SQLite 文档的学习笔记（2）测试方法

在 这篇文章 里，提到了 SQLite 采用了航空级别的测试过程。本文详细介绍了这一测试过程，SQLite 是世界上部署范围最广的 SQL 数据库引擎，显而易见，每次发布都会影响极广。如果没有一个完善的测试机制，则对每一次发布不会有足够的信心。

可惜这些文档的中文版，看起来是使用机器翻译的。我打算在这里以读书笔记的方式，给出一份自己翻译的中文版。

学习材料： How SQLite Is Tested ， 中文版：如何测试SQLite

1. 引言

1. Introduction
   The reliability and robustness of SQLite is achieved in part by thorough and careful testing. As of version 3.39.0 (2022-06-25), the SQLite library consists of approximately 151.3 KSLOC of C code. (KSLOC means thousands of “Source Lines Of Code” or, in other words, lines of code excluding blank lines and comments.) By comparison, the project has 608 times as much test code and test scripts - 92038.3 KSLOC.

SQLite 的可靠与健壮，有一部分来自深入彻底、悉心设计的测试。

到 2022年6月25日发布的 3.39.0 版本时，SQLite 库的规模大约是 151.3 KSLOC（15.13 万行）C语言源码，KSLOC 是指“千行源文件” ，意味着包括了空行、注释等等在内的统计。

相比之下，测试代码和脚本规模是 92038.3 KSLOC（9203.83万行），是上述库文件的 608 倍之多。

1.1 Executive Summary

• Four independently developed test harnesses
• 100% branch test coverage in an as-deployed configuration
• Millions and millions of test cases
• Out-of-memory tests
• I/O error tests
• Crash and power loss tests
• Fuzz tests
• Boundary value tests
• Disabled optimization tests
• Regression tests
• Malformed database tests
• Extensive use of assert() and run-time checks
• Valgrind analysis
• Undefined behavior checks
• Checklists

测试内容提要

• 4种独立开发的测试套件
• 按实际部署配置下的 100% 分支覆盖率
• 数量达几百万的测试用例规模
• 内存不足测试
• I/O 异常测试
• 崩溃和掉电测试
• 模糊测试
• 边界值测试
• 禁用优化测试
• 回归测试
• 数据格式错误测试
• 大量使用断言和程序运行时检查
• Valgrind 测试
• 预设行为之外的测试
• 检查清单

2. 测试套件

2. Test Harnesses
   There are four independent test harnesses used for testing the core SQLite library. Each test harness is designed, maintained, and managed separately from the others.

SQLite 核心库的测试，有4套相互独立开发的工具完成。每一套工具的设计、维护、管理过程，都和其他三种互不相干。这四种测试工具套件如下：

• TCL 测试集
• TH3 测试集
• SQL 逻辑测试
• dbsqlfuzz

The TCL Tests are the original tests for SQLite. They are contained in the same source tree as the SQLite core and like the SQLite core are in the public domain. The TCL tests are the primary tests used during development. The TCL tests are written using the TCL scripting language. The TCL test harness itself consists of 27.7 KSLOC of C code used to create the TCL interface. The test scripts are contained in 1343 files totaling 23.5MB in size. There are 50240 distinct test cases, but many of the test cases are parameterized and run multiple times (with different parameters) so that on a full test run millions of separate tests are performed.

TCL 测试集是 SQLite 最初的测试集合，和 SQLite 核心库的源代码在同一套代码库中，并且一起公开。在 SQLite 开发过程中，主要使用 TCL 测试集做测试。TCL 测试集使用 TCL 脚本语言编写。TCL 测试集中，为了创建 TCL 界面的 C 语言源代码有 27.7 千行。测试脚本共有 1343 个文件，共有 23.5 MB 大小。从测试集规模上说，有50240个独立的测试用例。而且，这其中有许多测试用例是高度参数化的，为了遍历参数集，一条测试用例会执行很多次。因此，一次完整的测试运行会包含数百万次的用例执行。

The TH3 test harness is a set of proprietary tests, written in C that provide 100% branch test coverage (and 100% MC/DC test coverage) to the core SQLite library. The TH3 tests are designed to run on embedded and specialized platforms that would not easily support TCL or other workstation services. TH3 tests use only the published SQLite interfaces. TH3 consists of about 75.7 MB or 1038.0 KSLOC of C code implementing 49116 distinct test cases. TH3 tests are heavily parameterized, though, so a full-coverage test runs about 2.3 million different test instances. The cases that provide 100% branch test coverage constitute a subset of the total TH3 test suite. A soak test prior to release does hundreds of millions of tests. Additional information on TH3 is available separately.

TH3 测试集，则是一套 SQLite 内部私有的测试集，用 C 语言编写，提供了针对 SQLite 核心库的 100% 的分支覆盖率、和 100% 的 MC/DC 测试覆盖率（注：MC/DC 是 Modified Condition/Decision Coverage 的简称，航空工业软件会要求这种覆盖测试）。设计编写 TH3 测试集的原由，是为那些不方便运行 TCL 的嵌入式系统或者专用系统平台上能够进行测试。TH3 测试仅使用已发布的 SQLite 接口。TH3 测试集文件的体积有 75.7 MB ，代码规模有 1038.0 KSLOC 的 C 代码，实现了 49116 个不同的测试用例。和上面的 TCL 测试集一样，TH3 测试是高度参数化的，因此全覆盖测试运行大约 230 万个不同的测试运行实例。TH3 测试套件中有一个用例子集，能提供 100% 的分支覆盖率。发布前的浸泡测试会进行数亿次测试。有关 TH3 的更多信息可通过单独途径获得。

SQL Logic Test or SLT test harness is used to run huge numbers of SQL statements against both SQLite and several other SQL database engines and verify that they all get the same answers. SLT currently compares SQLite against PostgreSQL, MySQL, Microsoft SQL Server, and Oracle 10g. SLT runs 7.2 million queries comprising 1.12GB of test data.

SQL 逻辑测试 ，或者简称为 “SLT 测试” ，包含巨量的 SQL 语句，分别向 SQLite 和其他若干 SQL 数据库查询，并对比验证是否能得到相同的答案。目前， SLT 测试将 SQLite 与 PostgreSQL、MySQL、Microsoft SQL Server 和 Oracle 10g 进行比较。一次 SLT 测试运行 720 万次查询，包括 1.12GB 的测试数据。

The dbsqlfuzz engine is a proprietary fuzz tester. Other fuzzers for SQLite mutate either the SQL inputs or the database file. Dbsqlfuzz mutates both the SQL and the database file at the same time, and is thus able to reach new error states. Dbsqlfuzz is built using the libFuzzer framework of LLVM with a custom mutator. There are 303 seed files. The dbsqlfuzz fuzzer runs about one billion test mutations per day. Dbsqlfuzz helps ensure that SQLite is robust against attack via malicious SQL or database inputs.

dbsqlfuzz 引擎是一个 SQLite 内部私有的模糊测试器。SQLite 的一般模糊器会要么只改变 SQL 输入或者只改变数据库文件。Dbsqlfuzz 同时改变 SQL 和数据库文件，因此能够产生前所未有的程序错误。Dbsqlfuzz 使用 LLVM 的 libFuzzer 框架和自定义 mutator 创建。有 303 个种子文件。dbsqlfuzz fuzzer 每天运行大约 10 亿个测试突变。Dbsqlfuzz 有助于确保 SQLite 能够抵御来自恶意 SQL 或其他数据库输入的攻击。

In addition to the four main test harnesses, there several other small programs that implement specialized tests.

