The issues with the MAX were also clearly preventable and there were multiple failures of the systems (regulators, internal reviews, etc.) that were in place to catch these kinds of issues.
But as you point out, the aeronautical industry has an excellent track record for software reliability, if you evaluate reliability by hull losses. By other metrics, it's a bit more debatable (eg. the integer overflow for Dreamliners such that they need to be restarted at least every 248 days), but still keeps people moving safely.
My primary point is that many people look at these failures and incorrectly conclude that the processes in place are objectively terrible and below average. This leads to them discounting the processes in these systems in favor of policies from vastly less reliable systems that they think are quality-focused or "best practices" because they, fairly, think "bad" in a safety-critical context means the same as regular "bad", so regular "amazing" is clearly better. In truth, "unconscionable deathtrap" and "gross criminal negligence" in the airplane world is more of a synonym for "amazing beyond belief" in the rest of the software industry. The correct takeaway is understanding that regular "amazing" is actually orders of magnitude worse than "unconscionable deathtrap" and is thus completely inadequate for the job. As a corollary, if you do not think you are doing "way better than amazing" you are probably not doing an adequate job in these contexts.
To reiterate, the solutions are really really good, unfortunately the problems are really really really really hard.
Uptime is not a comparable metric in any way. Aircraft computers often reboot every flight or every day. AWS downtimes don’t typically result in fatalities. The fall time of the 737 MAX before it impacts isn’t ‘downtime’, and simply cannot be used to summarize the reliability of aviation software as a whole. Arriving at 250000x this way makes it a meaningless number, and you didn’t account for the bug in the linked article in your reliability estimate at all.
I somewhat agree that the metric I chose is somewhat sloppy, but you can afford to be sloppy when you are comparing things with such disparate outcomes. Sure, maybe we are not comparing a 1 story house to a 50 story skyscraper, it is only a 30 story skyscraper, but that has little impact on the fact that they are fundamentally different and to declare that they are even remotely comparable is a massive category error.
I, however, disagree that "uptime" is a nonsense metric, though there are absolutely better ones. "Uptime" in this context means duration/probability of critical operational failure which is an extremely relevant metric. That AWS does not result in fatalities during critical operational failure has no bearing on whether critical operational failure occurred or not, it just means that it matters less. A valid quibble is that I am using crashes as a proxy for failure which discounts critical software failures that did not cause critical operational failure due to non-software redundancy, but again, the outcomes are so disparate it beggars belief that this would bridge the gap.
As for aircraft computers being rebooted frequently, true. So? I am comparing full system reliability during operation, not individual components. It is not like individual AWS servers run indefinitely; they are rebooted frequently, but the system as a whole stays operational due to redundancy and migration.
The reliability estimate does account for the bug. The bug did not cause a critical operational failure. It could cause a critical operational failure in an extremely unlikely case if it remained undetected and no measures were taken to avoid or correct for it. However, it was detected and countermeasures have been put into place, so the processes in place continue to achieve their intended goal of preventing critical operational failure. So, the outcome-based estimate continues to be accurate.
Just to be clear, an outcome-based estimate is not perfect. By its nature, it only looks at the past, so has no true predictive power. You can not use an outcome-based estimate to predict the effects of process changes. However, it is a relatively unbiased way of evaluating if prior processes were effective which we can use to inform us which processes of the past were actually effective or not and the effects of process changes.
Yes it did cause operational failure! An airplane turning itself the wrong direction is an outcome, and an extremely serious one.
There was a bug that put people at risk, and you are saying that just because a human caught it and it didn’t crash the plane, it doesn’t count as unreliable?! You’ve just rationalized ignoring all bugs that don’t cause fatal crashes when estimating software reliability. This is making your point weaker, not stronger. You’re arguing that software reliability should only be measured by fatalities. If you really want to go that way, one might conclude that “normal software” like AWS is infinitely more reliable that aviation software, because it never killed anyone. By discounting any bugs that don’t lead to plane crashes, you are undermining your own claim that aviation software is “250,000x” more reliable than other kinds of software.
This kind of analysis- the insistence that reliability is high because death has not occurred often- has played a major role in several high profile accidents. In the shuttle disaster, for one, it was specifically called out that reliability estimates were exaggerated. The Therac-25 incident is another case where engineers failed to understand what happened for a long time due to vastly exaggerated reports of the system’s risks and reliability.
No, uptime still makes zero sense to compare, it is a nonsense metric in this context. Uptime is a measure of continuous operation, and planes aren’t in continuous operation. Simple as that. It’s a metric that does not apply to aircraft, no matter how you spin it.
There are multiple cases of major software failure in military and aviation from systems being in continuous operation for too long. There was a thread just the other day about an airline’s safety procedures specifically requiring in writing a reboot every 30 days due to known bugs.
And you’re ignoring that the 737 MAX did not suffer system operational failure. The system didn’t go down, it kept working. If the system had gone down, those people might have survived. The crash happened precisely because the buggy system kept working. If you want to count the downtime of the system, you maybe ought to count all the flight hours the plane would have flown since the crash, rather than using a bogus concept of only the ratio of fall time to all flight hours to estimate industry reliability. Again, that ratio is completely and utterly meaningless as a proxy for software reliability.
“Downtime” in normal software is not always caused by catastrophic failure, sometimes it’s due to maintenance and upgrades, sometimes it’s due to low performance, sometimes it’s caused by people actively attempting to fix bugs during uptime. None of those things happen during an airplane’s uptime.
> One of the only ways to compare processes and not be tricked by fancy words, especially as a non-expert, is to look and compare actual outcomes.
I’m not arguing against comparing outcomes. I’d agree that looking at outcomes is a good thing, if, and only if, you are actually fair about seeing all outcomes. I’m suggesting that pointing at the more easily verifiable volume of testing effort and safety concern in regards to aviation software, when compared to how much testing and verification happens on ‘normal software’, might adequately persuade someone who didn’t know it that aviation software testing and bugs are taken way more seriously than testing and bugs of web apps are.
The difference in reliability between normal software and airplane software is so vast that "best practices" from normal software can not be applied to airplane software since that would be gross criminal negligence. To explain, in the 10 years prior to the 737-MAX problems there were 50,000,000 flights and software was not implicated in a single passenger air fatality. The average flight is ~5,000 KM which is ~4-5 hours. So, in ~250,000,000 flight-hours, there were two crashes due to software. A plane takes ~3 minutes to fall from cruising altitude, so we can model this as a downtime of 6 minutes per 250,000,000 hours which gives us an downtime of 1 in 2,500,000,000 or a 99.99999996% uptime (yes, that is 9 9s). In contrast, I think most software people would agree that AWS is high quality. The AWS SLA specifies a 99.99% uptime (1 in 10,000 downtime). So, by this metric, airplane software is 250,000x more reliable than normal high quality software.
The point of this is that the standard for airplanes is almost inconceivably high compared to normal software. To think that they are incompetent or suggest that all they need to do is adopt X or Y common-sense/best-practice is a gross misunderstanding of what is being done and what needs to be done to improve. It would be like someone trying to tell a civil engineer making a 50-story skyscraper that they really need to adopt high quality wood construction techniques from makers of doghouses. To actually improve it, you need to consider practices 250,000x better than "best practices" and go from there.
To put it another way, the solutions are actually really really good, unfortunately the problems are really really really really hard.