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Supporting analysis of technical debt causes and effects with cross-company probabilistic cause-effect diagrams.

, , , and . TechDebt@ICSE, page 3-12. IEEE / ACM, (2019)

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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)A Prototype Experience Management System for a Software Consulting Organization., , , and . SEKE, page 29-36. (2001)Usando Medição de Código Fonte para Refactoring., and . JIISIC, page 223-231. (2002)Data Warehousing in an Industrial Software Development Environment., , and . SEW, page 131-135. IEEE Computer Society, (2009)Relacionando Refactorings e Métricas de Código Fonte - Um Primeiro Passo para Detecção Automática de Oportunidades de Refactoring., and . SBES, page 47-62. SBC, (2003)A Study of Factors that Lead Development Teams to Incur Technical Debt in Software Projects., , , and . SEAA, page 429-436. IEEE Computer Society, (2018)A Strategy Based on Multiple Decision Criteria to Support Technical Debt Management., , , and . SEAA, page 334-341. IEEE Computer Society, (2017)Garimpando Fraudadores: O Uso da Mineração de Dados na Descoberta de Padrões de Consumidores que Causam Perdas Comerciais em uma Concessionária de Energia Elétrica., , , , , , , and . SBSI, page 154-165. (2010)Long Term-short Memory Neural Networks and Word2vec for Self-admitted Technical Debt Detection., , , and . ICEIS (2), page 157-165. SCITEPRESS, (2020)Identification and management of technical debt: A systematic mapping study., , , , , and . Inf. Softw. Technol., (2016)