The vast volume of "free" code maintained on open-source code management systems significantly simplifies the process of producing and sharing open-source software. Recently, we have seen a growing trend in which these open-source software is being used for neural code learning without authorization. Note that open-source software does not necessarily imply "unrestricted usage," e.g., software under the BSD license requires users to retain the copyright notice and credit the software's developers.The unauthorized use of software for (commercial) neural code learning models has raised copyright concerns. This paper, for the first time, provides approaches for protecting opensource software from unauthorized neural code learning via unlearnable examples. Our proposed technique applies a set of lightweight transformations toward a program before it is open-source released. When these transformed programs are used to train models, they mislead the model into learning the unnecessary knowledge of programs, then fail the model to complete original programs. The transformation methods are sophisticatedly designed to ensure that they do not impair the general readability of protected programs, nor do they entail a huge cost. We focus on code autocompletion as a representative downstream task of unauthorized neural code learning. We demonstrate highly encouraging and cost-effective protection against neural code autocompletion.

Debugging performance anomalies in real-world databases is challenging. Causal inference techniques enable qualitative and quantitative root cause analysis of performance downgrade. Nevertheless, causality analysis is practically challenging, particularly due to limited observability. Recently, chaos engineering has been applied to test complex real-world software systems. Chaos frameworks like Chaos Mesh mutate a set of chaos variables to inject catastrophic events (e.g., network slowdowns) to "stress" software systems. The systems under chaos stress are then tested using methods like differential testing to check if they retain their normal functionality (e.g., SQL query output is always correct under stress). Despite its ubiquity in the industry, chaos engineering is now employed mostly to aid software testing rather for performance debugging. This paper identifies novel usage of chaos engineering on helping developers diagnose performance anomalies in databases. Our presented framework, PERFCE, comprises an offline phase and an online phase. The offline phase learns the statistical models of the target database system, whilst the online phase diagnoses the root cause of monitored performance anomalies on the fly. During the offline phase, PERFCE leverages both passive observations and proactive chaos experiments to constitute accurate causal graphs and structural equation models (SEMs). When observing performance anomalies during the online phase, causal graphs enable qualitative root cause identification (e.g., high CPU usage) and SEMs enable quantitative counterfactual analysis (e.g., determining "when CPU usage is reduced to 45%, performance returns to normal"). PERFCE notably outperforms prior works on common synthetic datasets, and our evaluation on real-world databases, MySQL and TiDB, shows that PERFCE is highly accurate and moderately expensive.

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