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Surviving Software Dependencies


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Credit: T. Emil

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For decades, discussion of software reuse was more common than actual software reuse. Today, the situation is reversed: developers reuse software written by others every day, in the form of software dependencies, and the situation goes mostly unexamined.

My background includes a decade of working with Google's internal source code system, which treats software dependencies as a first-class concept,17 as well as developing support for dependencies in the Go programming language.2

Software dependencies carry with them serious risks that are too often overlooked. The shift to easy, fine-grained software reuse has happened so quickly that we do not yet understand the best practices for choosing and using dependencies effectively, or even for deciding when they are appropriate and when not. The purpose of this article is to raise awareness of the risks and encourage more investigation of solutions.

In software development today, a dependency is additional code a programmer wants to call. Adding a dependency avoids repeating work: designing, testing, debugging, and maintaining a specific unit of code. In this article, that unit of code is referred to as a package; some systems use the terms library and module instead.

Taking on externally written dependencies is not new. Most programmers have at one point in their careers had to go through the steps of manually installing a required library, such as C's PCRE or zlib; C++'s Boost or Qt; or Java's JodaTime or JUnit. These packages contain high-quality, debugged code that required significant expertise to develop. For a program that needs the functionality provided by one of these packages, the tedious work of manually downloading, installing, and updating the package is easier than the work of redeveloping that functionality from scratch. The high fixed costs of reuse, however, mean manually reused packages tend to be big; a tiny package would be easier to reimplement.

A dependency manager (a.k.a. package manager) automates the downloading and installation of dependency packages. As dependency managers make individual packages easier to download and install, the lower fixed costs make smaller packages economical to publish and reuse. For example, the Node.js dependency manager NPM provides access to more than 750,000 packages. One of them, escape-string-regexp, consists of a single function that escapes regular expression operators in its input. The entire implementation is:

var matchOperatorsRe =
/[|\\{}()[\]^$+*?.]/g;
module.exports = function (str) {
if (typeof str !== 'string') {
throw new TypeError(
'Expected a string');
}
return str.replace(
matchOperatorsRe, '\\$&');
};

Before dependency managers, publishing an eight-line code library would have been unthinkable: too much overhead for too little benefit. NPM, however, has driven the overhead approximately to zero, with the result that nearly trivial functionality can be packaged and reused. In late April 2019, the escape-string-regexp package was explicitly depended upon by almost a thousand other NPM packages, not to mention all the packages developers write for their own use and don't share.

Dependency managers now exist for essentially every programming language: Maven Central (Java), NuGet (.NET), Packagist (PHP), PyPI (Python), and RubyGems (Ruby) each host more than 100,000 packages. The arrival of this kind of fine-grained, widespread software reuse is one of the most consequential shifts in software development over the past two decades. And if we are not more careful, it will lead to serious problems.

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What Could Go Wrong?

A package, for this discussion, is code downloaded from the Internet. Adding a package as a dependency outsources the work of developing that code—designing, writing, testing, debugging, and maintaining—to someone else on the Internet, often unknown to the programmer. Using that code exposes the program to all the failures and flaws in the dependency. The program's execution now literally depends on code downloaded from this stranger on the Internet. Presented this way, it sounds incredibly unsafe. Why would anyone do this?

Because it's easy, it seems to work, everyone else is doing it, and, most importantly, it seems like a natural continuation of age-old established practice. But there are important differences that are being ignored.

Decades ago, most developers trusted others to write the software they depended on, such as operating systems and compilers. That software was purchased from known sources, often with some kind of support agreement. There was still a potential for bugs or outright mischief,20 but at least the developers knew who they were dealing with and usually had commercial or legal recourses available.

