Exploring Performance of Configurable Software Systems: the JHipster Case Study
Résumé
The performance of software systems remains a key concern in software engineering. Configurable software systems, with their numerous configurations, complicate the performance evaluation process. This paper investigates the impact of web stack configurations on performance, using the JHipster web stack generator as a case study. We analyze JHipster configurations to understand how component choices influence system performance and explore individual configuration options for their specific effects. Our study shows that correlations across performance indicators exist but are often weak, and different options affect performance unevenly, with some impacting one indicator minimally while significantly influencing another. We developed a performance model for JHipster to automate the identification of configurations optimized for specific metrics, identifying four configurations that outperform the current default. Overall, this study underscores JHipster’s relevance as a use case for studying component-level variability in software systems and highlights the importance of selecting configurations based on performance indicators rather than preferred technologies.
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