debt through a Code Comment Mining Tool., , , , , , and . SBSI, page 18:1-18:8. ACM, (2019)Investigating the Use of a Contextualized Vocabulary in the Identification of Technical Debt: A Controlled Experiment., , , , , and . ICEIS (1), page 369-378. SciTePress, (2016)Analyzing Distributions of Emails and Commits from OSS Contributors through Mining Software Repositories - An Exploratory Study., , , , and . ICEIS (2), page 303-310. SciTePress, (2015)Decision Criteria for the Payment of Technical Debt in Software Projects: A Systematic Mapping Study., , , and . ICEIS (1), page 572-579. SciTePress, (2016)Investigating the Identification of Technical Debt Through Code Comment Analysis., , , , and . ICEIS (Revised Selected Papers), volume 291 of Lecture Notes in Business Information Processing, page 284-309. Springer, (2016)