The phenomenon of open source software, distributed at no cost over the Internet, has displaced many of those earlier software purchases. When reuse was difficult, there were fewer projects publishing reusable code packages. Even though their licenses typically disclaimed, among other things, any "implied warranties of merchantability and fitness for a particular purpose," the projects built up well-known reputations that often factored heavily into people's decisions about which to use. The commercial and legal support for trusting software sources was replaced by reputational support. Many common early packages still enjoy good reputations: consider BLAS (published in 1979), Netlib (1987), libjpeg (1991), LAPACK (1992), HP STL (1994), and zlib (1995).

Dependency managers have scaled down this open source code reuse model. Now, developers can share code at the granularity of individual functions consisting of tens of lines of code. This is a major technical accomplishment. Myriad packages are available, and writing code can involve a large number of them, but the commercial, legal, and reputational support mechanisms for trusting the code have not carried over. Developers trust more code with less justification for doing so.

The cost of adopting a bad dependency can be viewed as the sum, over all possible bad outcomes, of the cost of each bad outcome multiplied by its probability of happening (risk), as shown in the equation.

ueq01.gif

The context in which a dependency will be used determines the cost of a bad outcome. At one end of the spectrum is a personal hobby project, where the cost of most bad outcomes is near zero: you are just having fun, bugs have no real impact other than wasting time, and even debugging can be fun. So, the risk probability almost doesn't matter—it's being multiplied by a failure cost of almost zero. At the other end of the spectrum is production software that must be maintained for years. Here, the cost of a bug in a dependency can be very high: servers may go down, sensitive data may be divulged, customers may be harmed, or companies may fail. High failure costs make it much more important to estimate and then reduce any risk of a serious failure.


Developers trust more code with less justification for doing so.


No matter what the expected cost, experiences with larger dependencies suggest some approaches for estimating and reducing the risks of adding a software dependency. Better tooling is likely needed to help reduce the costs of these approaches, much as dependency managers have focused to date on reducing the costs of downloading and installation.

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Inspect the Dependency

You would not hire a software developer you have never heard of and know nothing about. You would learn more about the person first: check references, conduct a job interview, run background checks, and so on. Before you depend on a package found on the Internet, it is similarly prudent to learn a bit about it first.

A basic inspection can provide a sense of how likely you are to run into problems trying to use this code. If the inspection reveals likely minor problems, you can take steps to prepare for or perhaps avoid them. If the inspection reveals major problems, it may be best not to use the package; maybe you will find a more suitable one, or maybe you need to develop one yourself. Remember that open source packages are published by their authors in the hope they will be useful but with no guarantee of usability or support. In the middle of a production outage, you will be the one debugging the package. As the original GNU General Public License warned, "The entire risk as to the quality and performance of the program is with you. Should the program prove defective, you assume the cost of all necessary servicing, repair or correction."7

The following are some considerations when inspecting a package and deciding whether to depend on it:

Design. Is the documentation clear? Does the API have a clear design? If the authors can explain the package's API and its design well in the documentation, that increases the likelihood they have explained the implementation well to the computer in the source code. Writing code using a clear, well-designed API is also easier, faster, and hopefully less error-prone. Have the authors documented what they expect from client code in order to make future upgrades compatible? (Examples include the C++23 and Go8 compatibility documents.)

Code quality. Is the code well written? Read some of it. Does it look like the authors have been careful, conscientious, and consistent? Does it look like code you would want to debug? You may need to.

Develop your own systematic ways to check code quality. For example, something as simple as compiling a C or C++ program with important compiler warnings enabled (for example, -Wall) can give you a sense of how seriously the developers work to avoid various undefined behaviors. Recent languages such as Go, Rust, and Swift use an unsafe keyword to mark code that violates the type system; look to see how much unsafe code there is. More advanced semantic tools such as Infer6 or SpotBugs19 are helpful, too. Linters are less helpful: you should ignore rote suggestions about topics such as brace style and focus instead on semantic problems.

