Related
A simple search for DoEvents brings up lots of results that lead, basically, to:
DoEvents is evil. Don't use it. Use threading instead.
The reasons generally cited are:
Re-entrancy issues
Poor performance
Usability issues (e.g. drag/drop over a disabled window)
But some notable Win32 functions such as TrackPopupMenu and DoDragDrop perform their own message processing to keep the UI responsive, just like DoEvents does.
And yet, none of these seem to come across these issues (performance, re-entrancy, etc.).
How do they do it? How do they avoid the problems cited with DoEvents? (Or do they?)
DoEvents() is dangerous. But I bet you do lots of dangerous things every day. Just yesterday I set off a few explosive devices (future readers: note the original post date relative to a certain American holiday). With care, we can sometimes account for the dangers. Of course, that means knowing and understanding what the dangers are:
Re-entry issues. There are actually two dangers here:
Part of the problem here has to do with the call stack. If you call .DoEvents() in a loop that itself handles messages that use DoEvents(), and so on, you're getting a pretty deep call stack. It's easy to over-use DoEvents() and accidentally fill up your call stack, resulting in a StackOverflow exception. If you're only using .DoEvents() in one or two places, you're probably okay. If it's the first tool you reach for whenever you have a long-running process, you can easily find yourself in trouble here. Even one use in the wrong place can make it possible for a user to force a stackoverflow exception (sometimes just by holding down the enter key), and that can be a security issue.
It is sometimes possible to find your same method on the call stack twice. If you didn't build the method with this in mind (hint: you probably didn't) then bad things can happen. If everything passed in to the method is a value type, and there is no dependance on things outside of the method, you might be fine. But otherwise, you need to think carefully about what happens if your entire method were to run again before control is returned to you at the point where .DoEvents() is called. What parameters or resources outside of your method might be modified that you did not expect? Does your method change any objects, where both instances on the stack might be acting on the same object?
Performance Issues. DoEvents() can give the illusion of multi-threading, but it's not real mutlithreading. This has at least three real dangers:
When you call DoEvents(), you are giving control on your existing thread back to the message pump. The message pump might in turn give control to something else, and that something else might take a while. The result is that your original operation could take much longer to finish than if it were in a thread by itself that never yields control, definitely longer than it needs.
Duplication of work. Since it's possible to find yourself running the same method twice, and we already know this method is expensive/long-running (or you wouldn't need DoEvents() in the first place), even if you accounted for all the external dependencies mentioned above so there are no adverse side effects, you may still end up duplicating a lot of work.
The other issue is the extreme version of the first: a potential to deadlock. If something else in your program depends on your process finishing, and will block until it does, and that thing is called by the message pump from DoEvents(), your app will get stuck and become unresponsive. This may sound far-fetched, but in practice it's surprisingly easy to do accidentally, and the crashes are very hard to find and debug later. This is at the root of some of the hung app situations you may have experienced on your own computer.
Usability Issues. These are side-effects that result from not properly accounting for the other dangers. There's nothing new here, as long as you looked in other places appropriately.
If you can be sure you accounted for all these things, then go ahead. But really, if DoEvents() is the first place you look to solve UI responsiveness/updating issues, you're probably not accounting for all of those issues correctly. If it's not the first place you look, there are enough other options that I would question how you made it to considering DoEvents() at all. Today, DoEvents() exists mainly for compatibility with older code that came into being before other credible options where available, and as a crutch for newer programmers who haven't yet gained enough experience for exposure to the other options.
The reality is that most of the time, at least in the .Net world, a BackgroundWorker component is nearly as easy, at least once you've done it once or twice, and it will do the job in a safe way. More recently, the async/await pattern or the use of a Task can be much more effective and safe, without needing to delve into full-blown multi-threaded code on your own.
Back in 16-bit Windows days, when every task shared a single thread, the only way to keep a program responsive within a tight loop was DoEvents. It is this non-modal usage that is discouraged in favor of threads. Here's a typical example:
' Process image
For y = 1 To height
For x = 1 to width
ProcessPixel x, y
End For
DoEvents ' <-- DON'T DO THIS -- just put the whole loop in another thread
End For
For modal things (like tracking a popup), it is likely to still be OK.
