An Adaptive Methodology to Overcome Localization Translation Challenges

  •  Abbas Brashi    


This study proposes an adaptive methodology to overcome localization translation challenges. The objective of the study is to generate a theoretical framework for identifying localization translation problems and ultimately propose a user-centred and agile-based methodology to minimize translation errors. The main research question that this paper attempts to answer is the question of “What would be the best theoretical framework for identifying current translation problems and addressing the convergence of translation and localization according to the new developments in informatics and communication technologies?” To answer this question, it was important to dismantle the notions of translation, translation theory, and localization. Based on the revised new definitions adapted to the new socio-technological context of the present digital era, the challenges can be identified and addressed through the formulation of a new methodology. The new methodology involves several steps, including the selection of recognized techniques like the “rich points” model to identify the localization translation challenges, a set of quality criteria to evaluate the projects, and adopting a user-centred approach and agile methodology for the project management of localization translation projects in order to assure the satisfaction of the stakeholders and a rapid adaptation to changes in the requirements. The proposed methodology must be validated in the future by applying it to concrete cases of localization translation projects and assessing its utility and performance. Thus, it would be useful in the future for improving localization translation projects.

This work is licensed under a Creative Commons Attribution 4.0 License.
  • ISSN(Print): 1923-869X
  • ISSN(Online): 1923-8703
  • Started: 2011
  • Frequency: bimonthly

Journal Metrics

Google-based Impact Factor (2021): 1.43

h-index (July 2022): 45

i10-index (July 2022): 283

h5-index (2017-2021): 25

h5-median (2017-2021): 37

Learn more