Keep an open mind about unfamiliar development practices. For example, the SQLite library ships as a single 200,000-line C source file and a single 11,000-line header called the amalgamation. The sheer size of these files should raise an initial red flag, but closer investigation would turn up the actual development source code, a traditional file tree with more than 100 C source files, tests, and support scripts. It turns out the single-file distribution is built automatically from the original sources and is easier for end users, especially those without dependency managers. (The compiled code also runs faster, because the compiler can see more optimization opportunities.)

Testing. Does the code have tests? Can you run them? Do they pass? Tests establish the code's basic functionality is correct, and they signal the developer is serious about keeping it correct. For example, the SQLite development tree has an incredibly thorough test suite with more than 30,000 individual test cases, as well as developer documentation explaining the testing strategy.10 On the other hand, if there are few tests or no tests, or if the tests fail, that's a serious red flag. Future changes to the package are likely to introduce regressions that could easily have been caught. If you insist on tests in code you write (you do, right?), you should insist on tests in code you outsource to others.

Assuming the tests exist, run, and pass, you can gather more information by running them with runtime instrumentation such as code coverage analysis, race detection,16 memory-allocation checking, and memory-leak detection.

Debugging. Find the package's issue tracker. Are there many open bug reports? How long have they been open? Are there many fixed bugs? Have any bugs been fixed recently? If you see lots of open issues about what look like real bugs, especially if they have been open for a long time, that's not a good sign. On the other hand, if the closed issues show that bugs are rarely found and promptly fixed, that's great.

Maintenance. Look at the package's commit history. How long has the code been actively maintained? Is it actively maintained now? Packages that have been actively maintained for an extended amount of time are more likely to continue to be maintained. How many people work on the package? Many packages are personal projects that developers create and share for fun in their spare time. Others are the result of thousands of hours of work by a group of paid developers. In general, the latter kind of package is more likely to have prompt bug fixes, steady improvements, and general upkeep.

On the other hand, some code really is "done." For example, NPM's escape-string-regexp, shown earlier, may never need to be modified again.

Usage. Do many other packages depend on this code? Dependency managers can often provide statistics about usage, or you can use a Web search to estimate how often others write about using the package. More users should at least mean more people for whom the code works well enough, along with faster detection of new bugs. Widespread usage is also a hedge against the question of continued maintenance; if a widely used package loses its maintainer, an interested user is likely to step forward.

For example, libraries such as PCRE or Boost or JUnit are incredibly widely used. That makes it more likely—al-though certainly not guaranteed—that bugs you might otherwise run into have already been fixed, because others ran into them first.

Security. Will you be processing un-trusted inputs with the package? If so, does it seem to be robust against malicious inputs? Does it have a history of security problems listed in the NVD (National Vulnerability Database)?13

For example, in 2006 when Jeff Dean and I started work on Google Code Search5—grep over public source code—the popular PCRE regular expression library seemed like an obvious choice. In an early discussion with Google's security team, however, we learned that PCRE had a history of problems such as buffer overflows, especially in its parser. We could have learned the same by searching for PCRE in the NVD. That discovery did not immediately cause us to abandon PCRE, but it did make us think more carefully about testing and isolation.

Licensing. Is the code properly licensed? Does it have a license at all? Is the license acceptable for your project or company? A surprising fraction of projects on GitHub have no clear license. Your project or company may impose further restrictions on the allowed licenses of dependencies. For example, Google disallows the use of code licensed under AGPL-like licenses (too onerous), as well as WTFPL-like licenses (too vague).9

Dependencies. Does the code have dependencies of its own? Flaws in indirect dependencies are just as bad for your program as flaws in direct dependencies. Dependency managers can list all the transitive dependencies of a given package, and each of them should ideally be inspected as described here. A package with many dependencies incurs additional inspection work, because those same dependencies incur additional risk that needs to be evaluated.

