Showing posts with label exceptions. Show all posts
Showing posts with label exceptions. Show all posts

Friday, February 16, 2018

Robustness Testing of Autonomy Software (ASTAA Paper Published)

I'm very pleased that our research team will present a paper on Robustness Testing of Autonomy Software at the ICSE Software Engineering in Practice session in a late May. You can see a preprint of the paper here:  https://goo.gl/Pkqxy6

The work summarizes what we've learned across several years of research stress testing many robots, including self-driving cars.

ABSTRACT
As robotic and autonomy systems become progressively more present in industrial and human-interactive applications, it is increasingly critical for them to behave safely in the presence of unexpected inputs. While robustness testing for traditional software systems is long-studied, robustness testing for autonomy systems is relatively uncharted territory. In our role as engineers, testers, and researchers we have observed that autonomy systems are importantly different from traditional systems, requiring novel approaches to effectively test them. We present Automated Stress Testing for Autonomy Architectures (ASTAA), a system that effectively, automatically robustness tests autonomy systems by building on classic principles, with important innovations to support this new domain. Over five years, we have used ASTAA to test 17 real-world autonomy systems, robots, and robotics-oriented libraries, across commercial and academic applications, discovering hundreds of bugs. We outline the ASTAA approach and analyze more than 150 bugs we found in real systems. We discuss what we discovered about testing autonomy systems, specifically focusing on how doing so differs from and is similar to traditional software robustness testing and other high-level lessons.

Authors:
Casidhe Hutchison
Milda Zizyte
Patrick Lanigan
David Guttendorf
Mike Wagner
Claire Le Guoes
Philip Koopman


Monday, May 16, 2016

Robustness Testing

I have been doing research in the area of robustness testing for many years, and once in a while I have to explain how that approach to testing fits into the bigger umbrella of fault injection and related ideas. Here's a summary of typical approaches (there are many variations and extensions beyond these as you might imagine).  At the end is a description of the robustness testing work my group has been doing over many years.

Mutation Testing:
Goal: Evaluate coverage/effectiveness of an existing test suite. (Also known as "bebugging.")
Approach: Modify System under Test (SuT) with a hypothetical bug and see if an existing test suite finds it.
Narrative: I have a test suite. I wonder how thorough it is?  Let me put a bug (mutation) into my code and see if my test suite finds it. If it finds all the mutations I insert, maybe my test suite is thorough.
Fault Model: Source code bug that is undetected by testing
Strengths: Can find problems even if code was already 100% branch-covered in test suite (e.g., mutate a comparison to > instead of >= to see if test suite exercises the equality case for that comparison)
Limitations: Requires an existing test suite (but, can combine with automated test generation to create additional tests automatically). Effectiveness heavily depends on inserting realistic mutations, which is not necessarily so easy.

Classical Fault Injection Testing:
Goal: Determine robustness of SuT when its code or data is corrupted.
Approach: Corrupt the binary image of the SuT code or corrupt data during run-time to see if system crashes, is unsafe, or tolerates the fault.
Narrative: I have a running system. I wonder what happens if I flip a bit in the data -- does the system crash?  I wonder what happens if I corrupt the compiled code -- does the system act in an unsafe way?
Fault Model: Hardware bit flip or software-based memory corruption.
Strengths: Can find realistic failures caused by single-event upsets, hardware faults, and software defects that corrupt computational state.
Limitations: The fault model is memory and program bit-level corruption. Fault injection testing is useful for high-integrity systems deployed in large volume (they have to survive even very infrequent faults), and essential for aviation and space systems that will see high rates of single event upsets.  But it is arguably a bit excessive for non-safety-critical applications.

Robustness Testing:
Goal: Determine robustness of SuT when it is fed exceptional or unusual values.
Approach: Corrupt the inputs to the SuT during run-time to see if system crashes, is unsafe, or tolerates the fault.
Narrative: I have a running system or subsystem. I wonder what happens if the inputs from other components or sensors have garbage, unusual, or random values -- does the system crash or act in an unsafe way?
Fault Model: Some other module than the SuT has a bug that results in exceptional data being sent to the SuT.
Strengths: Can find realistic failures caused by likely run-time faults such as null pointers, NaNs (Not-a-Number floating point values), corrupted input data, and in general faults in modules that are not the SuT, but rather other software or sensors present in the system that might have bugs that generate exceptional data.
Limitations: The fault model is generally that some other piece of software has a bug and that bug will generate bad data that kills the SuT.  You have decide how likely that is and whether it's OK in such a case for the SuT to misbehave. We have found many situations in which such test results are important, even in systems that are not safety critical.

