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These calls ask the Operating Environment to terminate our app if any assert has failed. The Operating Environment then aborts our app. In the Crash Report we get a:. If the problems have resulted directly from some IO problem file or network access for example or some human input problem such as a bad date value then we should not crash.
Such problems are better dealt with by logging, error handling, user alerts, and IO retries. The thing to keep in mind is that any code that touches upon the policies the Operating Environment has guards for is a good candidate for automated testing. Test cases always feel over-the-top when applied to trivial programs. But consider a large program that has an extensive Info. A new version of the app is called for so another Info. Then keeping the privilege settings in sync between the different build targets becomes an issue.
The UI test code shown here which merely launches the camera can catch such problems easily so has practical business value. Similarly, if our app has a lot of low-level code and then is ported from iOS to tvOS, for example, how much of that OS-sensitive code is still applicable? Unit testing a top level function comprehensively for different design concerns can pay off the effort invested in it before delving deeper and unit testing the underlying helper function calls in our code base.
For testing camera access, we have written a simple UI test case which just presses the Take Photo button by means of an accessibility identifier takePhotoButton. We have a rich set of tools available to assist crash dump analysis.
When used properly they can save a huge amount of time. Xcode provides much help out of the box. However, using and comprehending the information Xcode tools provide is daunting. In later chapters, we go through examples showing the use of such tools. Additionally there are command line tools provided as standard in macOS.
These are helpful when used in particular usage scenarios when we already know what we want to find out. We shall go through specific scenarios and show how the tools are used. Next come software tools that help us reverse engineer programs. Sometimes we cannot get our program to work with a third party library. Reverse engineering is where an already built binary such as an application, library, or helper process daemon , is studied to determine how it works. For a specific Object, we might want to find out:.
We generally do not want to know everything, only something specific to help build a hypothesis. Once we have a hypothesis, we will test it in relation to the crash dump we are dealing with. How far should we go with reverse engineering, and how much money and time to invest in it is a good question.
We offer the following recommendation:. One of the great things about the Objective-C runtime is that it carries lots of rich program structure information in its built binaries. These allow the dynamic aspects of the language to work. In fact, its flexibility of dynamic dispatch is a source for many crashes.
We recommend installing the class-dump tool right away because we shall reference its usage in later chapters. See Nygard The class dump tool allows us to look at what Objective C classes, methods and properties are present in a given program. The Apple Crash Reporter tool and supporting infrastructure in iTunes Connect is excellent but has some room for improvement.
The idea is to make our app handle all the possible signals and exceptions that can occur that would otherwise be unimplemented by the app and thus lead to the underlying Operating System to handle the crash. With this solution, the crash data can be recorded, and then later communicated to a server of our own choice. There are two benefits. Firstly, the crash handler can be fixed to handle edge cases not already handled by the Apple ReportCrash tool.
Secondly, a more comprehensive server side solution can be employed. For those wanting to explore, and understand, the Operating System, and low-level application code, plcrashreporter provides an excellent opportunity to study a well-engineered piece of system software. When a company has many apps, many app variants, and has apps based on competitor platforms such as Android, a more powerful multi-platform solution is needed.
Handling crash reports soon becomes a management problem. Which crash is the most serious? How many customers are affected? What are the metrics for quality and stability saying? A number of commercial solutions are available, largely based upon the above Open Source project.
The Mobile Software Development field has grown into a big industry over the last few years. Many specialist companies serve App Developers as their customers. The field is very active in terms of mergers and acquisitions. Therefore, we cannot name the competitors in the Crash Reporting space in this book, as the list would be constantly changing.
A good place to start is the rollout. We think of Xcode in layers of sophistication, where at the lowest layer of sophistication Xcode directly tells us the common error it has seen with suggested corrections, up to the highest level were Xcode is telling the raw information, but we need Operating Systems knowledge to interpret the information ourselves. We shall revisit Xcode configuration, setup and tooling many times. Nevertheless, let us first start off with the simple but high value assistance Xcode provides.
If we have a crash that is reproducible from our own developer environment and source code, then a methodology for finding is to switch on the appropriate diagnostic setting and then re-run our application. As we become familiar with each diagnostic, we will know which option to switch on.