Many developers have never looked at the full list of transitive dependencies of their code and do not know what they depend on. For example, the NPM user community discovered in March 2016 that many popular projects—including Babel, Ember, and React—all depended indirectly on a tiny package called left-pad, consisting of a single eight-line function body. They discovered this when the author of left-pad deleted that package from NPM, inadvertently breaking most Node.js users' builds.22 And left-pad is hardly exceptional in this regard. For example, 30% of the 750,000 packages published on NPM depend—at least indirectly—on escape-string-regexp. Adapting Leslie Lamport's observation about distributed systems, a dependency manager can easily create a situation in which the failure of a package you did not even know existed can render your own code unusable.

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Test the Dependency

The inspection process should include running a package's own tests. If the package passes the inspection and you decide to make your project depend on it, the next step should be to write new tests focused on the functionality needed by your application. These tests often start out as short stand-alone programs written to ensure you can understand the package's API and that it does what you think it does. (If you can't or it doesn't, turn back now!) It is worth making the extra effort to turn those programs into automated tests that can be run against newer versions of the package. If you find a bug and have a potential fix, you will want to be able to rerun these project-specific tests easily, to ensure the fix did not break anything else.

It is especially worth exercising the likely problem areas identified by the basic inspection. For Code Search, we knew from past experience that PCRE sometimes took a long time to execute certain regular expression searches. The initial plan was to have separate thread pools for "simple" and "complicated" regular expression searches. One of the first tests was a benchmark comparing pcre-grep with a few other grep implementations. For one basic test case, pcre-grep was 70 times slower than the fastest grep available, so we started to rethink the plan to use PCRE. Even though PCRE was eventually dropped entirely, that benchmark remains in the code base today.

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Abstract the Dependency

Depending on a package is a decision likely to be revisited later. Perhaps updates will take the package in a new direction. Perhaps serious security problems will be found. Perhaps a better option will come along. For all these reasons, it is worth the effort to make it easy to migrate your project to a new dependency.

If the package will be used from many places in your project's source code, migrating to a new dependency would require making changes to all those different source locations. Worse, if the package will be exposed in your own project's API, migrating to a new dependency would require making changes in all the code calling your API, which you might not control. To avoid these costs, it makes sense to define an interface of your own, along with a thin wrapper implementing that interface using the dependency. Note that the wrapper should include only what your project needs from the dependency, not everything the dependency offers. Ideally, that allows you to substitute a different, equally appropriate dependency later, by changing only the wrapper. Migrating your per-project tests to use the new interface will test the interface and wrapper implementation, as well as making it easy to test any potential replacements for the dependency.

For Code Search, we developed an abstract Regexp class that defined the interface Code Search needed from any regular expression engine. Then we wrote a thin wrapper around PCRE implementing that interface. The indirection made it easy to test alternate libraries, and it prevented accidentally introducing knowledge of PCRE internals into the rest of the source tree. That in turn ensured it would be easy to switch to a different dependency if needed.

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Isolate the Dependency

Isolating a dependency at runtime may also be appropriate in order to limit the possible damage caused by bugs. For example, Google Chrome allows users to add dependencies—extension code—to the browser. When Chrome launched in 2008, it introduced the critical feature (now standard in all browsers) of isolating each extension in a sandbox running in a separate operating-system process.18

An exploitable bug in a badly written extension therefore did not automatically have access to the entire memory of the browser itself and could be stopped from making inappropriate system calls.12 For Code Search, until we dropped PCRE entirely, the plan was to isolate at least the PCRE parser in a similar sandbox. Today, another option would be a lightweight hypervisor-based sandbox such as gVisor.11 Isolating dependencies reduces the associated risks of running that code.

Even with these examples and other off-the-shelf options, runtime isolation of suspect code is still too difficult and rarely done. True isolation would require a completely memorysafe language, with no escape hatch into untyped code. That's challenging not just in entirely unsafe languages such as C and C++, but also in languages that provide restricted unsafe operations, such as Java when including JNI (Java Native Interface), or Go, Rust, and Swift when including their "unsafe" features. Even in a memory-safe language such as JavaScript, code often has access to far more than it needs. In November 2018, the latest version of the NPM package event-stream, which provided a functional streaming API for JavaScript events, was discovered to contain obfuscated malicious code that had been added 2.5 months earlier. The code, which harvested large Bitcoin wallets from users of the Co-pay mobile app, was accessing system resources entirely unrelated to processing event streams.1 One of many possible defenses to this kind of problem would be to better restrict what dependencies can access.

