The insights from this study on JHipster can be broadly applied to other configurable software systems by leveraging the identified correlations between configuration options and performance metrics. By understanding how specific configurations impact performance indicators such as response time, power consumption, and resource utilization, developers can adopt a systematic approach to optimize their own systems.
Performance Modeling: Other software systems can benefit from creating performance models similar to the one developed for JHipster. These models can help predict how different configurations will affect performance, allowing developers to make informed decisions when selecting options.
Exhaustive Analysis Framework: The methodology used in this study, which involved an exhaustive analysis of configurations, can be adapted to other systems. By systematically testing various configurations, developers can identify optimal settings that enhance performance across multiple metrics.
Correlation Analysis: The study highlights the importance of understanding correlations between performance indicators. Other systems can implement similar correlation analyses to identify proxy indicators, which can simplify performance assessments and guide configuration choices.
Configuration Recommendations: The findings can inform the development of recommender systems that suggest optimal configurations based on desired performance outcomes. This can be particularly useful in environments where performance requirements are dynamic and vary based on user needs.
Focus on Specific Performance Objectives: By applying the insights regarding the impact of individual options on performance, developers can tailor their configurations to meet specific performance objectives, such as minimizing energy consumption or maximizing response speed.