We shall work through different scenarios so we understand when to use each. But when we are just starting out its worth just going through each one-by-one to get a feel for what is available. The basic approach is:. Another complementary approach for analyzing and proactively avoiding crashes is to run the Code Analyzer.
This is invoked using Command-Shift-B. This can be switched on for whenever the project is built, either in shallow or deep mode according to how we feel the tradeoff should be done between slower more thorough analysis versus quicker build times with less analysis. It is in the Build Settings tab for the Xcode project file. For a large project that has never had an Analysis report done, the output can be overwhelming. There will be some noise in the report but it generally does a good job.
There will be duplication in the report because certain classes of error will repeat throughout the code. If we are developing code using the Agile software methodology, then it is possible to frame the report as potential backlog items that can be worked upon during the time allocated for refactoring and maintenance. Different viewpoints arise in this area. The author recommends doing such work alongside the normal development activities so long as no high-risk changes are amongst the work being done.
For risky changes, leave that until after a major update of the app has been done. There is usually a lull where planning and strategy is developed following a release, which allows a convenient software engineering window to tackle such matters. Where the analyzer identifies potential crashes, from an economic point of view, it is a good investment to fix the problem. For example in the case of the QuickEdit iOS App, about 1 million lines of Objective-C, with 70 daily active users, the analyzer was run and found 13 clear crashing issues.
All 13 issues were fixed in the same day with testing taking two more days. Crashes are a top complaint from customers. Bugs found in the field typically are 20 times the effort and cost compared to those found in development. QuickEdit due to its age only used manual reference counting in Objective-C. Despite this, it had a reliability of One way to drive out crashes from our app, particularly when we are in a large organization, is to factor it in our software development process.
When a developer proposes a code change in a pull request, get the developer to ensure no new analyzer warnings are introduced. We might consider the analyzer report as a robotically generated code review available to us for free. That is particularly helpful if we are working alone on a project with no peer to review our code. When code is committed to a feature branch, have the automated tests run on it, with different diagnostics settings set.
This can shake out problems automatically. Before each release, schedule time to run some specific user cases under the memory profiler to look at memory usage or other key metrics. Record the highlights such as the peak memory usage as well as the profile file. Then when the following release is made we have a yardstick to see how things have changed both quantitatively and qualitatively. We recommend taking a measured approach.
There is a time for hacking together a sample app to understand a concept. There is a time to write a prototype that just needs to prove a business use case. There is a time to write heavily trusted code used by many people. We take the view that maximizing economic impact is the one that matters most because most developers are involved in professional software development. Alternatively, if we are working on non-commercial projects or hobby projects, the economic cost is really our personal free time which we will want to use most effectively.
We have seen that Xcode offers many automatic facilities for crash dump analysis and crash avoidance. However, these cannot get us all the answers we need. A complementary design oriented viewpoint is needed.
It shows an example of why design insights must also be considered. In the STL map abstraction a Hash Table when we query for an entry that does not exist, the STL will insert a new entry in the table for the key being queried, and then return that entry instead of returning an error or returning a nil. The assert is in place to avoid a division by zero.
The fact that it is triggered is good because we know where to start debugging the problem. Therefore, whatever name we pass in, we should always get a planet in response; never a NULL. The problem is that this API has not been thought deeply about. It has just been put together as a thin wrapper around the underlying abstractions that do the work.
At first glance, it might be that the database failed to load data properly. In fact, the database is missing the entry for Pluto due to it no longer being considered a planet:. The problem indirectly is because database. We see that the problem is not applying the paradigms of each framework and language properly and when we have a mixture of paradigms, those different assumptions get masked by each layer of abstraction.
In STL, we expect a find operation to be done, instead of the indexing operator. This allows the abstraction to flag the absence of the item being found. In Objective-C we expect the lookup API to be a function which returns an index given the lookup name. In addition, the index would be NSNotFound when the operation failed.
In this code example, each layer of abstraction assumes the other side will re-map the edge case into an appropriate form. On the consumer side, we have a helper method:. This is hard to parse if we are mainly an Objective-C programmer.
The lesson here is that crashes can arise from special case handling. Since different languages and frameworks deal with special cases in their own idiomatic manner, it is safer to separate out our code and use a Facade if possible to keep each paradigm cleanly separated. This chapter explains crash dump symbolification. Symbolification is the process of mapping machine addresses into symbolic addresses meaningful to the programmer possessing the source code.