除了上述四个主要的测试工具之外，还有其他若干小程序用来专项测试。

The “speedtest1.c” program estimates the performance of SQLite under a typical workload.
The “mptester.c” program is a stress test for multiple processes concurrently reading and writing a single database.
The “threadtest3.c” program is a stress test for multiple threads using SQLite simultaneously.
The “fuzzershell.c” program is used to run some fuzz tests.
All of the tests above must run successfully, on multiple platforms and under multiple compile-time configurations, before each release of SQLite.

speedtest1.c 程序用来评估 SQLite 在正常负载下的性能表现。

mptester.c 程序用来模拟多个进程对同一个数据库并发读写的性能表现。

threadtest3.c 程序用来模拟多个线程同时使用 SQLite 的压力测试。

fuzzershell.c 程序用来进行一些模糊测试。

上述所有测试必须在所有平台、多种编译条件下全部通过才能正式发布 SQLite 版本。

Prior to each check-in to the SQLite source tree, developers typically run a subset (called “veryquick”) of the Tcl tests consisting of about 300.2 thousand test cases. The veryquick tests include most tests other than the anomaly, fuzz, and soak tests. The idea behind the veryquick tests are that they are sufficient to catch most errors, but also run in only a few minutes instead of a few hours.

在开发人员每次提交代码之前，一般会运行一套 Tcl 测试集（称为 veryquick 测试），其中包含大约 30.02 万个测试用例。veryquick 测试包括了除异常、模糊和浸泡测试之外的大多数测试。veryquick 测试的意图是它们能以较短的时间覆盖大多数错误，执行时间只有几分钟，而非几个小时。

3. 异常测试

3. Anomaly Testing
   Anomaly tests are tests designed to verify the correct behavior of SQLite when something goes wrong. It is (relatively) easy to build an SQL database engine that behaves correctly on well-formed inputs on a fully functional computer. It is more difficult to build a system that responds sanely to invalid inputs and continues to function following system malfunctions. The anomaly tests are designed to verify the latter behavior.

异常测试的目的是验证在出现异常情况时，SQLite 能否做出正确的行为。建造一个在功能正常的计算机上能处理正确格式的 SQL 的数据库引擎是（相对）容易的，相比之下，构建一个对无效输入做出合理响应并在系统故障后继续运行的系统则更加困难。异常测试针对的是后一种情况。

内存不足测试

3.1. Out-Of-Memory Testing
SQLite, like all SQL database engines, makes extensive use of malloc() (See the separate report on dynamic memory allocation in SQLite for additional detail.) On servers and workstations, malloc() never fails in practice and so correct handling of out-of-memory (OOM) errors is not particularly important. But on embedded devices, OOM errors are frighteningly common and since SQLite is frequently used on embedded devices, it is important that SQLite be able to gracefully handle OOM errors.

与其他 SQL 数据库引擎一样，SQLite 大量使用 malloc()（分配内存函数，详情请参阅关于 SQLite 动态内存分配的章节）。当 SQLite 运行在服务器和工作站时（注：这时的内存是海量的），malloc() 函数从不会分配失败，所以这种情况下，能否正确处理内存不足 (OOM) 错误并不是特别重要。但是实际上，SQLite 更为常见的是在嵌入式设备上使用，嵌入式设备上的内存不足 (OOM) 现象非常普遍，因此 SQLite 能否优雅地处理 OOM 错误就非常重要。

OOM testing is accomplished by simulating OOM errors. SQLite allows an application to substitute an alternative malloc() implementation using the sqlite3_config(SQLITE_CONFIG_MALLOC,…) interface. The TCL and TH3 test harnesses are both capable of inserting a modified version of malloc() that can be rigged to fail after a certain number of allocations. These instrumented mallocs can be set to fail only once and then start working again, or to continue failing after the first failure. OOM tests are done in a loop. On the first iteration of the loop, the instrumented malloc is rigged to fail on the first allocation. Then some SQLite operation is carried out and checks are done to make sure SQLite handled the OOM error correctly. Then the time-to-failure counter on the instrumented malloc is increased by one and the test is repeated. The loop continues until the entire operation runs to completion without ever encountering a simulated OOM failure. Tests like this are run twice, once with the instrumented malloc set to fail only once, and again with the instrumented malloc set to fail continuously after the first failure.

OOM 异常测试是通过模拟产生 OOM 错误来实现的。SQLite 有一项配置，可以让程序不使用默认原始的 malloc() ，而是用一个受控的 malloc() 的函数替代。在上述的 TCL测试工具 和 TH3 测试工具中，都可以使用修改了的 malloc() 版本，目的是在分配内存达到一定次数后故意失败。这些受控的 malloc 行为可以设置为失败一下，然后恢复正常状态，或者在第一次失败后保持失败状态。OOM 测试是个循环过程。在第一次循环迭代中，受控的 malloc 被指定在第一次分配内存时即报告失败。接着执行一些 SQLite 操作并检查SQLite 的行为，以确保 SQLite 能正确处理 OOM 错误。然后这个受控的 malloc 函数内部的故障计数器递增，并再次开始测试。循环继续，直到不会遇到任何模拟的 OOM 故障为止。像这样的测试会运行两次，一次将 malloc 设置为仅失败一次后继续正常工作，第二次将 malloc 设置为在第一次失败后保持失败状态。

I/O 异常测试

3.2. I/O Error Testing I/O error testing seeks to verify that SQLite responds sanely to failed I/O operations. I/O errors might result from a full disk drive, malfunctioning disk hardware, network outages when using a network file system, system configuration or permission changes that occur in the middle of an SQL operation, or other hardware or operating system malfunctions. Whatever the cause, it is important that SQLite be able to respond correctly to these errors and I/O error testing seeks to verify that it does.

3.2. I/O 异常测试，旨在验证 SQLite 对失败的 I/O 操作能否做出合理的响应。I/O 错误的原因可能有磁盘空间满、磁盘硬件故障、使用网络文件系统时网络中断、SQL 操作过程中发生的系统配置或权限更改或其他硬件或操作系统故障。不管具体原因是什么，I/O 异常测试的目的就是验证 SQLite 能够针对这些错误做出正确的反应，这些是至关重要的。

I/O error testing is similar in concept to OOM testing; I/O errors are simulated and checks are made to verify that SQLite responds correctly to the simulated errors. I/O errors are simulated in both the TCL and TH3 test harnesses by inserting a new Virtual File System object that is specially rigged to simulate an I/O error after a set number of I/O operations. As with OOM error testing, the I/O error simulators can be set to fail just once, or to fail continuously after the first failure. Tests are run in a loop, slowly increasing the point of failure until the test case runs to completion without error. The loop is run twice, once with the I/O error simulator set to simulate only a single failure and a second time with it set to fail all I/O operations after the first failure.

从概念上讲，I/O 异常测试的具体做法和 OOM 异常测试是类似的：模拟产生 I/O 异常并进行验证以检查 SQLite 是否能做出正确的反应。在 TCL 和 TH3 测试工具中，通过新插入一个虚拟文件系统对象来模拟 I/O 异常，该对象专门用于在 I/O 动作达到一定次数后，模拟产生 I/O 异常事件。与 OOM 异常测试一样，I/O 异常模拟可以设置为失败一次后恢复正常，或在第一次失败后继续保持失败状态。测试是个循环过程，在逐次循环迭代，缓慢增加故障点的数量，直到所有的测试用例全部运行完成且没有错误。测试循环将执行两次，一次是 I/O 异常仅模拟单次故障后即恢复正常，一次是在出现故障后，所有 I/O 操作都报告失败。

In I/O error tests, after the I/O error simulation failure mechanism is disabled, the database is examined using PRAGMA integrity_check to make sure that the I/O error has not introduced database corruption.

在I/O 异常测试里，当 I/O 异常模拟被停用后，会使用 PRAGMA 完整性检查以确保 I/O 错误没有导致数据库损坏。

崩溃异常测试

3.3. Crash Testing Crash testing seeks to demonstrate that an SQLite database will not go corrupt if the application or operating system crashes or if there is a power failure in the middle of a database update. A separate white-paper titled Atomic Commit in SQLite describes the defensive measure SQLite takes to prevent database corruption following a crash. Crash tests strive to verify that those defensive measures are working correctly.

崩溃测试的目的是验证 SQLite 数据库在遇到程序或者操作系统崩溃时，或在数据库写入过程中出现电源故障等场景时，数据库不会损坏。有一份名为 Atomic Commit in SQLite 的白皮书描述了 SQLite 为防止崩溃后数据库损坏而采取的防御措施。崩溃测试的目的是验证这些防御措施是否能正常工作。

It is impractical to do crash testing using real power failures, of course, and so crash testing is done in simulation. An alternative Virtual File System is inserted that allows the test harness to simulate the state of the database file following a crash.

当然，用真实的电源故障进行崩溃测试并不具备可操作性，所以需要用模拟的方法。加入了一个替代虚拟文件系统，使测试工具能得到一个崩溃后数据库文件。

In the TCL test harness, the crash simulation is done in a separate process. The main testing process spawns a child process which runs some SQLite operation and randomly crashes somewhere in the middle of a write operation. A special VFS randomly reorders and corrupts the unsynchronized write operations to simulate the effect of buffered filesystems. After the child dies, the original test process opens and reads the test database and verifies that the changes attempted by the child either completed successfully or else were completely rolled back. The integrity_check PRAGMA is used to make sure no database corruption occurs.

在 TCL 测试工具中，模拟崩溃测试是在另一个进程中完成的。主测试进程产生一个子进程，这个子进程负责执行 SQLite 操作，同时在写操作中随机的制造程序崩溃。一个特殊的 VFS（Virtual File System）用于将那些未及时同步入数据库的写操作重新排序并破坏，目的是模拟缓冲文件系统的在崩溃时的行为。在子进程结束运行后，原先的测试主进程打开测试数据库，并读取数据，检查被测试子进程影响的过程是否仍然顺利地完成或者完整的回滚。使用 PRAGMA 完整性检查数据库没有发生损坏。

The TH3 test harness needs to run on embedded systems that do not necessarily have the ability to spawn child processes, so it uses an in-memory VFS to simulate crashes. The in-memory VFS can be rigged to make a snapshot of the entire filesystem after a set number of I/O operations. Crash tests run in a loop. On each iteration of the loop, the point at which a snapshot is made is advanced until the SQLite operations being tested run to completion without ever hitting a snapshot. Within the loop, after the SQLite operation under test has completed, the filesystem is reverted to the snapshot and random file damage is introduced that is characteristic of the kinds of damage one expects to see following a power loss. Then the database is opened and checks are made to ensure that it is well-formed and that the transaction either ran to completion or was completely rolled back. The interior of the loop is repeated multiple times for each snapshot with different random damage each time.

对 TH3 测试工具而言，因为是运行在嵌入式系统中，这种系统未必能产生子进程，因此使用内存中的 VFS （Virtual File System）来模拟崩溃。在一系列 I/O 操作之后，内存中的 VFS 用来制作整个文件系统的快照。崩溃测试是个循环过程，在每次循环中，制作快照的时间点都会比前一次循环推后一些，直到被测试的 SQLite 操作全部完成而没有命中快照。在一次循环中，每一次的 SQLite 操作结束后，将快照恢复到文件系统中，并随机地造成文件损坏，这是断电后常见的文件系统损坏。然后打开数据库并检查数据文件的格式是否仍然正确、数据库事务要么顺利地完成，要么完整地回滚。对于每一个快照，循环体会执行多次，而且每次都有不同位置的随机损坏。

复合异常测试

3.4. Compound failure tests The test suites for SQLite also explore the result of stacking multiple failures. For example, tests are run to ensure correct behavior when an I/O error or OOM fault occurs while trying to recover from a prior crash.

为了达到探索测试的目的，还会将几种异常的情况复合叠加。例如，可以在一次模拟程序崩溃的复原过程中，模拟产生 I/O 错误或 OOM 故障，并验证此时程序的行为是否能保持正常。

4. 模糊测试

4. Fuzz Testing Fuzz testing seeks to establish that SQLite responds correctly to invalid, out-of-range, or malformed inputs.

模糊测试的目的是确保 SQLite 针对无效的、或是越界的、或者错误格式的输入，能有正确的响应输出。

SQL Fuzz 模糊测试

4.1. SQL Fuzz SQL fuzz testing consists of creating syntactically correct yet wildly nonsensical SQL statements and feeding them to SQLite to see what it will do with them. Usually some kind of error is returned (such as “no such table”). Sometimes, purely by chance, the SQL statement also happens to be semantically correct. In that case, the resulting prepared statement is run to make sure it gives a reasonable result.

SQL Fuzz SQL 模糊测试使用语法虽然正确但含义荒谬的 SQL 语句，提供给 SQLite 以观察如何处理。正常情况下，会返回某种错误（例如“no such table”）。有的时候，SQL 语句偶然在语义上也是正确的。在这种情况下，一些预处理语句以确保它给出合理的结果。

使用 AFL 的 SQL Fuzz

4.1.1. SQL Fuzz Using The American Fuzzy Lop Fuzzer
The concept of fuzz testing has been around for decades, but fuzz testing was not an effective way to find bugs until 2014 when Michal Zalewski invented the first practical profile-guided fuzzer, American Fuzzy Lop or “AFL”. Unlike prior fuzzers that blindly generate random inputs, AFL instruments the program being tested (by modifying the assembly-language output from the C compiler) and uses that instrumentation to detect when an input causes the program to do something different - to follow a new control path or loop a different number of times. Inputs that provoke new behavior are retained and further mutated. In this way, AFL is able to “discover” new behaviors of the program under test, including behaviors that were never envisioned by the designers.

