Technical Debt: From Metaphor to Theory and Practice
By Philippe B Kruchten, R. Nord, Ipek Ozkaya. IEEE Software, 2012
692 citations recorded by the source corpus.
Subfields: Computer Science Applications, Information Systems
View Paper: https://www.semanticscholar.org/paper/d53f01b23b2f2f96de1584c5191856619a250706
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Science Applications
In Information Systems
- Michele Tufano
- Gabriele Bavota
- Denys Poshyvanyk
- Rocco Oliveto
- Andrea De Lucia
- Steve M. Easterbrook
- Massimiliano Di Penta
- Fausto Fasano
Citations in the corpus
- Sustainability Design and Software: The Karlskrona Manifesto (2015, background): “their evolution have often focused on improving architecture, decreasing lifecycle costs and managing technical debt [2].”
- Introduction - Platforms and Infrastructures in the Digital Age (2018, background): “Rolland, Mathiassen, and Rai also explore digital debt; “an organization’s cumulative buildup of technical and informational obligations related to maintenance and evolvability of its platform and infras-tructure” (Kruchten et al. 2012, Ramasubbu and Kemerer 2016).”
- When and Why Your Code Starts to Smell Bad (and Whether the Smells Go Away) (2017, positive): “THE technical debt metaphor introduced by Cunningham [22] explains well the trade-offs between delivering the most appropriate but still immature product, in the shortest timepossible [14], [22], [42], [47], [70].”
- Influence apart from Adoption: How Interaction between Programming and Scientific Practices Shapes Modes of Inquiry in Four Oceanography Teams (2016, background): ““Technical debt” is another term, like debugging, which was applied virtually not at all by the scientists to themselves, but which appears routinely in discussion of programming practices by computing researchers and practitioners [35].”
- On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation (2017, background): “As a form of technical debt (Cunningham 1993), they could hinder the comprehensibility and maintainability of software systems (Kruchten et al. 2012).”