Instead of seeing machine addresses, we want to see function names plus any offset ,. When dealing with real world crashes, a number of different entities are involved. These can be the end user device, the settings allowing the Crash Report to be sent back to Apple, the symbols held by Apple and our local development environment setup to mirror such a configuration.
In order to understand how things all fit together it is best to start from first principles and do the data conversion tasks ourselves so if we have to diagnose symbolification issues, we have some experience with the technologies at hand. Normally when we develop an app, we are deploying the Debug version of our app onto our device. When we are deploying our app for testers, app review, or app store release, we are deploying the Release version of our app.
By default for Release builds, debug information from the. The debugger can use debugging information when it sees a crash to help us understand where the program has gone wrong. When a user sees our program crash, there is no debugger. Instead, a crash report is generated. This comprises the machine addresses where the problem was seen. Symbolification can convert these addresses into meaningful source code references.
In the default setup, if we run our debug binary on our device, launching it from the app icon itself then if it were to crash we would not have any symbols in the Crash Report. This confuses many people. Lines 0, 1, 2, 5 are the same in both cases because our developer environment will have the symbols for the iOS release under test. It tells us this is line 33 in file PlanetViewController. This is:. From looking at our build log, we can see how the DSYM was generated.
In order to help us get comfortable with crash dump reports, we can demonstrate how the symbolification actually works. In the first crash dump, we want to understand:. If we knew accurately the version of our code at the time of the crash, we can recompile our program, but with the DSYM setting switched on, and then get a DSYM file after the original crash.
It should line up almost exactly. The crash dump program tells us where the program was loaded, in memory, at the time of the problem. That tells us the master base offset from which all other address TEXT locations are relative to. The Crash Reporter tool fundamentally just uses atos to symbolicate the Crash Report, as well as providing other system related information. Symbolification is described further by an Apple Technote in case we want to get into it in more detail.
In the above example we have the source code, and symbols, for the crash dump so can do Symbolification. Sometimes we may have included a third party binary framework in our project for which we do not have the source code. It is good practice for the vendor to supply symbol information for their framework to allow crash dump analysis. When symbol information is not available, it is still possible to make progress by applying some reverse engineering.
When working with third parties there is typically a much larger turnaround time for diagnostics and troubleshooting. We find that well written and specific bug reports can speed up things a lot. The following approach can help provide the kind of specific information needed. As before, we supply 0xe Now we can visit the code that crashed. The address 0xe45bc is actually the address the device would return to after performing the function call in the stack trace.
Nevertheless, it puts us in the right part of the file. This indeed shows the return address for the assert method. This is just a very basic Hopper example. We shall revisit Hopper later to demonstrate its most interesting feature - that of being able to generate pseudocode from assembly code. This lowers the mental load of comprehending crashes. Most developers rarely look at assembly code nowadays so this feature is worth the cost of the software itself!
In a more complex case, imagine we were using an image conversion library that was crashing. There can be many pixel formats for images. An assert might lead us to notice it was the particular format that was asserting. Therefore, we could just try a different pixel format. Another example would be a security library. Security code often gives back generic error codes, not specific fault codes to allow for future code enhancement and avoid leaking internal details a security risk.
A crash dump in a security library might point out exactly the kind of security issue, and help us correct some data structure passed into the library much earlier on. In this chapter, we get into the details of what comprises a Crash Report. Our main focus is the iOS Crash Report. We also cover the macOS Crash Report, which carries a slightly different structure but serves the same purpose.
Note, it is possible for an app to install crash handlers from third parties, either to get enhanced crash reporting diagnostics, or to link application crashes to a web-based service for managing Crash Reports across a potentially large population of users.
In this chapter we assume the app has not done this, and therefore the Apple CrashReport tool comes into play. When a crash occurs the ReportCrash program extracts information from the crashing process from the Operating System. The result is a text file with a.
When symbol information is available, Xcode will symbolicate the Crash Report to show symbolic names instead of machine addresses. This improves the comprehensibility of the report. Apple has produced a detailed document explaining the anatomy of a crash dump. However, if our problems are potentially triggered by an unexplained series of events or a more complex system interaction with hardware or Apple provided system services, then not only do we need to look at our Crash Reports, we need to study the system diagnostics.