模糊测试的概念尽管已经存在了几十年，但是直到 2014 年 Michal Zalewski 才首次发明了一个实际可用的、使用配置文件的模糊器：American Fuzzy Lop 或者叫 “AFL”。与先前盲目生成随机输入的模糊器不同，AFL 通过修改 C 编译器的汇编语言输出的方法，修改调整被测试的程序，并使用该调整手段来寻找导致程序改变输出的时机，以寻找一个不同的控制路径或恰当的循环次数。能够让程序改变输出的输入数据被保存下来，并在此基础上进一步异化。通过这种方式，AFL 能够“探索”被测程序的未知行为，甚至包括设计者从未设想到过的行为。

AFL proved adept at finding arcane bugs in SQLite. Most of the findings have been assert() statements where the conditional was false under obscure circumstances. But AFL has also found a fair number of crash bugs in SQLite, and even a few cases where SQLite computed incorrect results.

事实证明，AFL 擅长在 SQLite 中发现神秘的 bug。大多数 bug 都是在难以想象的条件下被 assert() 语句断定为 false 的情况下发生的。同时 AFL 还在 SQLite 中发现了相当多的引起崩溃的 bug ，甚至还发现了一些 SQLite 给出错误结果的 bug 。

Because of its past success, AFL became a standard part of the testing strategy for SQLite beginning with version 3.8.10 (2015-05-07) until it was superseded by better fuzzers in version 3.29.0 (2019-07-10).

由于 AFL 的成功使用，从版本 3.8.10 (2015-05-07) 开始成为 SQLite 测试策略的一部分，直到在版本 3.29.0 (2019-07-10) 中被更好的模糊器所取代。

使用 Google OSS Fuzz

4.1.2. Google OSS Fuzz
Beginning in 2016, a team of engineers at Google started the OSS Fuzz project. OSS Fuzz uses a AFL-style guided fuzzer running on Google’s infrastructure. The Fuzzer automatically downloads the latest check-ins for participating projects, fuzzes them, and sends email to the developers reporting any problems. When a fix is checked in, the fuzzer automatically detects this and emails a confirmation to the developers.

从 2016 年开始，一个 Google 团队创立了 OSS Fuzz 项目，这个项目在 Google 的运行架构上使用 AFL 风格的引导式模糊器。这种 Fuzzer 会自动下载被测试项目的最新提交版本，并对其开展模糊测试，出现问题后即向开发人员发送邮件。提交了修复代码后，模糊器会自动检测到并再发一封确认邮件。

SQLite is one of many open-source projects that OSS Fuzz tests. The test/ossfuzz.c source file in the SQLite repository is SQLite’s interface to OSS fuzz.
OSS Fuzz no longer finds historical bugs in SQLite. But it is still running and does occasionally find issues in new development check-ins. Examples: [1] [2] [3].

OSS Fuzz 并不只用来测试 SQLite 一种软件，许多其他的开源项目也在使用。源文件中的 test/ossfuzz.c 描述了 SQLite 对 OSS fuzz 的接口。OSS Fuzz 不再用于查找 SQLite 的早前 bug，但还是会定期测试 SQLite 的新提交的代码，寻找可能存在的缺陷。

使用 dbsqlfuzz fuzzer

4.1.3. The dbsqlfuzz fuzzer
Beginning in late 2018, SQLite has been fuzzed using a proprietary fuzzer called “dbsqlfuzz”. Dbsqlfuzz is built using the libFuzzer framework of LLVM.

2018 年底，SQLite 开始使用名为 “dbsqlfuzz” 的专用模糊器开展模糊测试。dbsqlfuzz 使用 LLVM 的 libFuzzer 框架构建。

The dbsqlfuzz fuzzer mutates both the SQL input and the database file at the same time. Dbsqlfuzz uses a custom Structure-Aware Mutator on a specialized input file that defines both an input database and SQL text to be run against that database. Because it mutates both the input database and the input SQL at the same time, dbsqlfuzz has been able to find some obscure faults in SQLite that were missed by prior fuzzers that mutated only SQL inputs or only the database file. The SQLite developers keep dbsqlfuzz running against trunk in about 16 cores at all times. Each instance of dbsqlfuzz program is able to evalutes about 400 test cases per second, meaning that about 500 million cases are checked every day.

dbsqlfuzz 模糊器同时变异 SQL 输入命令和数据库文件。dbsqlfuzz 使用一个对文件结构敏感的变异器，这个变异器以一个特别设计的文件为输入，文件包含两项：被测的数据库和 SQL 命令语句。由于 dbsqlfuzz 同时变异 SQL 输入命令和数据库文件，它可以发现早前的模糊器所不能发现的隐藏缺陷。SQLite 的开发人员在 trunk 代码上持续的使用16核执行 dbsqlfuzz，每个 dbsqlfuzz 程序实例每秒能执行 400 条测试用例，意味着每天能检查 5亿个用例。（16x400x24x60x60=552,960,000）

The dbsqlfuzz fuzzer has been very successful at hardening the SQLite code base against malicious attack. Since dbsqlfuzz has been added to the SQLite internal test suite, bug reports from external fuzzers such as OSSFuzz have all but stopped.

dbsqlfuzzfuzzer 在加固SQLite代码库、预防恶意攻击方面取得了极大的成功。由于 dbsqlfuzz 已加入到 SQLite 内部测试套件中，来自例如OSSFuzz 等外部模糊器的缺陷报告已几乎停止。

Note that dbsqlfuzz is not the Protobuf-based structure-aware fuzzer for SQLite that is used by Chromium and described in the Structure-Aware Mutator article. There is no connection between these two fuzzers, other than the fact that they are both based on libFuzzer The Protobuf fuzzer for SQLite is written and maintained by the Chromium team at Google, whereas dbsqlfuzz is written and maintained by the original SQLite developers. Having multiple independently-developed fuzzers for SQLite is good, as it means that obscure issues are more likely to be uncovered.

注意，dbsqlfuzz 不是基于 Protobuf 的文件结构敏感的变异器，这种变异器为 Chromium 使用。注意，dbsqlfuzz 和 基于 Protobuf 这两种模糊器之间没有任何联系，尽管它们都基于 libFuzzer。Protobuf 模糊器由 Google 的 Chromium 团队编写和维护，而dbsqlfuzz由 SQLite开发人员编写和维护。使用各自独立开发的模糊器对 SQLite 有好处，更容易发现隐藏的缺陷。

其他第三方模糊器

4.1.4. Other third-party fuzzers
SQLite seems to be a popular target for third-parties to fuzz. The developers hear about many attempts to fuzz SQLite and they do occasionally get bug reports found by independent fuzzers. All such reports are promptly fixed, so the product is improved and that the entire SQLite user community benefits. This mechanism of having many independent testers is similar to Linus’s law: “given enough eyeballs, all bugs are shallow”.

SQLite 似乎是第三方模糊器的热门目标。开发人员经常能听说其他独立开发的模糊器的正在尝试测试 SQLite，也确实收到过来自他们的缺陷报告。所有这样的报告都被及时修复，因此产品得到了改进，整个SQLite用户群体都从中受益。这种拥有许多独立测试人员的机制符合 Linus 定律：“只要有足够多的目光，所有的 bug 都是浅显易见的”。

One fuzzing researcher of particular note is Manuel Rigger, currently (as this paragraph is written on 2019-12-21) at ETH Zurich. Most fuzzers only look for assertion faults, crashes, undefined behavior (UB), or other easily detected anomalies. Dr. Rigger’s fuzzers, on the other hand, are able to find cases where SQLite computes an incorrect answer. Rigger has found many such cases. Most of these finds are obscure corner cases involving type conversions and affinity transformations, and a good number of the finds are against unreleased features. Nevertheless, his finds are still important as they are real bugs, and the SQLite developers are grateful to be able to identify and fix the underlying problems. Rigger’s work is currently unpublished. When it is released, it could be as influential as Zalewski’s invention of AFL and profile-guided fuzzing.

