Finding Bugs using Path-Sensitive Static Analysis Gábor Horváth Gabor.Horvath@microsoft.com @XazaxHunWelcome to CppCon 2021! Join #visual_studio channel on CppCon Discord https://aka.ms/cppcon/discord latest announcements Take our survey https://aka.ms/cppconAgenda • Intro to path-sensitive static analysis • Path-sensitive checks in MSVC • A look under the hood • Upcoming features • Lessons learned2012 -> Unknown p -> Null p -> MaybeNull p -> MaybeNull Warning Unknown Null NotNull MaybeNull Analysis state Transition semi-lattice• Some paths are infeasible: • Not taking branch 1, but taking branch

Apache Cassandra static column 介绍与实战 Spark大数据博客 - https://www.iteblog.com Apache Cassandra static column 介绍与实战 假设我们有这样的场景：我们想在 Cassandra 中使用一张表记录用户基本信息（比如 email、密 码等）以及用户状态更新。我们知道，用户的基本信息一般很少会变动，但是状态会经常变化， ，Cassandra 引入了 static column。同一个 partition key 中被声明为 static 的列只有一个值的，也就是只存储一份。 如果想及时了 解Spark、Hadoop或者Hbase相关的文章，欢迎关注微信公共帐号：iteblog_hadoop 定义 static column 在表中将某个列定义为 STATIC 很简单，只需要在列的最后面加上 STATIC 关键字，具体如下： "email" text STATIC, "encrypted_password" blob STATIC, "body" text, PRIMARY KEY ("username", "id") ); iteblog_users_with_status_updates 表中我们将 email 和 encrypted_password 两个字段设置为 STATIC 了，这意味着同一个 username

第三届中国Rust开发者大会 Rustle: the first static analyzer for smart contracts in Rust Matthew Jiang Director of Security Team @ BlockSec Motivation Background Design 1 2 3 Capability Usability Conclusion Front-end Clang Rustc Gollvm LLVM IR Back-end x86 ARM BPF WASM LLVM Pass: rich APIs to analysis the LLVM IR Background Near Blockchains with smart contracts in Rust Rust Web Assembly Workflow Design Compile Smart Contracts LLVM IR LLVM Pass Extracted Patterns Pattern Match Analysis Results Design #[private] pub fn callback_withdraw(&mut self, amount: U128) { match env::promise_result(0)

https://www.meetup.com/St-Petersburg-CPP-User- Group/ ● C++ Russia: https://cppconf.ru/en/Why Code Analysis?Software QualityReadability Maintainability tools fuzzer battery life Repeatable tests Undefined Behavior – Fun with NULL pointers, part 1: https://lwn.net/Articles/342330/Why code analysis – ● Improve software quality ● Lower developer frustration ● Avoid UBLanguageLanguage helps Built-in compiler check ○ Current LLVM implementation gives 5% overhead ○ Annotations to help analysis: gsl::SharedOwner, gsl::Owner, gsl::Pointer void sample1() { int* p = nullptr; {

1 Competitor Analysis: KubeSphere vs. Rancher and OpenShift September 2021 2 Table of Contents Competitor Analysis: KubeSphere vs. Rancher and OpenShift........................................1 integration of SonarQube for static code analysis supported and analysis results available on the UI Manual configurations required to set up SonarQube for static code analysis Manual configurations configurations required to set up SonarQube for static code analysis Application Application deployment App Store available to support Helm Chart and application repository configurations Operator Hub

Lecture Notes on Gaussian Discriminant Analysis, Naive Bayes and EM Algorithm Feng Li fli@sdu.edu.cn Shandong University, China 1 Bayes’ Theorem and Inference Bayes’ theorem is stated mathematically through parameters θ = {P(X = x | Y = y), P(Y = y)}x,y. 2 Gaussian Discriminant Analysis In Gaussian Discriminate Analysis (GDA) model, we have the following as- sumptions: • A1: Y ∼ Bernoulli(ψ): Y follows ˜y | X = ˜x) = pY |X(˜y | ˜x) = p(˜x | ˜y)p(˜y) p(˜x) where ˜y = 0, 1. 3 Gaussian Discriminant Analysis and Logistic Regression By far, we introduce two classification algorithms, Logistic Regression

pandas: powerful Python data analysis toolkit Release 0.19.0 Wes McKinney & PyData Development Team Oct 02, 2016 CONTENTS 1 What’s New 3 1.1 v0.19.0 (October 2, 2016) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376 7.4 Practical data analysis with Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376 7.5 Modern . . . . . . . . . . . . . . 1888 Python Module Index 1891 xliv pandas: powerful Python data analysis toolkit, Release 0.19.0 PDF Version Zipped HTML Date: Oct 02, 2016 Version: 0.19.0 Binary Installers:

pandas: powerful Python data analysis toolkit Release 0.19.1 Wes McKinney & PyData Development Team Nov 03, 2016 CONTENTS 1 What’s New 3 1.1 v0.19.1 (November 3, 2016) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378 7.4 Practical data analysis with Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378 7.5 Modern . . . . . . . . . . . . . . 1895 Python Module Index 1897 xliv pandas: powerful Python data analysis toolkit, Release 0.19.1 PDF Version Zipped HTML Date: Nov 03, 2016 Version: 0.19.1 Binary Installers:

pandas: powerful Python data analysis toolkit Release 0.20.3 Wes McKinney & PyData Development Team Jul 07, 2017 CONTENTS 1 What’s New 3 1.1 v0.20.3 (July 7, 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 6.4 Practical data analysis with Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424 6.5 Exercises . . . . . . . . . . . . . . 1995 Python Module Index 1997 xlvi pandas: powerful Python data analysis toolkit, Release 0.20.3 PDF Version Zipped HTML Date: Jul 07, 2017 Version: 0.20.3 Binary Installers: