Skuld: a self-learning tool for impact-driven technical debt management
Document typeConference lecture
PublisherAssociation for Computing Machinery (ACM)
Rights accessOpen Access
As the development progresses, software projects tend to accumulate Technical Debt and become harder to maintain. Multiple tools exist with the mission to help practitioners to better manage Technical Debt. Despite this progress, there is a lack of tools providing actionable and self-learned suggestions to practitioners aimed at mitigating the impact of Technical Debt in real projects. We aim to create a data-driven, lightweight, and self-learning tool positioning highly impactful refactoring proposals on a Jira backlog. Bearing this goal in mind, the first two authors have founded a startup, called Skuld.ai, with the vision of becoming the go-to software renovation company. In this tool paper, we present the software architecture and demonstrate the main functionalities of our tool. It has been showcased to practitioners, receiving positive feedback. Currently, its release to the market is underway thanks to an industry-research institute collaboration with Fraunhofer IESE to incorporate self-learning technical debt capabilities.
CitationBurgaya-Pujols, J. [et al.]. Skuld: a self-learning tool for impact-driven technical debt management. A: International Conference on Technical Debt. "2020 IEEE/ACM International Conference on Technical Debt, TechDebt 2020: Seoul, Republic of Korea, 28-30 June 2020: proceedings". New York: Association for Computing Machinery (ACM), 2020, p. 113-114. ISBN 978-1-4503-7960-1. DOI 10.1145/3387906.3388626.
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