When understanding the environment that gave rise to our crash, we may need to install Mobile Device Management Profiles to switch on certain debugging subsystems , or create virtual network interfaces for network sniffing. Apple provides a great web page covering each scenario. On iOS, the basic idea is that we install a profile, which alters our device to produce more logging, and then we reproduce the crash or get the customer to do that.
Then we press a special key sequence on the device for example, both volume buttons and the side button. The system vibrates briefly to indicate it is running a program, sysdiagnose , which extracts many log files. Inside this archive file are many system and subsystem logs, and we can see when crashes occur and the context that gave rise to them. Here we go through each section of an iOS Crash Report and explain the fields. After a crash, apps are often debugged on the Simulator.
The exception code may be different in that case because the Simulator uses different methodology to cause the app to stop under the debugger. The first thing to look at is the version. Typically, if we are a small team or an individual, we will not have the resources to diagnose crashes in older versions of our app, so the first thing might be to get the customer to install the latest version. If we have many crashes then a pattern may emerge. It could be one customer common CrashReporter key seen , or many customers different CrashReporter keys seen.
This may affect how we rank the priority of the crash. The hardware model could be interesting. Is it iPad only devices, or iPhone only, or both? Maybe our code has less testing or unique code paths for a given platform. The hardware model might indicate an older device, which we have not tested on. Whether the app crashed in the Foreground or Background the Role is interesting because most applications are not tested whilst they are in the background.
For example, we might receive a phone call, or have task switched between apps. The first thing to check is the OS Version. Is it a beta version of the operating system? The next thing to check is the difference between the launch time and the time of the crash.
Did the app crash immediately or after a long time? Early start crashes can sometimes be a packaging and deployment problem. We shall visit some techniques to tackle those later on. Is the date a sensible value? Sometimes a device is set back or forwards in time, perhaps to trigger date checks on security certificates or license keys. Make sure the date is a realistic looking one. Normally the baseband version is not interesting. The presence of the baseband means we could be interrupted by a phone call of course there is VOIP calling as well in any case.
What has happened is that the MachOS kernel has raised an Operating System Exception on the problematic process, which terminates the process. The ReportCrash program then retrieves from the OS the details of such an exception. When we have a SIGABRT , we should look for what exceptions and assertions are present in our code from the stack trace of the crashed thread.
For this type of problem, the diagnostics settings within Xcode for the target scheme is relevant. The address sanitizer should be switched on to see if it could spot the error. If Xcode shows a lot of memory is being used by the app, then it might be that memory we were relying upon has been freed by the system. Then at some point during the app, the MemGraph button can be clicked, and then the allocation history of objects explored.
The program may have been running standalone without a debugger so where did the breakpoint come from? Typically, we are running NSException code. This will make the system signal the process with the trace trap signal and this makes any available debugger attach to the process to aid debugging. So in the case where we were running the app under the debugger, even with breakpoints switched off, we would breakpoint in here so we can find out why there was a runtime exception.
In the case of normal app running, there is no debugger so we would just crash the app. This should be a rare condition. It is worthwhile adjusting the optimization level of the code at fault in the Build Settings because higher level optimizations can cause more exotic instructions to be emitted during build time, and hence a bigger chance for a compiler bug.
Alternatively, the problem might be a lower level library that has hand assembly optimizations in it - such as a multimedia library. Handwritten assembly can be the cause of bad instructions. Certain files descriptors on the system are specially protected because they are used by the Operating System. The exception code 0x08fd4dbfade2dead indicates a database related file descriptor was modified in our example it was closed. If we have code talking to file descriptors, we should always check the return value for the close operation in particular.
It is possible to infer the file operation from the first of the exception codes. It is a bit flag, specified as follows:. This is an anomalous section because it is supposed to look at the process ID of the crashed process and then look to see if there are any syslog System Log entries for that process. We have never seen filtered entries in a crash, and only see None found reported.
When our app has detected a problem and has asked the Operating System to terminate the app, we get an Exception Backtrace section of the report. One presumes that the prior section for filtered syslog information was supposed to do that job. When we see an Exception Backtrace in a customer Crash Report, we should ask for the device console log of the crashing device. No such method exists. The purpose of the exception back trace section is to give more detail than that provided by the crashing thread.