值得一提是一位专门研究模糊测试的 Manuel Rigger，他目前（撰写本文时的2019-12-21）在苏黎世联邦理工学院（ETH Zurich）工作。一般的模糊器只查找断言错误、崩溃、未定义行为（UB）或其他比较容易检测到的异常。然而，Rigger 博士的模糊器能够发现 SQLite 给出错误答案的情况。Rigger 发现了很多这样的错误场景。这些发现中的大多数都是难以触及的角落场景，例如数据类型转换、关联转换等等，而且有相当数量的缺陷也存在于尚未发布的功能里。他的发现很重要，因为它们是真正的bug，能帮助识别并修复 SQLite 潜在的问题，开发人员表示很感激。Rigger 尚未发布他的模糊器，若有一天发布时，它可能会像 Zalewski 发明的 AFL 和 配置文件引导的模糊器一样具有影响力。

fuzzcheck 测试工具

4.1.5. The fuzzcheck test harness
Historical test cases from AFL, OSS Fuzz, and dbsqlfuzz are collected in a set of database files in the main SQLite source tree and then rerun by the “fuzzcheck” utility program whenever one runs “make test”. Fuzzcheck only runs a few thousand “interesting” cases out of the billions of cases that the various fuzzers have examined over the years. “Interesting” cases are cases that exhibit previously unseen behavior. Actual bugs found by fuzzers are always included among the interesting test cases, but most of the cases run by fuzzcheck were never actual bugs.

AFL、OSS Fuzz、dbsqlfuzz下的回归测试用例存放于 SQLite 源代码的一组数据文件中，当运行 make test 时，fuzzcheck 程序就运行测试。这些年来，各种模糊器已经了测试了数十亿个测试用例， fuzzcheck 只运行其中数千个 “感兴趣” 的案例。“感兴趣”表示在之前有不可见的行为。fuzzer 的真实 bug 总是被这些 “感兴趣” 的用例发现，而其他的大多数用例从来都没有发现实际的 bug。

模糊测试和 100% MC/DC 测试之间的纠结

4.1.6. Tension Between Fuzz Testing And 100% MC/DC Testing
Fuzz testing and 100% MC/DC testing are in tension with one another. That is to say, code tested to 100% MC/DC will tend to be more vulnerable to problems found by fuzzing and code that performs well during fuzz testing will tend to have (much) less than 100% MC/DC. This is because MC/DC testing discourages defensive code with unreachable branches, but without defensive code, a fuzzer is more likely to find a path that causes problems. MC/DC testing seems to work well for building code that is robust during normal use, whereas fuzz testing is good for building code that is robust against malicious attack.

Of course, users would prefer code that is both robust in normal use and resistant to malicious attack. The SQLite developers are dedicated to providing that. The purpose of this section is merely to point out that doing both at the same time is difficult.

模糊测试和100%MC/DC测试是存在紧张关系的。也就是说，100%MC/DC测试通过的代码往往更容易受到攻击，而在模糊测试通过的代码往往低于100%MC/DC。这是因为 MC/DC 测试不具备针对不可达场景的防御性代码，但如果没有防御代码，模糊器更有可能找到导致问题的场景。MC/DC 测试似乎更加适应正常使用场景的代码，而模糊测试则适用于可能遭受恶意攻击的代码。

作为用户，当然既想要正常健壮使用，也想要能抵御攻击的代码。SQLite 开发人员致力于做到这一点。上面这段只是为了说明想要同时做到这两点是困难的。

For much of its history SQLite has been focused on 100% MC/DC testing. Resistance to fuzzing attacks only became a concern with the introduction of AFL in 2014. For a while there, fuzzers were finding many problems in SQLite. In more recent years, the testing strategy of SQLite has evolved to place more emphasis on fuzz testing. We still maintain 100% MC/DC of the core SQLite code, but most testing CPU cycles are now devoted to fuzzing.

在 SQLite 的发展历史上，一直专注于 100% MC/DC 测试。直到2014年开始使用AFL，抵御模糊攻击的能力才成为一个话题。从那时起，模糊器在 SQLite 中发现了许多问题。近些年来，SQLite 在模糊测试领域有了长足发展。我们一方面维持着 SQLite 核心代码的 100% MC/DC 测试，同时更多的测试资源用于模糊测试。

While fuzz testing and 100% MC/DC testing are in tension, they are not completely at cross-purposes. The fact that the SQlite test suite does test to 100% MC/DC means that when fuzzers do find problems, those problems can be fixed quickly and with little risk of introducing new errors.

虽然模糊测试和100%MC/DC测试存在紧张关系，但二者并不是毫不相干。事实上，SQlite 执行了 100% MC/DC 测试后再开展模糊测试，发现的 bug，都可以快速解决，并且引入新错误的风险很小。

格式错误的数据库文件

4.2. Malformed Database Files
There are numerous test cases that verify that SQLite is able to deal with malformed database files. These tests first build a well-formed database file, then add corruption by changing one or more bytes in the file by some means other than SQLite. Then SQLite is used to read the database. In some cases, the bytes changes are in the middle of data. This causes the content of the database to change while keeping the database well-formed. In other cases, unused bytes of the file are modified, which has no effect on the integrity of the database. The interesting cases are when bytes of the file that define database structure get changed. The malformed database tests verify that SQLite finds the file format errors and reports them using the SQLITE_CORRUPT return code without overflowing buffers, dereferencing NULL pointers, or performing other unwholesome actions.

The dbsqlfuzz fuzzer also does an excellent job of verifying that SQLite responds sanely to malformed database files.

针对SQLite是否能够处理格式错误的数据库文件，有许多测试用例。这些测试用例首先构建一个正确格式的数据库文件，然后通过SQLite外部的其他方式更改文件中的一个或多个字节来使其损坏。再使用SQLite读取数据库。如果字节更改在数据的位置，只会导致数据的内容发生变化，而数据库文件继续保持的正确的格式。如果修改到文件中未使用的字节，丝毫不影响数据库文件的完整性。需要关注的场景是，修改的字节位于数据库结构的定义。需要验证SQLite是否能够发现文件格式错误，而且返回 SQLite_CORRUPT错误码，而不会产生缓冲区溢出错误、取消引用空指针或执行其他有害动作。

在验证 SQLite 是否能正确对待错误数据库文件问题上，dbsqlfuzz 模糊器做的很好。

边界值测试

4.3. Boundary Value Tests
SQLite defines certain limits on its operation, such as the maximum number of columns in a table, the maximum length of an SQL statement, or the maximum value of an integer. The TCL and TH3 test suites both contains numerous tests that push SQLite right to the edge of its defined limits and verify that it performs correctly for all allowed values. Additional tests go beyond the defined limits and verify that SQLite correctly returns errors. The source code contains testcase macros to verify that both sides of each boundary have been tested.