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Avoid the Dependency

If a dependency seems too risky and you can't find a way to isolate it, the best answer may be to avoid it entirely, or at least to avoid the parts you have identified as most problematic.


Even after all that work, you are not done tending your dependencies. It's important to continue to monitor them and perhaps even re-evaluate your decision to use them.


For example, as we better understood the risks and costs associated with PCRE, our plan for Google Code Search evolved from "use PCRE directly," to "use PCRE but sandbox the parser," to "write a new regular expression parser but keep the PCRE execution engine," to "write a new parser and connect it to a different, more efficient open source execution engine." Later we rewrote the execution engine as well, so that no dependencies were left, and we open sourced the result: RE2.4

If you need only a tiny fraction of a dependency, the simplest solution may be to make a copy of what you need (preserving appropriate copyright and other legal notices, of course). You are taking on responsibility for fixing bugs, maintenance, and so on, but you are also completely isolated from the larger risks. The Go developer community has a proverb about this: "A little copying is better than a little dependency."14

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Upgrade the Dependency

For a long time, the conventional wisdom about software was, "If it ain't broke, don't fix it." Upgrading carries a chance of introducing new bugs; without a corresponding reward—such as a new feature you need—why take the risk? This analysis ignores two costs. The first is the cost of the eventual upgrade. In software, the difficulty of making code changes does not scale linearly: making 10 small changes is less work and easier to get right than making one equivalent large change. The second is the cost of discovering already-fixed bugs the hard way. Especially in a security context, where known bugs are actively exploited, every day you wait is another day that attackers can break in.

For example, consider what happened at Equifax in 2017, as recounted by executives in detailed Congressional testimony.21 On March 7, a new vulnerability in Apache Struts was disclosed, and a patched version was released. On March 8, Equifax received a notice from US-CERT (United States Computer Emergency Readiness Team) about the need to update any uses of Apache Struts. Equifax ran source code and network scans on March 9 and March 15, respectively; neither scan turned up a particular group of public-facing Web servers. On May 13, attackers found the servers that Equifax's security teams could not. They used the Apache Struts vulnerability to breach Equifax's network and then steal detailed personal and financial information about 148 million people over the next two months. Equifax finally noticed the breach on July 29 and publicly disclosed it on September 4. By the end of September, Equifax's CEO, CIO, and CSO had all resigned, and a Congressional investigation was underway.

Equifax's experience drives home the point that although dependency managers know the versions they are using at build time, other arrangements must be made to track that information through the production deployment process. For the Go language, we are experimenting with automatically including a version manifest in every binary, so that deployment processes can scan binaries for dependencies that need upgrading. Go also makes that information available at runtime, so that servers can consult databases of known bugs and self-report to monitoring software when they are in need of upgrades.

Upgrading promptly is important, but it means adding new code to your project, which should mean updating your evaluation of the risks of using the dependency based on the new version. At minimum, you would want to skim the diffs showing the changes being made from the current version to the upgraded versions, or at least read the release notes, to identify the most likely areas of concern in the upgraded code. If a lot of code is changing, so that the diffs are difficult to digest, you can incorporate that fact into your risk-assessment update.

You will also want to rerun the tests you have written that are specific to your project, to ensure the upgraded package is at least as suitable for the project as the earlier version. Rerunning the package's own tests also makes sense. If the package has its own dependencies, it is entirely possible that your project's configuration uses versions of those dependencies (either older or newer ones) different from those used by the package's authors. Running the package's own tests can quickly identify problems specific to your configuration.

