MegaModelling at Runtime

Truscan, Dragos (Principal Investigator)
Ahmad, Tanwir (Co-Investigator)
Iqbal, Junaid (Co-Investigator)
Porres Paltor, Ivan (Co-Investigator)
Siavashi, Faezeh (Co-Investigator)
Ashraf, Adnan (Co-Investigator)

15 Conference contribution
4 Article
1 Chapter
1 Review Article or Literature Review

4 results

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2020
Using Deep Reinforcement Learning for Exploratory Performance Testing of Software Systems with Multi-Dimensional Input Spaces
Ahmad, T., Ashraf, A., Truscan, D., Domi, A. & Porres, I., 2020, In: IEEE Access. 8, p. 195000-195020 21 p.
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
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7 Citations (Scopus)

34 Downloads (Pure)
2019

On a tool-supported model-based approach for building architectures and roadmaps: The MegaM@Rt2 project experience
Sadovykh, A., Afzal, W., Truscan, D., Pierini, P., Bruneliere, H., Bagnato, A., Gómez, A., Cabot, J. & Avila-García, O., 2019, In: Microprocessors and Microsystems. 71, p. –
Research output: Contribution to journal › Article › Scientific › peer-review

File

2 Citations (Scopus)

26 Downloads (Pure)
2018

Identifying Worst-Case User Scenarios for Performance Testing of Web Applications Using Markov-Chain Workload Models
Ahmad, T., Truscan, D. & Porres Paltor, I., 2018, In: Future Generation Computer Systems. 87, p. 910–920
Research output: Contribution to journal › Article › Scientific › peer-review

File

11 Citations (Scopus)

64 Downloads (Pure)
The MegaM@Rt2 ECSEL project : MegaModelling at Runtime – Scalable model-based framework for continuous development and runtime validation of complex systems
Afzal, W., Bruneliere, H., Di Ruscio, D., Sadovykh, A., Mazzini, S., Cariou, E., Truscan, D., Cabot, J., Gómez, A., Gorroñogoitia, J., Pomante, L. & Smrz, P., 2018, In: Microprocessors and Microsystems. 61, p. 86–95
Research output: Contribution to journal › Article › Scientific › peer-review

Open Access
File

28 Citations (Scopus)

29 Downloads (Pure)

MegaModelling at Runtime

Research output

Search results

Using Deep Reinforcement Learning for Exploratory Performance Testing of Software Systems with Multi-Dimensional Input Spaces

On a tool-supported model-based approach for building architectures and roadmaps: The MegaM@Rt2 project experience

Identifying Worst-Case User Scenarios for Performance Testing of Web Applications Using Markov-Chain Workload Models

The MegaM@Rt2 ECSEL project : MegaModelling at Runtime – Scalable model-based framework for continuous development and runtime validation of complex systems