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Resource leaks are a common and elusive source of bugs that can result in crashes and security vulnerabilities. The most effective technique to identify such leaks during development is static analysis. However, empirical studies show that in addition to leak warnings, developers often need help in the form of automated fix suggestions to correctly repair such leaks. The only existing tool that can suggest resource-leak fixes is the general-purpose tool Footpatch. Footpatch, however, performs poorly at this task; it generates fixes for only 6% of the leaks, out of which only 27% are correct. In this paper, we introduce RLFixer, a specialized repair tool that generates high-quality fixes for resource leaks identified by any resource-leak detector. A major challenge for RLFixer is that the most general version of the resource-leak repair problem is at least as hard as compile-time object deallocation, a well-known hard problem for compilers. RLFixer tackles this issue by separating the resource-leaks that are infeasible for a compile-time tool to fix from those that are feasible to fix. RLFixer achieves this separation by using a new data-flow analysis of resource objects to classify how they escape the context of their methods. The same analysis also enables RLFixer to generate correct repairs for the feasible-to-fix leaks. RLFixer is demand-driven and hence only analyzes statements relevant to the leak, thereby keeping overhead low. We evaluated RLFixer by applying it to warnings generated by five popular Java resource-leak detectors. We show that, on average, RLFixer generates repairs for 66% of their warnings, out of which 95% are correct. It has an average repair time of 14 seconds
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RESIN: A Holistic Service for Dealing with Memory Leaks in Production Cloud Infrastructure
Memory leak is a notorious issue. Despite the extensive efforts, addressing memory leaks in large production cloud systems remains challenging. Existing solutions incur high overhead and/or suffer from high inaccuracies. This paper presents RESIN, a solution designed to holistically address memory leaks in production cloud infrastructure. RESIN takes a divide-and-conquer approach to tackle the challenges. It performs a low-overhead detection first with a robust bucketization-based pivot scheme to identify suspicious leaking entities. It then takes live heap snapshots at appropriate time points in carefully sampled leak entities. RESIN analyzes the collected snapshots for leak diagnosis. Finally, RESIN automatically mitigates detected leaks. RESIN has been running in production in Microsoft Azure for 3 years. It reports on average 24 leak tickets each month with high accuracy and low overhead, and provides effective diagnosis reports. Its results translate into a 41× reduction of VM reboots caused by low memory.
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- Award ID(s):
- 1942794
- PAR ID:
- 10343367
- Date Published:
- Journal Name:
- 16th USENIX Symposium on Operating Systems Design and Implementation
- Page Range / eLocation ID:
- 109-125
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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