Deep Software Variability for Resilient Performance Models of Configurable Systems
Luc Lesoil · HAL (Le Centre pour la Communication Scientifique Directe) · 2023
Software systems are heavily configurable, in the sense that users can adapt them according to their needs thanks to configurations. But not all configurations are equals, and some of them will clearly be more efficient than others in terms of performance. For human beings, it is quite complex to handle all the possible configurations of a system and to choose among one of them to reach a performance goal. Research work have shown that machine learning can bridge this gap and predict the performance value of a software systems based on its configurations. Problem. These techniques do not include the executing environment as part of the training data, while it could interact with the different configuration options and change their related performance distribution. In short, our machine learning models are too simple and will not be useful or applicable for end-users. Contributions. In this thesis, we first propose the term deep variability to refer to the existing interactions between the environment and the configurations of a software system, altering its performance distribution. We then empirically demonstrate the existence of deep variability and propose few solutions to tame the related issues. Finally, we prove that machine learning models can be adapted to be by-design robust to deep variability.