Fuzzing:
A classical form of robustness testing is "fuzzing," in which random inputs are tossed into a system to see what happens rather than carefully selected specific input values. My research group's work centers on finding efficient ways to do robustness testing so that fewer tests are needed to find system-killer values.

Ballista:
The Ballista project pioneered efficient robustness testing in the late 1990s, and is still active today on stress testing robots and autonomous vehicles.

Two key ideas of Ballista are:
  • Have a dictionary of interesting exceptional values so you don't have to stumble onto them by chance (e.g., just try a NULL pointer straight out rather than wait for a random number generator to happen to generate a zero value as a fuzzing input value)
  • Make it easy to generate tests by basing that dictionary on the data types taken by a function call instead of the function being performed.  So we don't care if it is a memory management function or a file write being tested - we just say for example that if it's a memory pointer, let's try NULL as an input value to the function. This gets us excellent scalability and portability across systems we test.
A key benefit of Ballista and other robustness testing approaches is that they look for holes in the code itself, rather than holes in the test cases. Consider that most test coverage approaches (including mutation testing) are interested in testing all the code that is there (which is a good thing!). In contrast, robustness testing goes beyond code coverage to find the places where you should have had code to handle exceptional situations, but that code is missing. In other words, robustness testing often finds bugs due to missing code that should have been there. We find that it is pretty typical for software to be non-robust unless this type of testing has been done it identify such problems.

You can find more about our research at the Stress Tests for Autonomy Architectures (STAA) project page, which includes video of what goes wrong when you stress test a couple robotic systems:

Monday, March 7, 2016

Multiple Returns and Error Checking

Summary: Whether or not to allow multiple returns from a function is a controversial matter, but I recommend having a single return statement AND avoiding use of goto.

Discussion:

If you've been programming robust embedded systems for a while, you've seen code that looks something like this:

int MyRoutine(...)
{ ...
  if(..something fails..) { ret = 0; }
  else 
  { .. do something ...
    if(..somethingelse fails..) {ret = 0;}
    else 
    { .. do something ..
      if(...yetanotherfail..) {ret = 0;}
      else 
      { .. do the computation ...
        ret = value; 
      }
    }
  }
  // perform default function
  return(ret);
}

(Note: the "ret = 0" and "ret = value" parts are usually more complex; I'm keeping this simple for the sake of the discussion.)

The general pattern to this code is doing a bunch of validity checks and then, finally, in the most deeply nested "else" doing the desired action. Generally the code in the most deeply nested "else" is actually the point of the entire routine -- that's the action that you really want to take if all the exception checks pass.  This type of code shows up a lot in a robust embedded system, but can be difficult to understand.

This problem has been dealt with a number of times and most likely every reasonable idea has been re-invented many times. But when I ran up against it in a recent discussion I did some digging and found out I mostly disagree with a lot of the common wisdom, so here is what I think.

A common way to simplify things is the following, which is the guard clause pattern described by Martin Fowler in his refactoring catalog (an interesting resource with accompanying book if you haven't run into it before.)

int MyRoutine(...)
{ ...
  if(..something fails..)     {return(0);}
  if(..somethingelse fails..) {return(0);}
  if(...yetanotherfail..)     {return(0);}
  // perform default function
  return(value);
}

Reasonable people can (and do!) disagree about how to handle this.   Steve McConnell devotes a few pages of his book Code Complete (Section 17.1, and 17.3 in the second edition) that covers this territory, with several alternate suggestions, including the guard clause pattern and a discussion that says maybe a goto is OK sometimes if used carefully.

I have a somewhat different take, possibly because I specialize in high-dependability systems where being absolutely sure you are not getting things wrong can be more important than subjective code elegance. (The brief point of view is: getting code just a little klunky but obviously right and easy to check for correctness is likely to be safe. Getting code to look nice, but with a residual small chance of missing a subtle bug might kill someone. More this line of thought can be found in a different posting.)

Here is a sketch of some of the approaches I've seen and my thoughts on them:

(1) Another Language. Use another language that does a better job at this.  Sure, but I'm going to assume that you're stuck with just C.