The Crash Report continues with a dump of the thread backtraces as follows formatted for ease of demonstration. Most of our focus should be on the crashed thread; it is often thread 0. Take note of the thread name. Note no long duration tasks such as networking may be done on the main thread, com. When looking at stack backtraces, stack frame 0, the top of the stack, comes first, and then calling frames are listed. Therefore, the last thing being done is in frame 0. The frame numbers, as they count upwards takes us backwards in time in terms of program execution order.
The top of stack, or most recently run code, is in frame 0. One reason for writing code with meaningful function names is that the call stack describes what is going on conceptually. Using small single-purpose functions is good practice. It serves the needs of both diagnostics and maintainability. The second column in a back trace is the binary file.
We focus on our own binary mostly because framework code from Apple is generally very reliable. Faults usually occur either directly in our code, or by faults caused by incorrect usage of Apple APIs. Just because the code crashed in Apple provided code does not mean the fault is in Apple code. The third column, the execution position, is slightly tricky. If it is for frame 0, it is the actual position in the code that was running. If it is for any later frame, it is the position in the code we shall resume from once the child functions have returned.
The fourth column is the site at which the code is running for frame 0 , or the site that is making a function call for later frames. For symbolicated crashes, we will see the symbolic form for the address. This will include a positional offset from the start of a function to reach the code calling the child function. If we have only short functions, this offset will be a small value.
It means much less stepping through code, or much less reading assembly code when performing diagnosis. That is another reason for keeping our functions short. If our crash is not symbolicated then we shall just see a memory address value. One thing to look out for is the special hex code, 0xbaddc0dedeadbead which means a non-initialized pointer. The Crash Report has a section enumerating all the binary images loaded by the process that crashed. It is usually a long list. It highlights the fact that there are many supporting frameworks for our apps.
Most frameworks are private frameworks. The first part is where the image has been loaded into memory. The third part is the architecture slice within the binary that was loaded. We generally expect to just see arm64 here ARM bit.
The fourth part is the UUID of the binary. Most of the binaries have a self-explanatory name. The dyld binary is the dynamic loader. It is seen at the bottom of all stack backtraces because it is responsible for commencing the loading of binaries before their execution. The dynamic loader does many tasks in preparing our binary for execution.
If our binary references libraries, it will load them. If there are absent, it will fail to load our app. This is why it is possible to crash even before any code in main. Later on, we shall study how to diagnose such problems. To avoid repetition, we just highlight notable differences from iOS. Here we see familiar information describing the binary at fault.
Whilst iOS is a system that runs the user experience as one user, the macOS system exposes the fact that there are multiple User IDs in the system. Failed wakeup is a common cause of a system crash in contrast to the application crashes we have been discussing. Further information can be obtained using the pmset power management command. We use this as a broad indication only because the numbers seen always rounded to a convenient number. This means that even if we are logged in as the superuser we cannot change system binaries.
Those are protected with the help of firmware. This is similar to iOS. However, we should note that if we are reproducing an iOS crash on the simulator, then the simulator might model the same programming error differently. We can get a different exception on x86 hardware than its ARM counterpart. Consider the following code, setup with legacy manual reference counting MRC instead of automatic reference counting ARC.
The take away message is when iOS ARM crashes are being reproduced on x86 hardware, either via the Simulator or via equivalent macOS code, expect the runtime environment to be different and cause a slightly different looking crash. Fortunately, here it is clear that a semaphore was deallocated whilst it was in use in both Crash Reports. In addition to the iOS equivalent, we get further information about the CPU that was running the thread.
The trap number can be looked up in the Darwin XNU source code if needed. The traps can be searched for. The Error Code is a bit vector, used to describe the mach error code. Here is an example of the first few binaries in a Crash Report, truncated for ease of demonstration:. When a plus sign appears next to the binary it is meant to mean the binary is part of the OS. However, we see examples of the plus sign present in third party binaries and absent in system binaries, so the plus sign is not a reliable indicator last tested on OS X This permits under certain conditions modification of our process.
