On the static vs. dynamic side, I would always opt for the dynamic when it can guarantee me no false positives, even if the results are incomplete. It is pretty much impossible to deploy a tool that produces lots of false positives because developers usually will reject it at some point and question every result.
In my experience TSan finds all possible races if it's built with unit tests and unit tests provide enough coverage. If unit tests don't provide enough coverage then there are likely other bugs lurking beyond just data races.
The code coverage analysis I've seen in the past has only evaluates what code is run at least once, but how do you get coverage analysis to evaluate the coverage of what code runs concurrently with what other code effectively enough to get confidence that you'll detect data races?
For example, if you've got a cache that safely handles concurrent use for most operations, but has a data race when two threads both cache miss on the same key within a short window of each other, are there kinds of code-coverage analysis that could theoretically point out that none of your tests involve a cache miss of the same key in two different threads?
I've seen concurrency permutation testing tools, that can reorder the sequence of operations between different threads searching for a permutation that causes a data race, but that relies on actually executing the potentially-racy behaviour at all during testing, right?
I guess this is also what fuzzing is about, but are there tools or techniques you can use to increase your confidence in how well your tests cover the space of concurrent use of your data structures?
I disagree. Maintenance cost doesn't need to be minimized. It needs to be reasonable.
The goal should be to produce high quality software. High quality software is easy to understand and easy to instrument with tools.
(I'd also argue that unit testing can reduce the quality of the software as it makes the internals of the architecture more costly to change).
I think it's more irresponsible to produce something that the customer isn't going to be able to use for many years or that needs a patch before it's even released.
Coverage can be misleading. Maybe there is some function being executed in parallel:
int global;
void foo() {
int local;
if (complex_condition)
bar(&local);
else
bar(&global);
}
So without true 100% test coverage, it is possible races are being missed by dynamic tools, and true 100% test coverage is probably not possible or feasible in large complex projects.
Yes.
> true 100% test coverage is probably not possible or feasible in large complex projects.
I have yet to encounter a single large complex project where 100% test coverage is not possible. I have encountered such projects where 100% test coverage required massive refactoring of the code to facilitate testing and doing so resulting in fixing most customer complaints received.
For instance, pretty much every piece of code (whether native or JS) I've seen is programmed defensively, assuming that for some reason invariants can be broken - something that typically cannot happen unless there is a memory corruption involved. Testing all these defensive programming branches is not possible without introducing memory corruptions.
Removing those "impossible" code branches is a bad idea. "Impossible" things happen millions of times a day to Firefox.
I used to work for LimeWire, which had tens of millions of users. We were sometimes seeing bug reports of "impossible" stack traces. They went down dramatically when we added a splash screen that calculated SHA-1 hashes of all of the DLLs and JARs before using the reflection API to load and start the actual application. Given that the "corrupted" JARs were still presumably passing the JARs' CRC-32 checksums and bytecode verification at class load time, I think we were most often just detecting dodgy RAM that would later have caused "impossible" errors by corrupting bytecode or native code after bytecode verification.
If you're writing code to defend against "dodgy RAM" whose symptom is a corrupt SHA then it's super trivial to test that code: have a valid JAR file and adjust its content so that it will still "load" but isn't the file you expect. Then verify that the program refuses to load the JAR.
Sorry for being a bit slow today. but what's the difference between "load"ing and loading? You clearly understand that you're using two different definitions of load, otherwise you wouldn't have put one in quotes. (Also, it would be self-contradictory if the two were identical.) However, I can't for the life of me figure out two different definitions for "load" which both involve CRC-32 and bytecode verification checks, and yet aren't identical.
If I'm correct, we saw a dramatic reduction in the "impossible" errors not because we were detecting the actual problem that caused the errors (corruption of internal JVM buffers after bytecode validation), but instead because our SHA-1 failures (loading chunks of files into our own buffers and checksumming those) were highly correlated with the "impossible" failures because they had the same underlying cause.
In our case, the JVM loaded the code from the JAR, which, unless I misunderstand Sun/Oracle's classloader, means that both CRC-32 checksums and bytecode verification pass, even for unsigned JARs. The chances of random bit flips passing CRC-32 checks are 4 billion to one. The odds of random bit flips passing both a CRC-32 check and bytecode verification are probably at least quadrillions to one. I'm pretty sure the corruption was happening minutes to hours after the classes actually got loaded by the JVM.
In other words, I think we were getting saved by SHA-1 summing a bunch of large files being a poor-man's memtest86: writing semi-random-ism patterns to big chunks of memory and verifying you can read back what you wrote. Failing this correlates heavily with seeing "impossible" errors at runtime, but we weren't directly examining the classloader's internal buffers that were the actual cause of the errors.
On a side note, modern OSes should really have a low rate memory checker built into the kernel's memory manager. It's a crutch, but cheap consumer IoT devices and WiFi APs aren't getting ECC any time soon.
That is:
1) the JAR file successfully loads by the JVM -- as in it will "load"
2) the JAR file doesn't pass the SHA checksum requirement you've added -- as in it isn't the file you expect.
As a dynamic tool, if a race is observed during execution by TSan, the race is very likely to be real. But it is impossible to "observe" execution for every part of a large code base, and so real races are likely still hiding in some edge case that TSan did not execute.
Static Analysis tools are interesting in that they have the opposite problem. Static tools can detect races across the entire code base, but without runtime information it is difficult to know if the races are real. This leads to more real races being detected, but also comes with more false positives.
There is a lot of interesting working being done on data race detection and how we can bring these two sides closer together to find more real races, without overwhelming developers with false positives.