Again, upgrades should not be completely automatic. You must verify the upgraded versions are appropriate for your environment before deploying them.3


If a dependency seems too risky and you can't find a way to isolate it, the best answer may be to avoid it entirely, or at least to avoid the parts you have identified as most problematic.


If your upgrade process includes rerunning the integration and qualification tests you have already written for the dependency, so that you are likely to identify new problems before they reach production, then in most cases delaying an upgrade is riskier than upgrading quickly.

The window for security-critical upgrades is especially short. In the aftermath of the Equifax breach, forensic security teams found evidence that attackers (perhaps different ones) had successfully exploited the Apache Struts vulnerability on the affected servers on March 10, only three days after it was publicly disclosed, but they had run only a single whoami command.

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Watch Your Dependencies

Even after all that work, you are not done tending your dependencies. It's important to continue to monitor them and perhaps even re-evaluate your decision to use them.

First, ensure you keep using the specific package versions you think you are. Most dependency managers now make it easy or even automatic to record the cryptographic hash of the expected source code for a given package version and then to check that hash when redownloading the package on another computer or in a test environment. This ensures your build uses the same dependency source code you inspected and tested. These kinds of checks prevented the event-stream attacker, described earlier, from silently inserting malicious code in the already-released version 3.3.5. Instead, the attacker had to create a new version, 3.3.6, and wait for people to upgrade (without looking closely at the changes).

It is also important to watch for new indirect dependencies creeping in. Upgrades can easily introduce new packages upon which the success of your project now depends. They deserve your attention as well. In the case of event-stream, the malicious code was hidden in a different package, flatmap-stream, which the new event-stream release added as a new dependency.

Creeping dependencies can also affect the size of your project. During the development of Google's Sawzall15—a JIT'ed logs processing language—the authors discovered at various times that the main interpreter binary contained not just Sawzall's JIT but also (unused) PostScript, Python, and JavaScript interpreters. Each time, the culprit turned out to be unused dependencies declared by some library Sawzall did depend on, combined with the fact that Google's build system eliminated any manual effort needed to start using a new dependency. This kind of error is the reason the Go language makes importing an unused package a compile-time error.

Upgrading is a natural time to revisit the decision to use a dependency that's changing. It's also important to periodically revisit any dependency that isn't changing. Does it seem plausible that there are no security problems or other bugs to fix? Has the project been abandoned? Maybe it's time to start planning to replace that dependency.

It's also important to recheck the security history of each dependency. For example, Apache Struts disclosed different major remote code execution vulnerabilities in 2016, 2017, and 2018. Even if you have a list of all the servers that run it and update them promptly, that track record might make you rethink using it at all.

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Conclusion

Software reuse is finally here, and its benefits should not be understated. It has brought an enormously positive transformation for software developers. Even so, we have accepted this transformation without completely thinking through the potential consequences. The old reasons for trusting dependencies are becoming less valid at exactly the same time there are more dependencies than ever.

The kind of critical examination of specific dependencies outlined in this article is a significant amount of work and remains the exception rather than the rule. It's unlikely that any developers actually make the effort to do this for every possible new dependency. I have done only a subset of them for a subset of my own dependencies. Most of the time the entirety of the decision is, "Let's see what happens." Too often, anything more than that seems like too much effort.

The Copay and Equifax attacks are clear warnings of real problems in the way software dependencies are consumed today. We should not ignore the warnings. Here are three broad recommendations:

  1. Recognize the problem. If nothing else, this article hopefully convinced you that there is a problem here worth addressing. We need many people to focus significant effort on solving it.
  2. Establish best practices for today. Best practices are needed for managing dependencies using what is available today. This means working out processes that evaluate, reduce, and track risk, from the original adoption decision through production use. In fact, just as some engineers specialize in testing, others may need to specialize in managing dependencies.
  3. Develop better dependency technology for tomorrow. Dependency managers have essentially eliminated the cost of downloading and installing a dependency. Future development efforts should focus on reducing the cost of the kind of evaluation and maintenance necessary to use a dependency. For example, package-discovery sites might work to find more ways to allow developers to share their findings. Build tools should, at the least, make it easy to run a package's own tests. More aggressively, build tools and package-management systems could also work together to allow package authors to test new changes against all public clients of their APIs. Languages should also provide easy ways to isolate a suspect package.

