Authors:
Diego Saraiva
;
José Gameleira Neto
;
Uirá Kulesza
;
Guilherme Freitas
;
Rodrigo Reboucas
and
Roberta Coelho
Affiliation:
Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte, Natal, Brazil
Keyword(s):
Systematic Mapping Study, Technical Debt, Technical Debt Management, Tools.
Abstract:
Context: The concept of technical debt is a metaphor that contextualizes problems faced during software evolution that reflect technical compromises in tasks that are not carried out adequately during their development - they can yield short-term benefit to the project in terms of increased productivity and lower cost, but that may have to be paid off with interest later. Objective: This work investigates the current state of the art of technical debt tools by identifying which activities, functionalities and kind of technical debt are handled by existing tools that support the technical debt management in software projects. Method: A systematic mapping study is performed to identify and analyze available tools for managing technical debt based on a set of five research questions. Results: The work contributes with (i) a systematic mappping of current research on the field, (ii) a highlight of the most referenced tools, their main characteristics, their supported technical debt types
and activities, and (iii) a discussion of emerging findings and implications for future research. Conclusions: Our study identified 50 TD tools where 42 of them are new tools, and 8 tools extend an existing one. Most of the tools address technical debt related to code, design, and/or architecture artifacts. Besides, the different TD management activities received different levels of attention. For example, TD identification is supported by 80% of the tools, whereas 30% of them handle the TD documentation activity. Tools that deal with TD identification and measurement activities are still predominant. However, we observed that recent tools focusing on TD prevention, replacement, and prioritization activities represent emergent research trends.
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