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Modern web applications have stringent latency requirements while processing an ever-increasing amount of user data. To address these challenges and improve programmer productivity, Object Relational Mapping (ORM) frameworks have been developed to allow developers writing database processing code in an object-oriented manner. Despite such frameworks, prior work found that developers still struggle in developing performant ORM-based web applications. This paper presents PowerStation, a RubyMine IDE plugin for optimizing web applications developed using the Ruby on Rails ORM. Using automated static analysis, PowerStation detects ORMrelated inefficiency problems and suggests fixes to developers. Our evaluation on 12 real-world applications shows that PowerStation can automatically detects 1221 performance issues across all of them. We have uploaded a tutorial on using PowerStation plugin to https://youtu.be/v_uY5bjGuK0.
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How not to structure your database-backed web applications: a study of performance bugs in the wild
Many web applications use databases for persistent data storage, and using Object Relational Mapping (ORM) frameworks is a common way to develop such database-backed web applications. Unfortunately, developing efficient ORM applications is challenging, as the ORM framework hides the underlying database query generation and execution. This problem is becoming more severe as these applications need to process an increasingly large amount of persistent data. Recent research has targeted specific aspects of performance problems in ORM applications. However, there has not been any systematic study to identify common performance anti-patterns in real-world such applications, how they affect resulting application performance, and remedies for them. In this paper, we try to answer these questions through a comprehensive study of 12 representative real-world ORM applications. We generalize 9 ORM performance anti-patterns from more than 200 performance issues that we obtain by studying their bug-tracking systems and profiling their latest versions. To prove our point, we manually fix 64 performance issues in their latest versions and obtain a median speedup of 2× (and up to 39× max) with fewer than 5 lines of code change in most cases. Many of the issues we found have been confirmed by developers, and we have implemented ways to identify other code fragments with similar issues as well.
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- Award ID(s):
- 1546083
- PAR ID:
- 10067547
- Date Published:
- Journal Name:
- ICSE '18 Proceedings of the 40th International Conference on Software Engineering
- Page Range / eLocation ID:
- 800 to 810
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3180155.3180194
