Performance Issues and Optimizations in JavaScript: An Empirical Study

As JavaScript is becoming increasingly popular, the performance of JavaScript programs is crucial to ensure the responsiveness and energy-efficiency of thousands of pro- grams. Yet, little is known about performance issues that developers face in practice and they address these issues. This paper pr...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Proceedings / International Conference on Software Engineering S. 61 - 72
Hauptverfasser: Selakovic, Marija, Pradel, Michael
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: ACM 01.05.2016
Schlagworte:
Computer bugs
Computer science
Empirical study
Engines
JavaScript
Libraries
Optimization
Performance issue
Reliability
Servers
ISSN:1558-1225
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:As JavaScript is becoming increasingly popular, the performance of JavaScript programs is crucial to ensure the responsiveness and energy-efficiency of thousands of pro- grams. Yet, little is known about performance issues that developers face in practice and they address these issues. This paper presents an empirical study of 98 fixed performance issues from 16 popular client-side and server-side JavaScript projects. We identify eight root causes of issues and show that inefficient usage of APIs is the most prevalent root cause. Furthermore, we find that most is- sues are addressed by optimizations that modify only a few lines of code, without significantly affecting the complexity of the source code. By studying the performance impact of optimizations on several versions of the SpiderMonkey and V8 engines, we find that only 42.68% of all optimizations improve performance consistently across all versions of both engines. Finally, we observe that many optimizations are instances of patterns applicable across projects, as evidenced by 139 previously unknown optimization opportunities that we find based on the patterns identified during the study. The results of the study help application developers to avoid common mistakes, researchers to develop performance-related techniques that address relevant problems, and engine developers to address prevalent bottleneck patterns.
ISSN:1558-1225
DOI:10.1145/2884781.2884829