SQLite对某些操作定义了边界限制，例如：表中的最大列数、SQL语句的最大长度、整型变量的最大值。TCL和TH3测试套件都包含大量针对此类型的测试，这些测试将 SQLite 处于极限的边缘处运行，并验证它对所有正常的值条件下，是否能正确运行。还有一些测试，则超出了定义的限制，目的是验证SQLite是否如期返回错误码。源代码包含测试用例宏，用于确保每个边界值的两侧都通过了测试。

5. 回归测试

5. Regression Testing
   Whenever a bug is reported against SQLite, that bug is not considered fixed until new test cases that would exhibit the bug have been added to either the TCL or TH3 test suites. Over the years, this has resulted in thousands and thousands of new tests. These regression tests ensure that bugs that have been fixed in the past are not reintroduced into future versions of SQLite.

每当发现 SQLite 的 bug 时，会针对这个 bug 设计新的测试用例，并添加到TCL或TH3测试套件中，只有这个新的测试用例通过后，才会认为这个 bug 已经修复。多年积累下来，以此产生了成千上万的新测试用例。这些回归测试确保了曾经发生的的 bug 在 SQLite 的新版本中不会再次出现。

6. 资源泄漏的自动检测

6. Automatic Resource Leak Detection
   Resource leak occurs when system resources are allocated and never freed. The most troublesome resource leaks in many applications are memory leaks - when memory is allocated using malloc() but never released using free(). But other kinds of resources can also be leaked: file descriptors, threads, mutexes, etc.

Both the TCL and TH3 test harnesses automatically track system resources and report resource leaks on every test run. No special configuration or setup is required. The test harnesses are especially vigilant with regard to memory leaks. If a change causes a memory leak, the test harnesses will recognize this quickly. SQLite is designed to never leak memory, even after an exception such as an OOM error or disk I/O error. The test harnesses are zealous to enforce this.

当分配出去的系统资源再也没有被释放回收时，就产生了资源泄漏。在大多数应用程序中，内存泄漏是最麻烦的一种资源泄漏，比如使用 malloc() 分配内存，但从未使用free()释放内存，就会产生内存泄漏。而其他类型的资源也可能泄漏：比如文件描述符、线程、互斥锁等。

TCL和TH3测试工具都会自动监测系统资源，并在每次测试运行时上报是否存在资源泄漏，而无需特殊配置或设置。测试工具尤其警惕内存是否泄漏。如果某次代码的更改导致内存泄漏，测试工具将很快识别出这一点。按照设计，SQLite应当决不会泄漏内存，即使在发生OOM错误或磁盘I/O错误等异常之后也是如此。测试工具热衷于强制执行这一点。

7. 测试覆盖率

7. Test Coverage
   The SQLite core, including the unix VFS, has 100% branch test coverage under TH3 in its default configuration as measured by gcov. Extensions such as FTS3 and RTree are excluded from this analysis.

SQLite核心，包括unix VFS，在默认配置 TH3下有100%的分支测试覆盖率，这是由 gcov 统计出的。并不包含额外的测试，例如 FTS3 和 RTree 之类的扩展。

7. 语句覆盖率 vs. 分支覆盖率

7.1. Statement versus branch coverage
There are many ways to measure test coverage. The most popular metric is “statement coverage”. When you hear someone say that their program as “XX% test coverage” without further explanation, they usually mean statement coverage. Statement coverage measures what percentage of lines of code are executed at least once by the test suite.

有很多方法可以衡量测试覆盖率。最流行的衡量标准是“语句覆盖率”。当你听到有人说他们的程序是 “测试覆盖率达到百分之多少” 而没有进一步解释时，他们通常指的是语句覆盖率。语句覆盖率的定义是，有多少比例的代码被测试代码执行了至少一次。

Branch coverage is more rigorous than statement coverage. Branch coverage measures the number of machine-code branch instructions that are evaluated at least once on both directions.

而分支覆盖率统计方法则比语句覆盖率更加严格。分支覆盖率指机器码的分支指令的两条路径，至少都被执行一次的比例。

To illustrate the difference between statement coverage and branch coverage, consider the following hypothetical line of C code:

为了展示两种覆盖率的不同，我们考察下面的这行 C 代码：

if( a>b && c!=25 ){d++;}

Such a line of C code might generate a dozen separate machine code instructions. If any one of those instructions is ever evaluated, then we say that the statement has been tested. So, for example, it might be the case that the conditional expression is always false and the “d” variable is never incremented. Even so, statement coverage counts this line of code as having been tested.

这行C代码会产生几十条机器码指令集。如果其中有任何一条机器码指令被执行，我们说这条语句是被测试了的。即使每次测试中，if 的条件总是 false，导致变量 d 总是不能自增，然而因为这行语句确实被执行了，所以按语句覆盖率统计方法，这一行会被认为测试通过了的。

Branch coverage is more strict. With branch coverage, each test and each subblock within the statement is considered separately. In order to achieve 100% branch coverage in the example above, there must be at least three test cases:

分支覆盖率统计方法则更加严格。每个判定条件和其中的组成部分定都会被单独处理。还是上面的例子，将至少产生 3个测试用例：

a<=b
a>b && c==25
a>b && c!=25

Any one of the above test cases would provide 100% statement coverage but all three are required for 100% branch coverage. Generally speaking, 100% branch coverage implies 100% statement coverage, but the converse is not true. To reemphasize, the TH3 test harness for SQLite provides the stronger form of test coverage - 100% branch test coverage.

7.2. Coverage testing of defensive code A well-written C program will typically contain some defensive conditionals which in practice are always true or always false. This leads to a programming dilemma: Does one remove defensive code in order to obtain 100% branch coverage?

In SQLite, the answer to the previous question is “no”. For testing purposes, the SQLite source code defines macros called ALWAYS() and NEVER(). The ALWAYS() macro surrounds conditions which are expected to always evaluate as true and NEVER() surrounds conditions that are always evaluated to false. These macros serve as comments to indicate that the conditions are defensive code. In release builds, these macros are pass-throughs:

#define ALWAYS(X) (X) #define NEVER(X) (X) During most testing, however, these macros will throw an assertion fault if their argument does not have the expected truth value. This alerts the developers quickly to incorrect design assumptions.

#define ALWAYS(X) ((X)?1:assert(0),0) #define NEVER(X) ((X)?assert(0),1:0) When measuring test coverage, these macros are defined to be constant truth values so that they do not generate assembly language branch instructions, and hence do not come into play when calculating the branch coverage:

#define ALWAYS(X) (1) #define NEVER(X) (0) The test suite is designed to be run three times, once for each of the ALWAYS() and NEVER() definitions shown above. All three test runs should yield exactly the same result. There is a run-time test using the sqlite3_test_control(SQLITE_TESTCTRL_ALWAYS, …) interface that can be used to verify that the macros are correctly set to the first form (the pass-through form) for deployment.