We need to know if such a thing happened because it can invalidate any design assumption in the code because registers can be modified of the process and thus a crash can be induced. Ordinarily the above snapshot would be seen. This means no process has directly attached to the process to change the state of a register. Such actions would be acceptable for implementations of managed runtimes, or debuggers.
Outside of these scenarios, such actions would be suspicious and need further investigation. Such data would normally make us suspicious of the environment the program ran in, before crashing. Ordinarily only first party Apple provided programs have privilege to perform the above modifications. It is possible to install software that also does this. The example code tfpexample demonstrates this.
The next section of the Crash Report is the virtual memory summary and region type breakdown. If we have a graphics heavy app that renders pages of a document, we might look at how big the CoreUI image data region is, for example. Virtual memory statistics are only meaningful when the app has already been studied in the Xcode Instruments memory profiler because then we can get a feel for the dynamic usage of memory in the app, and thus begin to spot when things look numerically wrong.
Sometimes our app closely interacts with a hardware peripheral, and if that is via a standards based interface such as USB, then a lot of variability is possible. Consider disk drives. Many vendors provide disk drives, and they may be directly powered, or independently powered. Sometimes newer hardware, such as a new type of MacBook Pro comes with its own hardware issues, so crashes unrelated to our app can be seen.
The key to understanding whether the hardware environment comes into play is to see a number of crashes to look for patterns. As application developers, we only see crashes in our app. If we have contact with the user who has provided a crash, we can ask if any other apps are crashing, or if any system stability issues are present. Another interesting aspect is that not all hardware is actively used by the system all the time. For example, when a MacBook Pro is connected to an external display, different graphics RAM is used and a different graphics card is used external versus an internal GPU.
If our app does something special, when connected to an external display, the fault may be in the hardware instead of our code due to it triggering a latent fault in the hardware. Running system diagnostics and looking to see if the problems are appearing against only specific Anonymous UUID Crash Reports are ways to try and understand if we have a machine specific hardware issue.
This chapter discusses a formal technique for solving problems. The idea is to provide a framework that prompts the right questions to be asked. Most problems have a direct and obvious way forward to progress towards their resolution. As engineers, developers, and testers, we are well acquainted with such problem solving.
This chapter does not concern those types of problems. A hammer is best for hammering in nails, and smashing things generally, but not useful for other types of task. A hammer is a solution to a restricted set of problems. Furthermore, our way of thinking about problems is framed by the available tools at our disposal. If we increase the available tools, we can start thinking about problems in different ways, one of which may lead to the answers we desire.
Suppose we had a spanner and a hacksaw in our toolbox. We wanted to remove an old bathroom fitting held in place with rusty bolts. Using the spanner might not work, due to the bolts not turning. However, using a hack saw to remove the bolt heads might be a workable next best solution.
Observing an experienced plumber, or mechanic, reveals such tricks of the trade. If we are a sole developer of an app, perhaps with a few customers, and receive a Crash Report, it can feel like we are being offered a curious intellectual challenge. In a professional software engineering context, the reality is starkly different.
There is typically a team of people involved, we are some levels removed from the customer, and there are many different Crash Reports from different customers, for different products and product variants. We have to prioritize which crash to work on. We can consider three different aspects of the problem: Seriousness, Urgency and Growth. To judge the seriousness of the bug, we need to assess the impact of the bug. What use cases were being done at the time of the problem?
If the customer is in the middle of doing an e-commerce purchase, then clearly revenue is at stake if the problem is not solved. If whilst updating our privacy settings, we see a crash, we have a privacy issue. Depending on the type of market we are operating in, that could be a major problem. One way to assess impact is to build analytics into our app.
Then the set of steps, and more broadly, the customer use case, can be studied alongside the crash. Crashes from the most important use cases can then be identified as high impact bugs to fix. One advantage of third party crash reporting services is that they allow logs to be recorded that are delivered to the Crash Report server along with the crash. To judge the urgency of a bug fix, we need to assess the deadline associated with the bug.
Whenever Apple updates their product line, for example, historically iPhone is updated in September, then a natural product lifecycle cadence is seen in the market. New customers will come to the App Store to provision new apps. There will be a lot of discussion about Apple product features in the press.