(2) Guard Clauses.  Use the guard class pattern with early returns as shown above . The downside of this is that it breaks the single return per function rule that is often imposed on safety critical code. While a very regular structure might work out OK in many cases, things get tricky if the returns are part of code that is more complex. Rather than deal with shades of gray about whether it is OK to have more than one return, I prefer a strict single-return-per-function rule. The reason is that it avoids having to waste a lot of time hashing out when multiple returns are OK and when they aren't -- and the accompanying risk of getting that judgment call wrong. In other words black-and-white rules take less interpretation.

If you are building a safety critical system, every subjective judgement call as to what is OK and what is not OK is a chance to get it wrong.  (Could you get airtight design rules in place? Perhaps. But you still need to check them accurately after every code modification. If you can't automate the checks, realistically the code quality will likely degrade over time, so it's a path I prefer not to go down.)

(3) Else If Chain.  Use an if/else chain and put the return at the end:

int MyRoutine(...)
{ ...
  if(..something fails..)          {ret=0;}
  else if(..somethingelse fails..) {ret=0;}
  else if(...yetanotherfail..)     {ret=0;}
  else { // perform default function
         ret = value;
        }
  return(ret);
}

Wait, isn't this the same code with flat indenting?  Not quite.  It uses "else if" rather than "else { if". That means that the if statements aren't really nested -- there is really only a single main path through the code (a bunch of checks and the main action in the final code segment), without the possibility of lots of complex conditions in the "if" sidetrack branches.

If your code is flat enough that this works then this is a reasonable way to go. Think of it as guard clauses without the multiple returns.  The regular "else if" structure makes it pretty clear that this is a sequence of alternatives, or often a set of "check that everything is OK and in the end take the action." However, there may be cases in which the code logic is difficult to flatten this way, and in those cases you need something else (keep reading for more ideas).  The trivial case is:

(3a) Simplify To An Else If Chain.  Require that all code be simplified enough that an "else if" chain works.  This is a nice goal, and my first choice of style approaches.  But sometimes you might need a more capable and flexible approach.  Which brings us to the rest of the techniques...

(4) GOTO. Use a "goto" to break out of the flow and jump to the end of the routine.  I have seen many discussion postings saying this is the right thing to do because the code is cleaner than lots of messy if/else structures. At the risk of being flamed for this, I respectfully disagree. The usual argument is that a good programmer exhibiting discipline will do just fine.  But that entirely misses what I consider to be the bigger picture.  I don't care how smart the programmer who wrote the code is (or thinks he is).  I care a lot about whoever has to check and maintain the code. Sure, a good programmer on a good day can use a "goto" and basically emulate a try/throw/catch structure.  But not all programmers are top 10 percentile, and a lot of code is written by newbies who simply don't have enough experience to have acquired mature judgment on such matters. Beyond that, nobody has all good days.

The big issue isn't whether a programmer is likely to get it right. The issue is how hard (and error-prone) it is for a code reviewer and static analysis tools to make sure the programmer got it right (not almost right, or subtly wrong). Using a goto is like pointing a loaded gun at your foot. If you are careful it won't go off. But even a single goto shoves you onto a heavily greased slippery slope. Better not to go there in the first place. Better to find a technique that might seem a little more klunky, but that gets the job done with minimum fuss, low overhead, and minimal chance to make a mistake. Again, see my posting on not getting things wrong.

(Note: this is with respect to unrestricted "goto" commands used by human programmers in C. Generated code might be a different matter, as might be a language where "goto" is restricted.)

(5) Longjmp.  Use setjmp/longjmp to set a target and then jump to that target if the list of "if" error checks wants to return early. In the final analysis this is really the moral equivalent of a "goto," although it is a bit better controlled. Moreover, it uses a pointer to code (the setjmp/longjmp variable), so it is an indirect goto, and that in general can be hazardous. I've used setjmp/longjmp and it can be made to work (or at least seem to work), but dealing with pointers to code in your source code is always a dicey proposition. Jumping to a corrupted or uninitialized pointer can easily crash your system (or sometimes worse).  I'd say avoid using this approach.

I've seen discussion forum posts that wrap longjmp-based approaches up in macros to approximate Try/Throw/Catch. I can certainly appreciate the appeal of this approach, and I could see it being made to work. But I worry about things such as whether it will work if the macros get nested, whether reviewers will be aware of any implementation assumptions, and what will happen with static analysis tools on those structures. In high-dependability code if you can't be sure it will work, you shouldn't do it.