I may be wrong, but it seems to me that DoDragDrop and TrackPopupMenu are rather special cases, in that they take over the UI, so don't have the reentrancy problem (which I think is the main reason people describe DoEvents as "Evil").
Personally I don't think it's helpful to dismiss a feature as "Evil" - rather explain the pitfalls so that people can decide for themselves. In the case of DoEvents there are rare cases where it's still reasonable to use it, for example while a modal progress dialog is displayed, where the user can't interact with the rest of the UI so there is no re-entrancy issue.
Of course, if by "Evil" you mean "something you shouldn't use without fully understanding the pitfalls", then I agree that DoEvents is evil.
What do you do when you're assigned to work on code that's
atrocious and antiquated to the point where it's almost incomprehensible?
For example: hardware interface code, mixed with logic, AND user interface code, ALL in the same functions?
We see bad code all the time, but what do you actually do about it?
Do you try to refactor it?
Try to make it OO if it's not?
Or do you try to make some sense of it, make the necessary changes and move on?
Depends on a few factors for me:
Will I be maintaining this code in the future, or is it a one-off fix?
How long until this system is replaced entirely?
How busy am I at the moment?
Ideally, I'd refactor all bad code I had to maintain, but the reality is there are only so many hours in the day.
As is frequently the case, "It Depends".
I tend to ask myself some of the following questions:
Are there unit tests for the existing code?
Is refactoring the code an acceptable risk for my project?
Is the author still available to clarify any questions I might have about the code?
Will my employer consider the time spent on changing existing, functioning code to be an acceptable use of my time?
And so on...
But assuming that I have the capacity to do so, refactoring is preferential as the up front cost of fixing the code now will likely save me a lot of time and effort later in maintenance and development time.
There are other benefits as well, including the fact that the more clean and well maintained you keep your code base, the more likely other developers are to keep it that way. The Pragmatic Programmer calls this the Broken Window Theory.
Developers have an instinct to assume that code is always ugly because of other, inferior developers. Sometimes, code is ugly because the problem space is ugly. All that ugliness isn't just ugliness - it is sometimes institutional memory. Each line of ugly in your code probably represents a bug fix. So think very carefully before you rip it all out.
Basically, I would say that you shouldn't touch code like this unless you actually have to. If there's a real bug that you can solve, refactoring is reasonable, if you can be sure you're maintaining the same amount of functionality. But refactoring for the sake of refactoring (eg, "make the code OO") is what I would generally classify as a classic newbie mistake.
The book Working Effectively with Legacy Code discusses the options you can do. In general the rule is not to change code until you have to (to fix a bug or add a feature). The book describes how to make changes when you can't add testing and how to add testing to complex code (which allows more substantial changes).
You try to refactor it, in the strict sense on the word, where you're not changing the behaviour.
The first target is usually to break up giant methods.
Given the strength of some of the adjectives you use, i.e. atrocious, antiquated and incomprehensible, I'd bin it!
If it is in such a state, like the example you give, it's probably not got any test code for it either. Refactoring is mentioned in many of the other answers but, sometimes, it is not appropriate. I always find that, when refactoring, you generally need a clear path through which the old code can be gradually morphed into the new in a number of well defined steps.
When the old code is so far removed from how you want it to look, such as the extreme cases you seem to be suggesting, you could probably redesign, rewrite and test the new code in a shorter time than it would to take to refactor it.
Scrap it and start over, using the compiled legacy application as a business requirements document.
And spending time in analysis with the users to see what they want changed.
Post it to www.worsethanfailure.com!!!
If no modifications are needed, I don't touch it.
If at all possible, I write automated unit tests first, especially focused on the areas that need modification.
If automated unit tests are not possible, I do what I can to document manual unit tests.
I am just using the tests to document "current" behavior at this point.
If possible, I always keep a version of the code and executable environment that runs things the "original" way (before I touched it) so I can always add new "behavior documentation" tests and better detect regressions I may have caused later.
Once I start changing things, I want to be very careful not to introduce regressions. I do this by continually rerunning (and or adding new tests) to the tests I wrote before I started writing code.
When possible, I leave bugs as-is if there is no business need for them to be fixed. Those bugs may be "features" to some users and may have unclear side effects that wouldn't be clear until the code was redeployed to production.