Consequently, it becomes a good market window to target. Any crash that prevents app store approval or app first time use issues becomes more important at this time. Occasionally, Apple introduces a new app category, for example watch apps, or sticker packs.
Being available on the first day provides a first-mover advantage, and the possibility of being featured as part of the Apple launch event. The growth in the number of Crash Reports we see can be alarming, and needs to be assessed by analyzing the trend. We can see how many Crash Reports we get over time, and see if there is a spike, or an upward trend. If our app crashes due to features in a new major release of iOS then the first people to experience the problem are early adopters of the beta releases of iOS.
After that, iOS devices will start being automatically upgraded. Sometimes the new version of iOS is released in geographic staggered updates. We would expect to see this reflected in the trend we see amongst our Crash Reports. If we see a spike a sharp rise and then a sharp fall in our Crash Reports, then there may be other factors of components of the system architecture in play. For example, if our app relies on a back-end server that is updated in a problematic way for our app, we could see crashes until the server has been fixed.
The timing of problems can be awkward. For example, when dealing with security credentials such as certificates, it is best to set their expiry date to not being during traditional vacation periods such as Christmas or Chinese New Year because when they expire, there might be few staff available to rectify the problem.
It is bad practice to release a major software update prior to a popular vacation period. If our market opportunity requires the product be released for a vacation period, staffing needs to be setup to accommodate potential problems. Keeping an eye on trends allows us to schedule work to fix problems before they become widespread amongst our customers.
Different apps have different risk profiles. For example, a Mobile Device Management API sensitive app should be tested with Beta versions of iOS because at the systems level, subtle changes can have dramatic impact and need to be picked up early. If we have a graphics sensitive app, then we should keep an eye on new hardware devices, hardware specification updates, and we should have a test suite that exercises the key APIs in the platform we depend upon, so a new OS version, or hardware platform, can be quickly assessed.
The Crash Report trend need not be adverse. If an unusual crash is seen only on older hardware, then we expect the trend to be downwards over time, so it might be possible to de-prioritize such crashes. The information we have for a crash: the Crash Report, customer logs, analytic data etc. This is often a critical first step in triaging a potentially large number of Crash Reports. This gives us a first level approximation of what is at hand and allows managers and other interested parties to get a feel for where we are with product quality, maturity, risks, etc.
First, we state the object of the problem. That is the app, or product, that is failing. Then, we state the defect. The problem should be tracked in a bug management system. Specifying the problem is the most important step in the Analytic Troubleshooting methodology because here we see the gaps in our knowledge, and that prompts the questions that lead us forwards to a resolution. Analytical Troubleshooting works well in a team setting.
By having some domain experts, together with people from other disciplines, and non-technical staff, makes for a good troubleshooting team. Experts sometimes overlook asking the basic questions, and less informed staff could ask good clarifying questions that further shake out implicit assumptions in the problem specification. Hot customer problems can cause anxiety, so having the team come together to troubleshoot can ease tensions and build morale.
Sometimes our customer can be invited to participate; that can often speed up the process and shake out even more assumptions. When troubleshooting as a team, we can just use a whiteboard divided up into a grid as above.
Each person can be given a handout that enumerates the questions to ask for each box within the grid. On the web site associated with this book are support materials and handouts for Analytic Troubleshooting. When troubleshooting on our own, having a print out of the questions and writing up a grid of answers is a good approach. Being away from our computer, and making a list of items to check is good because it can remove the immediate impulse to dive into details.
Instead, once we have our list of items for follow up, we can prioritize our work. We fill out details in the IS column first. Often we notice a big blank area in the grid where we have no data. That is a signal for us to go and collect more data or do research. This allows us to develop a good hypothesis that we can test, or perhaps a number of hypotheses we can prioritize for testing.
Often the first solution we think of only explains part of the pattern of defects seen in the problem specification. Spending a little more time thinking about potential causes, or doing a little more research can be a good investment of time particularly if it is difficult or time-consuming to try out different candidate solutions. We will have an understanding of the system specification and its behavior when operating within those constraints.
In practice, the system evolves over time with new software and hardware releases. Therefore, we must keep going back to primary information sources, and perform experimentation to refine this understanding. This allows us to discover good questions, and allows us to develop a hypothesis.