There is a lot of good software out there. Let's work together to find out how to reuse it safely.

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References

1. Baldwin, A. Details about the event-stream incident. The npm Blog (Nov. 2018); https://bit.ly/2DRjySJ

2. Cox, R. Go & Versioning, 2018; https://research.swtch.com/vgo.

3. Cox, R. The principles of versioning in Go. GopherCon Singapore (May 2018); https://www.youtube.com/watch?v=F8nrpe0XWRg.

4. Cox, R. RE2: A principled approach to regular expression matching. Google Open Source Blog (Mar. 2010); https://bit.ly/2XoLFzC.

5. Cox, R. Regular expression matching with a trigram index or how Google Code Search worked. Swtch.com (Jan. 2012); https://swtch.com/~rsc/regexp/regexp4.html.

6. Facebook. Infer: A tool to detect bugs in Java and C/ C++/Objective-C code before it ships; https://fbinfer.com/.

7. GNU Project. GNU General Public License, version 1, 1989; https://www.gnu.org/licenses/old-licenses/gpl-1.0.html.

8. Go Project. Go 1 and the future of Go programs, 2013; https://golang.org/doc/go1compat.

9. Google Open Source. Using third-party licenses; https://opensource.google.com/docs/thirdparty/licenses/#banned.

10. Hipp, D. R. How SQLite is tested; https://www.sqlite.org/testing.html.

11. Lacasse, N., Open-sourcing gVisor, a sandboxed container runtime. Google Cloud (May 2018); http://bit.ly/2wzA84D.

12. Langley, A. Chromium's seccomp sandbox. ImperialViolet (Aug. 2009); https://www.imperialviolet.org/2009/08/26/seccomp.html.

13. National Institute of Standards and Technology. National Vulnerability Database—Search and Statistics; https://nvd.nist.gov/vuln/search.

14. Pike, R. Go Proverbs, 2015; https://go-proverbs.github.io/.

15. Pike, R., Dorward, S., Griesemer, R. and Quinlan, S. Interpreting the data: Parallel analysis with Sawzall. Scientific Programming J. 13, 4 (2005), 277–298; https://doi.org/10.1155/2005/962135.

16. Potapenko, A. Testing Chromium: ThreadSanitizer v2, a next-gen data race detector. Chromium Blog (Apr. 2014); http://bit.ly/2WN29o0.

17. Potvin, R., Levenberg, J. Why Google stores billions of lines of code in a single repository. Commun. ACM 59, 7 (July 2016), 78–87; https://doi.org/10.1145/2854146.

18. Reis, C. Multi-process architecture. Chromium Blog (Sept. 2008); https://blog.chromium.org/2008/09/multi-process-architecture.html.

19. SpotBugs: Find bugs in Java programs; https://spotbugs.github.io/.

20. Thompson, K. Reflections on trusting trust. Commun. ACM 27, 8 (Aug. 1984), 761–763; https://doi.org/10.1145/358198.358210.

21. U.S. House of Representatives Committee on Oversight and Government Reform. The Equifax Data Breach, Majority Staff Report, 115th Congress (Dec. 2018); http://bit.ly/2Gf53IJ.

22. Willis, N. A single Node of failure. LWN.net (Mar. 2016); https://lwn.net/Articles/681410/.

23. Winters, T. SD-8: Standard library compatibility, C++ standing document, 2018; http://bit.ly/2QNhT5k.

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Author

Russ Cox leads the development of the Go programming language at Google, with a current focus on improving the security and reliability of using software dependencies. With Jeff Dean, he created Google Code Search. He worked for many years on the Plan 9 from Bell Labs operating system.


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