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Sujet de thèse - 2019

Cloud-Based Testing Workbench for Low-Code Engineering

Période : Septembre 2019 - 36 mois

Deadline : 10th April 2019


On behalf of the Lowcomote-ITN, IMT Atlantique (France) is pleased to announce the recruitment of 15 Marie Skłodowska -Curie (MSCA) PhD positions (Early Stage Researchers).
This new project is funded by the European Union’s Horizon 2020 research & innovation programme under the Marie Skłodowska-Curie – ITN, European Training Network programme. (Grant Agreement n°813884)

The project is titled Lowcomote: Training the Next Generation of Experts in Scalable Low-Code Engineering Platforms, and it has 15 PhD positions (Early-Stage Researchers – ESR) in the area of Computer Sciences (software engineering, Model-Driven Engineering, operating systems, computer languages, Modelling engineering, Cloud computing, Low-code Engineering Platforms) starting 1st September 2019 for a duration of 36 months.



This specific PhD position will hired by the LS2N among the team Naomod at the IMT Atlantique ( IMT ) France.


The benefits brought by low-code development, in terms of simplicity and maintainability could be annihilated if developed software is not correctly verified. A trap would be to consider that software with less code requires less test would be indeed the case for unit tests since the quality of the code is highly related to the quality of the code generators.

However, functional tests are still mandatory and LCEP should provide methods and tools to manage their heterogeneity and distribution upon scalability. Lowcomote will provide a quality workbench for LCEP.

The first objective is to help on test configuration. To follow the LCDP principles, the tests should be written in the same language as the software, meaning that the users should only provide their expert knowledge and the test implementation should be up to the test workbench. Here, MDE techniques will be useful to transform the low-code tests into a test model that will be merged with the system and infrastructure models. Therefore, since model transformations will be used to generate executable platform dependent tests, their heterogeneity and distribution are the main issues for this task. While Cloud computing techniques may help for managing distributed tests, they also have quality issues. Distributed test data must be collected in different formats and to run dependent code which could be distributed and written in different languages.

The second objective is to run the tests and get test results to be analysed for diagnostic. This objective requires to consider heterogeneity of the deployment platforms over the Cloud. Finally, dynamic modelling is still an issue which faces the scalability issue. Each test execution generates a trace that must be reified and linked to the global model, involving the generation of an important amount of data, which should be stored and queried effectively.


More informations and apply on lowcomote website and IMT Atlantique website ( -> PDF )


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