7.3. Forcing coverage of boundary values and boolean vector tests Another macro used in conjunction with test coverage measurement is the testcase() macro. The argument is a condition for which we want test cases that evaluate to both true and false. In non-coverage builds (that is to say, in release builds) the testcase() macro is a no-op:

#define testcase(X) But in a coverage measuring build, the testcase() macro generates code that evaluates the conditional expression in its argument. Then during analysis, a check is made to ensure tests exist that evaluate the conditional to both true and false. Testcase() macros are used, for example, to help verify that boundary values are tested. For example:

testcase( a==b ); testcase( a==b+1 ); if( a>b && c!=25 ){ d++; } Testcase macros are also used when two or more cases of a switch statement go to the same block of code, to make sure that the code was reached for all cases:

switch( op ){ case OP_Add: case OP_Subtract: { testcase( op==OP_Add ); testcase( op==OP_Subtract ); /* … / break; } / … */ } For bitmask tests, testcase() macros are used to verify that every bit of the bitmask affects the outcome. For example, in the following block of code, the condition is true if the mask contains either of two bits indicating either a MAIN_DB or a TEMP_DB is being opened. The testcase() macros that precede the if statement verify that both cases are tested:

testcase( mask & SQLITE_OPEN_MAIN_DB ); testcase( mask & SQLITE_OPEN_TEMP_DB ); if( (mask & (SQLITE_OPEN_MAIN_DB|SQLITE_OPEN_TEMP_DB))!=0 ){ … } The SQLite source code contains 1143 uses of the testcase() macro.

7.4. Branch coverage versus MC/DC Two methods of measuring test coverage were described above: “statement” and “branch” coverage. There are many other test coverage metrics besides these two. Another popular metric is “Modified Condition/Decision Coverage” or MC/DC. Wikipedia defines MC/DC as follows:

Each decision tries every possible outcome. Each condition in a decision takes on every possible outcome. Each entry and exit point is invoked. Each condition in a decision is shown to independently affect the outcome of the decision. In the C programming language where && and || are “short-circuit” operators, MC/DC and branch coverage are very nearly the same thing. The primary difference is in boolean vector tests. One can test for any of several bits in bit-vector and still obtain 100% branch test coverage even though the second element of MC/DC - the requirement that each condition in a decision take on every possible outcome - might not be satisfied.

SQLite uses testcase() macros as described in the previous subsection to make sure that every condition in a bit-vector decision takes on every possible outcome. In this way, SQLite also achieves 100% MC/DC in addition to 100% branch coverage.

7.5. Measuring branch coverage Branch coverage in SQLite is currently measured using gcov with the “-b” option. First the test program is compiled using options “-g -fprofile-arcs -ftest-coverage” and then the test program is run. Then “gcov -b” is run to generate a coverage report. The coverage report is verbose and inconvenient to read, so the gcov-generated report is processed using some simple scripts to put it into a more human-friendly format. This entire process is automated using scripts, of course.

Note that running SQLite with gcov is not a test of SQLite — it is a test of the test suite. The gcov run does not test SQLite because the -fprofile-args and -ftest-coverage options cause the compiler to generate different code. The gcov run merely verifies that the test suite provides 100% branch test coverage. The gcov run is a test of the test - a meta-test.

After gcov has been run to verify 100% branch test coverage, then the test program is recompiled using delivery compiler options (without the special -fprofile-arcs and -ftest-coverage options) and the test program is rerun. This second run is the actual test of SQLite.

It is important to verify that the gcov test run and the second real test run both give the same output. Any differences in output indicate either the use of undefined or indeterminate behavior in the SQLite code (and hence a bug), or a bug in the compiler. Note that SQLite has, over the previous decade, encountered bugs in each of GCC, Clang, and MSVC. Compiler bugs, while rare, do happen, which is why it is so important to test the code in an as-delivered configuration.

7.6. Mutation testing Using gcov (or similar) to show that every branch instruction is taken at least once in both directions is good measure of test suite quality. But even better is showing that every branch instruction makes a difference in the output. In other words, we want to show not only that every branch instruction both jumps and falls through but also that every branch is doing useful work and that the test suite is able to detect and verify that work. When a branch is found that does not make a difference in the output, that suggests that the code associated the branch can be removed (reducing the size of the library and perhaps making it run faster) or that the test suite is inadequately testing the feature that the branch implements.

SQLite strives to verify that every branch instruction makes a difference using mutation testing. A script first compiles the SQLite source code into assembly language (using, for example, the -S option to gcc). Then the script steps through the generated assembly language and, one by one, changes each branch instruction into either an unconditional jump or a no-op, compiles the result, and verifies that the test suite catches the mutation.

Unfortunately, SQLite contains many branch instructions that help the code run faster without changing the output. Such branches generate false-positives during mutation testing. As an example, consider the following hash function used to accelerate table-name lookup:

55 static unsigned int strHash(const char *z){ 56 unsigned int h = 0; 57 unsigned char c; 58 while( (c = (unsigned char)*z++)!=0 ){ /OPTIMIZATION-IF-TRUE/ 59 h = (h«3) ^ h ^ sqlite3UpperToLower[c]; 60 } 61 return h; 62 } If the branch instruction that implements the “c!=0” test on line 58 is changed into a no-op, then the while-loop will loop forever and the test suite will fail with a time-out. But if that branch is changed into an unconditional jump, then the hash function will always return 0. The problem is that 0 is a valid hash. A hash function that always returns 0 still works in the sense that SQLite still always gets the correct answer. The table-name hash table degenerates into a linked-list and so the table-name lookups that occur while parsing SQL statements might be a little slower, but the end result will be the same.

To work around this problem, comments of the form “/OPTIMIZATION-IF-TRUE/” and “/OPTIMIZATION-IF-FALSE/” are inserted into the SQLite source code to tell the mutation testing script to ignore some branch instructions.

7.7. Experience with full test coverage The developers of SQLite have found that full coverage testing is an extremely effective method for locating and preventing bugs. Because every single branch instruction in SQLite core code is covered by test cases, the developers can be confident that changes made in one part of the code do not have unintended consequences in other parts of the code. The many new features and performance improvements that have been added to SQLite in recent years would not have been possible without the availability of full-coverage testing.

Maintaining 100% MC/DC is laborious and time-consuming. The level of effort needed to maintain full-coverage testing is probably not cost effective for a typical application. However, we think that full-coverage testing is justified for a very widely deployed infrastructure library like SQLite, and especially for a database library which by its very nature “remembers” past mistakes.

8. Dynamic Analysis Dynamic analysis refers to internal and external checks on the SQLite code which are performed while the code is live and running. Dynamic analysis has proven to be a great help in maintaining the quality of SQLite.

8.1. Assert The SQLite core contains 6548 assert() statements that verify function preconditions and postconditions and loop invariants. Assert() is a macro which is a standard part of ANSI-C. The argument is a boolean value that is assumed to always be true. If the assertion is false, the program prints an error message and halts.

Assert() macros are disabled by compiling with the NDEBUG macro defined. In most systems, asserts are enabled by default. But in SQLite, the asserts are so numerous and are in such performance critical places, that the database engine runs about three times slower when asserts are enabled. Hence, the default (production) build of SQLite disables asserts. Assert statements are only enabled when SQLite is compiled with the SQLITE_DEBUG preprocessor macro defined.

See the Use Of assert in SQLite document for additional information about how SQLite uses assert().