(6) Do..While..Break. Out of the classical C approaches I've seen, the one I like the most (beyond else..if chains) is using a "do..while..break" structure:

int MyRoutine(...)
{ ...
  do { //  start error handling code sequence
    if(..something fails..)     {ret=0; break;}
    if(..somethingelse fails..) {ret=0; break;}
    if(...yetanotherfail..)     {ret=0; break;}
    // perform default function
    ret = value;
  } while (0); // end error handling code sequence
  return(ret);
}

This code is a hybrid of the guard pattern and the "else if" block pattern. It uses a "break" to skip the rest of the code in the code block (jumping to the while(0), which always exits the loop). The while(0) converts this structure from a loop into just a structured block of code with the ability to branch to the end of the code block using the "break" keyword. This code ought to compile to an executable that is has essentially identical efficiency to code using goto or code using an else..if chain. But, it puts an important restriction on the goto-like capability -- all the jumps have to point to the end of the do..while without exception.

What this means in practice is that when reviewing the code (or a change to the code) there is no question as to whether a goto is well behaved or not. There are no gotos, so you can't make an unstructured goto mistake with this approach.

While this toy example looks pretty much the same as the "else if" structure, an important point is that the "break" can be placed anywhere -- even deeply within a nested if statement -- without raising questions as to what happens when the "break" is hit. If in doubt or there is some reason why this technique won't work for you, I'd suggest falling back on restructuring the code so "else if" or this technique works if the code gets too complex to handle. The main problem to keep in mind is that if you nest do..while structures the break will un-nest only one level.

I recognize that this area falls a little bit into a matter of taste and context. My taste is for code that is easy to review and unlikely to have bugs. If that is at odds with subjective notions of elegance, so be it. In part my preference is to outlaw the routine use of techniques that require manual analysis to determine of a potentially unsafe structure is being used the "right" way. Every such judgement call is a chance to get it wrong, and a distraction of human reviewer attention away from more important things. And I dislike arguments of the form that a "good" and experienced programmer won't make a mistake. It is just too easy to miss a subtle bug, especially when you're modifying code in a hurry.

If you've run into another way to handle this problem let me know.

Update 11/2022:  Olayiwola Ayinde has a similar take, pointing out that multiple exits make it too easy to forget to de-allocate resources that might have been allocated at the start of a procedure:

Monday, December 2, 2013

Why You Need A Software-Specific Test Plan

Summary: Pretty much every embedded system goes through some sort of system test. But, you need to do software-specific testing in addition to that.  For example, what if you ship a system with the watchdog timer accidentally turned off?



In essentially every embedded system there is some sort of product testing. Typically there is a list of product-level requirements (what the product does), and a set of tests designed to make sure the product works correctly. For many products there is also a set of tests dealing with fault conditions (e.g., making sure that an overloaded power supply will correctly shed load). And many companies think this is enough .. but I've found that such tests usually fall short in many cases.

The problem is that there are features built into your software that are difficult or near-impossible to test in traditional product-level testing.  Take the watchdog timer for example. I have heard more than one developer say that there was a case where a product shipped (at least one version of a product) with the watchdog timer accidentally turned off. How could this happen?  Easy: a field problem is reported; developer turns off watchdog to do single-step debugging; bug found and fixed; forgot to turn the watchdog back on; product test doesn't have a way to intentionally crash the software to see if the watchdog is working; new software version ships with watchdog timer still turned off.

Continuing with the watchdog example, how do you solve this? One way is to include user-accessible functions that exercise the watchdog timer by intentionally crashing the software. Sounds a bit dangerous, especially if you are worried about security. More likely you'll need to have some separate, special way of testing functions that you don't want visible to the end user. And you'll need a plan for executing those tests.

And ... well, here we are, needing a Software Test Plan in addition to a Product Test Plan. Maybe the software tests are done by the same testers who do product test, but that's not the point. The point is you are likely to need some strategy for testing things that are there not because the end product user manual lists them as functions, but rather because the software requirements say they are needed to provide reliability, security, or other properties that aren't typically thought of as product functions. ("Recovers from software crashes quickly" is typically not something you boast about in the user manual.) For similar reasons, the normal product testers might not even think to test such things, because they are product experts and not software experts.