There is normally a positive feedback loop here between asking questions, learning about our system, and then discovering new relevant questions. The problem specification questions seem unusual at first, and awkwardly phrased. Looking at some practical examples helps explain things more clearly. That will prompt us to ask clarifying questions. If In this example the share button is broken. We could get some sample code that uses the share button and see if it crashes in a similar environment to our problem.
We could test the code in a standalone app, as well as grafted into our Camera App to see if it works there. In this example, we only said system pop ups did not appear. How about console messages? We might find that tell us the reason why the system is crashing our app. Immediately it feels like we can think about good hypotheses consequently.
We notice that only the iMac has a problem, not the printer. If we swap the location of the printer and the iMac, since they are both sensitive electronic products, we could get a good contrast between IS and IS NOT. Electronic equipment can only operate within certain specified environmental conditions. Correct voltage, current, temperature, humidity, limited electromagnetic interference, etc. If we do a site survey with such a requirements specification in mind, we can discover what may be the reason for this location specific issue.
We could also try with and without surge protectors since it is known that power spikes can damage electronic equipment. This is clearly an app start up issue. This example highlights that sometimes questions in one area trigger questions and research in another area. Database connection strings can be setup and re-configured.
It could be that a null connection string, or absent setting, or first time use setup code is not being triggered. Maybe the code for debug builds has a hack to skip first time use workflows to speed up development of features but such features are not present in the release deployment of the app used for App Store review.
This example is harder to understand. We need an understanding of the architecture of the app to ask good questions. Some clues appear. There are 6 types of rendering thread, one of whom is fine. In addition, the main thread is fine.
We need to explore the relevant differences between them. When we have a problem that does not always happen, one strategy is to think about what we could do to make the problem worse, and thus happen more frequently. Then, when we have a candidate solution, we can set a confidence threshold number of iterations without a failure seen and test the fix against this threshold in the special environment which makes the problem more likely.
Perhaps during installation, a server is picked in round-robin fashion amongst four servers that host the back-end for the game. Furthermore, during development, perhaps the server used is a special development server different from production servers used by our customers. Again, the IS NOT section provides the most revealing clues as to where to look for a potential solution.
If we did not do Analytic Troubleshooting, in this example the first instinct would be to check for memory leaks, memory pressure, hardware limitations, etc. That kind of analysis can easily consume a week of engineering effort. This section describes a problem with MacBook Pro computers that were crashing. The above information resembles what we often see in hard problems. The pattern of defects and the actual panic in Bridge OS that runs on the T2 chip make this clear.
The second point is that the volume of failures is low. We see the problem is never during boot up, orderly shutdown, or heavy usage. This is interesting because during hardware validation computers are generally stress tested to shake out problems.
They are not normally left in a sleep state to see if they still perform wake up functions. Therefore, it is possible that there is a testing strategy gap. Replacement hardware still has the same problem for customers. This is a helpful sign because it shows the stability of the defect. Over time, Apple will collect computers known to have the problem, so their faulty batch of computers, to do validation on, improves dramatically. A major gap in the above data set is there are no pmset logs.
A potentially key data point is that a customer, using Safe Mode boot, never saw the problem. It seems that 30 minutes is a key figure in the sleep time. There may be a threshold at 30 minutes, perhaps to go into a deep sleep rather than a quick nap.
One strategy for understanding the problem is to make it occur more frequently. For example, it might be possible to make the computer very quickly go into deep sleep. That may make the problem appear after say 30 seconds, instead of randomly after 30 minutes of sleeping. If the problem can be made more frequent then an automated system test could be written. Then any fix to the Bridge OS would have a robust test suite to validate it.
We do not have the source code of the Bridge OS. It would be interesting to discern the differences between the three crashes seen. For example, sometimes there is a case statement of 20 possible faults, and only one is being entered.
We do not have machine fault register information. When a low level problem occurs, the processor documentation will allow the system architect to look up exactly the kind of failure timeouts, parity errors, etc. The BridgeOS may just be a canary telling us of a problem elsewhere. Some customers have received a full hardware replacement, but still see the problem. It indicates a software problem. It has many good suggestions. When BridgeOS sees a problem, it should be updated to print out the relevant diagnostic registers.
Here is an example of Siri crashing on a Mac. Note that binaries on a Mac are not encrypted.
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