8.2. Valgrind Valgrind is perhaps the most amazing and useful developer tool in the world. Valgrind is a simulator - it simulates an x86 running a Linux binary. (Ports of Valgrind for platforms other than Linux are in development, but as of this writing, Valgrind only works reliably on Linux, which in the opinion of the SQLite developers means that Linux should be the preferred platform for all software development.) As Valgrind runs a Linux binary, it looks for all kinds of interesting errors such as array overruns, reading from uninitialized memory, stack overflows, memory leaks, and so forth. Valgrind finds problems that can easily slip through all of the other tests run against SQLite. And, when Valgrind does find an error, it can dump the developer directly into a symbolic debugger at the exact point where the error occur, to facilitate a quick fix.

Because it is a simulator, running a binary in Valgrind is slower than running it on native hardware. (To a first approximation, an application running in Valgrind on a workstation will perform about the same as it would running natively on a smartphone.) So it is impractical to run the full SQLite test suite through Valgrind. However, the veryquick tests and the coverage of the TH3 tests are run through Valgrind prior to every release.

8.3. Memsys2 SQLite contains a pluggable memory allocation subsystem. The default implementation uses system malloc() and free(). However, if SQLite is compiled with SQLITE_MEMDEBUG, an alternative memory allocation wrapper (memsys2) is inserted that looks for memory allocation errors at run-time. The memsys2 wrapper checks for memory leaks, of course, but also looks for buffer overruns, uses of uninitialized memory, and attempts to use memory after it has been freed. These same checks are also done by valgrind (and, indeed, Valgrind does them better) but memsys2 has the advantage of being much faster than Valgrind, which means the checks can be done more often and for longer tests.

8.4. Mutex Asserts SQLite contains a pluggable mutex subsystem. Depending on compile-time options, the default mutex system contains interfaces sqlite3_mutex_held() and sqlite3_mutex_notheld() that detect whether or not a particular mutex is held by the calling thread. These two interfaces are used extensively within assert() statements in SQLite to verify mutexes are held and released at all the right moments, in order to double-check that SQLite does work correctly in multi-threaded applications.

8.5. Journal Tests One of the things that SQLite does to ensure that transactions are atomic across system crashes and power failures is to write all changes into the rollback journal file prior to changing the database. The TCL test harness contains an alternative OS backend implementation that helps to verify this is occurring correctly. The “journal-test VFS” monitors all disk I/O traffic between the database file and rollback journal, checking to make sure that nothing is written into the database file which has not first been written and synced to the rollback journal. If any discrepancies are found, an assertion fault is raised.

The journal tests are an additional double-check over and above the crash tests to make sure that SQLite transactions will be atomic across system crashes and power failures.

8.6. Undefined Behavior Checks In the C programming language, it is very easy to write code that has “undefined” or “implementation defined” behavior. That means that the code might work during development, but then give a different answer on a different system, or when recompiled using different compiler options. Examples of undefined and implementation-defined behavior in ANSI C include:

Signed integer overflow. (Signed integer overflow does not necessarily wrap around, as most people expect.) Shifting an N-bit integer by more than N bits. Shifting by a negative amount. Shifting a negative number. Using the memcpy() function on overlapping buffers. The order of evaluation of function arguments. Whether or not “char” variables are signed or unsigned. And so forth…. Since undefined and implementation-defined behavior is non-portable and can easily lead to incorrect answers, SQLite works very hard to avoid it. For example, when adding two integer column values together as part of an SQL statement, SQLite does not simply add them together using the C-language “+” operator. Instead, it first checks to make sure the addition will not overflow, and if it will, it does the addition using floating point instead.

To help ensure that SQLite does not make use of undefined or implementation defined behavior, the test suites are rerun using instrumented builds that try to detect undefined behavior. For example, test suites are run using the “-ftrapv” option of GCC. And they are run again using the “-fsanitize=undefined” option on Clang. And again using the “/RTC1” option in MSVC. Then the test suites are rerun using options like “-funsigned-char” and “-fsigned-char” to make sure that implementation differences do not matter either. Tests are then repeated on 32-bit and 64-bit systems and on big-endian and little-endian systems, using a variety of CPU architectures. Furthermore, the test suites are augmented with many test cases that are deliberately designed to provoke undefined behavior. For example: “SELECT -1*(-9223372036854775808);”.

9. Disabled Optimization Tests The sqlite3_test_control(SQLITE_TESTCTRL_OPTIMIZATIONS, …) interface allows selected SQL statement optimizations to be disabled at run-time. SQLite should always generate exactly the same answer with optimizations enabled and with optimizations disabled; the answer simply arrives quicker with the optimizations turned on. So in a production environment, one always leaves the optimizations turned on (the default setting).

One verification technique used on SQLite is to run an entire test suite twice, once with optimizations left on and a second time with optimizations turned off, and verify that the same output is obtained both times. This shows that the optimizations do not introduce errors.

Not all test cases can be handled this way. Some test cases check to verify that the optimizations really are reducing the amount of computation by counting the number of disk accesses, sort operations, full-scan steps, or other processing steps that occur during queries. Those test cases will appear to fail when optimizations are disabled. But the majority of test cases simply check that the correct answer was obtained, and all of those cases can be run successfully with and without the optimizations, in order to show that the optimizations do not cause malfunctions.

10. Checklists The SQLite developers use an on-line checklist to coordinate testing activity and to verify that all tests pass prior each SQLite release. Past checklists are retained for historical reference. (The checklists are read-only for anonymous internet viewers, but developers can log in and update checklist items in their web browsers.) The use of checklists for SQLite testing and other development activities is inspired by The Checklist Manifesto .

The latest checklists contain approximately 200 items that are individually verified for each release. Some checklist items only take a few seconds to verify and mark off. Others involve test suites that run for many hours.

The release checklist is not automated: developers run each item on the checklist manually. We find that it is important to keep a human in the loop. Sometimes problems are found while running a checklist item even though the test itself passed. It is important to have a human reviewing the test output at the highest level, and constantly asking “Is this really right?”

The release checklist is continuously evolving. As new problems or potential problems are discovered, new checklist items are added to make sure those problems do not appear in subsequent releases. The release checklist has proven to be an invaluable tool in helping to ensure that nothing is overlooked during the release process.

11. Static Analysis Static analysis means analyzing source code at compile-time to check for correctness. Static analysis includes compiler warning messages and more in-depth analysis engines such as the Clang Static Analyzer. SQLite compiles without warnings on GCC and Clang using the -Wall and -Wextra flags on Linux and Mac and on MSVC on Windows. No valid warnings are generated by the Clang Static Analyzer tool “scan-build” either (though recent versions of clang seem to generate many false-positives.) Nevertheless, some warnings might be generated by other static analyzers. Users are encouraged not to stress over these warnings and to instead take solace in the intense testing of SQLite described above.

Static analysis has not been helpful in finding bugs in SQLite. Static analysis has found a few bugs in SQLite, but those are the exceptions. More bugs have been introduced into SQLite while trying to get it to compile without warnings than have been found by static analysis.

12. Summary SQLite is open source. This gives many people the idea that it is not well tested as commercial software and is perhaps unreliable. But that impression is false. SQLite has exhibited very high reliability in the field and a very low defect rate, especially considering how rapidly it is evolving. The quality of SQLite is achieved in part by careful code design and implementation. But extensive testing also plays a vital role in maintaining and improving the quality of SQLite. This document has summarized the testing procedures that every release of SQLite undergoes with the hope of inspiring confidence that SQLite is suitable for use in mission-critical applications.

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