So to get this right the software folks are going to have to work with the product testers to create a software-specific test plan that tests what the software requirements need to have tested, even if they have little directly to do with normal product functions. You can put it in product test or not, but I'd suggest making it a separate test plan, because some tests probably need to be done by testers who have particular skill and knowledge in software internals beyond ordinary product testers. Some products have a "diagnostic mode" that, for example, sends test messages on a network interface. Putting the software tests here makes a lot of sense.

But for products that don't have such a diagnostic mode, you might have to do some ad hoc testing before you build the final system by, for example, manually putting infinite loops into each task to make sure the watchdog picks them up. (Probably I'd use conditional compilation to do that -- but have a final product test make sure the conditional compilation flags are off for the final product!)

Here are some examples of areas you might want to put in your software test plan:

  • Watchdog timer is turned on and stays turned on; product reboots as desired when it trips
  • Watchdog timer detects timing faults with each and every task, with appropriate recovery (need a way to kill or delay individual tasks to test this)
  • Tasks and interrupts are meeting deadlines (watchdog might not be sensitive enough to detect minor deadline misses, but deadline misses usually are a symptom of a deeper problem)
  • CPU load is as expected (even if it is not 100%, if you predicted an incorrect number it means you have a problem with your scheduling estimates)
  • Maximum stack depth is as expected
  • Correct versions of all code have been included in the build
  • Code included in the build compiles "clean" (no warnings)
  • Run-time error logs are clean at the end of normal product testing
  • Fault injection has been done for systems that are safety critical to test whether single points of failure turn up (of course it can't be exhaustive, but if you find a problem you know something is wrong)
  • Exception handlers have all been exercised to make sure they work properly. (For example, if your code hits the "this can never happen" default in a switch statement, does the system do something reasonable, even if that means a system reset?)
Note that some of these are, strictly speaking, not really "tests." For example, making sure the code compiles free of static analysis warnings isn't done by running the code. But, it is properly part of a software test plan if you think of the plan as ensuring that the software you're shipping out meets quality and functionality expectations beyond those that are explicit product functions.

And, while we're at it, if any of the above areas aren't in your software requirements, they should be. Typically you're going to miss tests if there is nothing in the requirements saying that your product should have these capabilities.

If you have any areas like the above that I missed, please leave a comment.  I welcome your feedback!

Friday, January 25, 2013

Exception Handling Fishbone Diagram

Exception handling is the soft underbelly of many software systems. A common observation is that there a lot more ways for things to go wrong than there are for them to go right, so designing and testing for exceptional conditions is difficult to do well. Anecdotally, it is also where you find many bugs that, while infrequently encountered, can cause dramatic system failures. While not every software system has to be bullet-proof, embedded systems often have to be quite robust. And it's a lot easier to make a system robust if you have a checklist of things to consider when designing and testing.

Fortunately, just such a checklist already exists. Roy Maxion and Bob Olszewski at Carnegie Mellon created a structured list of exceptional conditions to consider when designing a robust system in the form of a fishbone diagram (click on the diagram to see the full detail in a new window).




(Source: Maxion & Olszewski, Improving Software Robustness with Dependability Cases, FTCS, June 1998.)

The way to read this diagram is that an exception failure could be caused by any of the general causes listed in the boxes at the end of the fish-bone segments, and the arrows into each fishbone are more specific examples of those types of problems.

If you don't have the picture handy, a way to remember the main branches is:
C - Computational problem
H - Hardware problem
I  - I/O and file problem
L - Library function problem
D - Data input problem
R - Return value problem
E - External user/client problem  (in embedded systems this may include control network exceptions)
N - Null pointer or memory problems

There isn't a silver bullet for exception handling -- getting it right takes attention to detail and careful work. But, this fishbone diagram does help developers avoid missing exception vulnerabilities. You can read more about the idea and the human subject experiments showing its effectiveness in the free on-line copy of their conference paper: Improving Software Robustness with Dependability Cases,

You can read more detail in the (non-free unless you have a subscription) journal paper:
Eliminating exception handling errors with dependability cases: a comparative, empirical study, IEEE Transactions on Software paper, Sept. 2000.  http://dx.doi.org/10.1109/32.877848



Static Analysis Ranked Defect List

  Crazy idea of the day: Static Analysis Ranked Defect List. Here is a software analysis tool feature request/product idea: So many times we...