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Impact of Extensions on Browser Performance: An Empirical Study on Google Chrome (2404.06827v1)

Published 10 Apr 2024 in cs.PF, cs.HC, and cs.SE

Abstract: Web browsers have been used widely by users to conduct various online activities, such as information seeking or online shopping. To improve user experience and extend the functionality of browsers, practitioners provide mechanisms to allow users to install third-party-provided plugins (i.e., extensions) on their browsers. However, little is known about the performance implications caused by such extensions. In this paper, we conduct an empirical study to understand the impact of extensions on the user-perceived performance (i.e., energy consumption and page load time) of Google Chrome, the most popular browser. We study a total of 72 representative extensions from 11 categories (e.g., Developer Tools and Sports). We observe that browser performance can be negatively impacted by the use of extensions, even when the extensions are used in unintended circumstances (e.g., when logging into an extension is not granted but required, or when an extension is not used for designated websites). We also identify a set of factors that significantly influence the performance impact of extensions, such as code complexity and privacy practices (i.e., collection of user data) adopted by the extensions. Based on our empirical observations, we provide recommendations for developers and users to mitigate the performance impact of browser extensions, such as conducting performance testing and optimization for unintended usage scenarios of extensions, or adhering to proper usage practices of extensions (e.g., logging into an extension when required).

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Authors (3)
  1. Bihui Jin (3 papers)
  2. Heng Li (138 papers)
  3. Ying Zou (23 papers)
Citations (1)
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Summary

Impact of Extensions on Browser Performance: An Empirical Study on Google Chrome

Introduction to the Study

The comprehensive research conducted by Jin, Li, and Zou offers an in-depth examination of the nuanced ways in which browser extensions, specifically within Google Chrome, influence the user-perceived performance metrics of browsers: page load time and energy consumption. Distinguishing itself by its breadth, this paper critically explores not only the immediate impact of extension usage but also explores the effects varying activation modes and extension-specific factors have on energy efficiency and speed.

Study Design and Methodology

The paper's robust experimental design meticulously selected 72 extensions from a pool of 110,240, spanning 11 categories. This selection process aimed at inclusivity, covering a wide spectrum of extension functionalities. The research scrutinized extensions across different phases: page load and post-load (stabilized consumption) energy use, assessing the impact on performance metrics. These assessments were executed against a backdrop of seven distinct user scenario settings, further deepening the paper's investigational rigor.

Collecting and Clustering Extensions

The initial phase involved collecting extensions from the Chrome Web Store, subsequently clustering them based on their declared privacy practices—information crucial for understanding each extension's potential performance impact. This meticulous process ensured a diverse and representative sample for the experimental analysis.

Designing Testing Scenarios

A thoughtful approach led to the creation of seven tailored testing scenarios, closely mirroring real-world user interactions with these extensions. Such detailed preparation underscores the paper's commitment to producing relevant and applicable findings.

Performance Measurement Strategy

Leveraging Running Average Power Limit (RAPL) for energy measurements alongside Selenium for automating user interaction scenarios, the paper maintained a high degree of accuracy and reproducibility in its experimental procedures. These measurements spanned critical performance indicators, including page load time and energy consumption, presenting a comprehensive view of the performance impact across different usage contexts.

Key Findings and Insights

The Dual Impact of Extensions

Extensions showcased a dual capacity to both enhance and degrade browser performance, with a pronounced tendency towards inducing higher energy consumption during page load. This nuanced view underscores the complexity of extensions' influence on browser performance, challenging the notion of a one-size-fits-all impact.

Activation Modes Matter

A groundbreaking insight of this paper is the discernible performance differential triggered by varying activation modes of extensions. Unexpectedly, even inactive extensions or those operating in non-designated modes exhibited a significant performance impact, underlining the importance of context and usage patterns in assessing extension performance.

Influential Factors on Performance

Through a sophisticated analysis employing elastic net regression models, the paper illuminated key factors influencing browser performance, notably including the number of functions, privacy practices, and specific file types within extensions. These factors offer actionable insights for developers aiming to optimize extension performance.

Theoretical and Practical Implications

This empirical investigation enriches the academic discourse on browser extensions, offering a granular understanding of how different elements and usage scenarios affect performance. Practically, it equips developers with data-driven insights to refine extensions, enhancing efficiency and usability. Furthermore, it empowers users to make informed decisions regarding extension usage, balancing functionality with performance considerations.

Future Directions in Browser Extension Research

Building on this foundation, future research could explore the longitudinal impact of extensions on browser performance, considering evolving web standards and user expectations. Additionally, investigations into the interplay between extension-induced performance variations and user behavior patterns could yield further refinements in extension development and optimization practices.

Conclusion

In conclusion, Jin, Li, and Zou's work establishes a nuanced understanding of browser extensions' impact on performance within Google Chrome, marked by methodological rigor and a comprehensive analytical lens. Their findings pave the way for enhanced user experiences and extension development practices, highlighting the intricate balance between added functionality and performance overhead.

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