This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a technique, called SEQUENCER, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary problem that occurs with big code. Our system is data-driven; we train it on 35,578 samples, carefully curated from commits to open-source repositories. We evaluate SEQUENCER on 4,711 independent real bug fixes, as well on the Defects4J benchmark used in program repair research. SEQUENCER is able to perfectly predict the fixed line for 950/4,711 testing samples, and find correct patches for 14 bugs in Defects4J benchmark. SEQUENCER captures a wide range of repair operators without any domain-specific top-down design.
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This content will become publicly available on October 23, 2024
Acto: Automatic End-to-End Testing for Operation Correctness of Cloud System Management
Cloud systems are increasingly being managed by operation programs termed operators, which automate tedious,
human-based operations. Operators of modern management platforms like Kubernetes, Twine, and ECS implement declarative interfaces based on the state-reconciliation principle. An operation declares a desired system state and the operator automatically reconciles the system to that declared state.
Operator correctness is critical, given the impacts on system operations—bugs in operator code put systems in undesired or error states, with severe consequences. However, validating operator correctness is challenging due to the enormous system-state space and complex operation interface. A correct operator must not only satisfy correctness properties of its own code, but it must also maintain managed systems in desired states. Unfortunately, end-to-end testing of operators significantly falls short.
We present Acto, the first automatic end-to-end testing technique for cloud system operators. Acto uses a statecentric approach to test an operator together with a managed system. Acto continuously instructs an operator to reconcile a system to different states and checks if the system successfully reaches those desired states. Acto models operations as state transitions and systematically realizes state-transition sequences to exercise supported operations in different scenarios. Acto’s oracles automatically check whether a system’s state is as desired. To date, Acto has helped find 56 serious new bugs (42 were confirmed and 30 have been fixed) in eleven Kubernetes operators with few false alarms.
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- NSF-PAR ID:
- 10467388
- Publisher / Repository:
- ACM
- Date Published:
- Format(s):
- Medium: X
- Location:
- ACM Symposium on Operating Systems Principles
- Sponsoring Org:
- National Science Foundation
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