As far as refactoring, I do that as aggressively as possible, but only in the code that I need to change otherwise anyway. I may refactor more aggressively in my own personal copy of the code that will never be checked in, just to improve the readability of the code for me personally. It's often times difficult to properly test changes that are only made for readability reasons, so for safety reasons, I generally don't check those changes in / deploy them unless I can confidently test that the code changes are completely safe (it's really bad to introduce bugs when you are making changes that are unnecessary for anything but readability).
Really, it's a risk management problem. Proceed with caution. The users do not care if the code is atrocious, they just care that it gets better without getting worse. Your need for beautiful code is not important in this scenario, get past it.
Just like any other code, you leave it slightly better when you leave it than it was when you entered it. You do not ever, ever rewrite the whole code. If that is the work it takes for some reason, then you start a project (small or large) for it.
I am assuming we are talking about a substantial amount of code here.
Not every day is a great day at work you know :)
The first question to ask is: does it work?
If the answer is yes, that would be a huge disincentive to simply ditch it and start over. There may be thousands of man-hours in that code which address edge cases and nasty bugs. Worse yet, there may be other modules in the system that depend on the current incorrect (but known and possibly documented) behavior. Don't mess with it if it isn't broken.
If you are keen on cleaning it up, start by writing test cases for the current behavior. When you run across an instance where the behavior differs from the specification, you must decide whether to accept the behavior as "correct" or go with what the spec say it ought to do.
Only once you have written test cases that all pass should you begin to refactor. The tests will tell you whether your efforts are breaking anything.
I'd talk to my manager and describe the code. Most managers would not want a program held together by banding wire and duct tape per se. If the code is really that bad there are sure to be some business logic errors, hardcoding etc. stuffed in there that will eventually just destroy productivity.
I've come across some pretty bad code before (single letter variable names, no comments, everything crammed onto one line, etc.) and once I mentioned/showed it to my manager they almost always said "go ahead and re-write it", because not only are you taking the hit for reading and changing the code but future co-workers will have to go through the same pain. Better that you take a longer period of time just once to rewrite it rather than having each person who touches the code in the future have to go through and comprehend and decipher it first.
There is an old saying. If is isn't broke, do not fix it. If you have to maintain it then reverse engineer it and document it so the next time you come across it you will know what it does.
You do not know the situation the developer was in when he or she wrote the code. He or she may have been under a time crunch when it was written, (management was all over the developer, etc)
There are also situations where he or she wrote the code per the spec, The spec then changed several times, the developer had to patch the code, as rewrite is out of the question due to time constraints. This happens all of the time.
If the code impacts the performance of robustness of the application and is modular then you can re factor or re-write. Document the situation to assist future programmers in understanding.
Also many programmers consider reverse engineering other developers code as beneath them.
they would rather rewrite without considering the ramifications of doing so.
If you have never done so, try it sometime, it will make you a better developer.
Thanks
Joe
Kill it with fire.
Depends on your time frame and how important that code is to you. If you have to "just make it work" then do that and rewrite the module when time allows.
If its an important or integral part of what you do then refactor refactor refactor.
Then find the guy/girl who wrote it and send them a rude postcard!
The worst offender (in my experience) of really AWFUL code is the ease with which people can do cut & paste these days. Cut & paste should be used rarely. If you think that's the right solution, it's generally better to step back and generalize the problem a little.
Anytime you see code that is "nearly incomprehensible", PROCEED WITH CAUTION. You need to assume that any major re-factoring will result in new bugs being introduced that you'll need to find and correct.
Additionally, I've seen this scenario many times (even fell victim to it myself once or twice): Programmer inherits legacy code, decides code is ancient & unmaintainable and decides to refactor it, ends up deleting key "fixes" or "business rules" subtly patched in over the years, ends up spending a lot of time tracking down and re-introducing similar code when users complain about "a problem fixed years ago is happening again".
Re-factoring (and debugging) almost always takes longer than expected and should never be considered as a "freebie" that comes along with whatever task you're supposed to be doing.
"If it ain't broke, don't 'fix' it" still has a lot of truth.
Im my company we always Refactor Mercilessly. so we still come across atrocious code but LESS and Less and less ...
We write a lot of in-house code and the company is run for about 100 years by the same family. Management usually tells us we have to maintain the code base (evolve) for another 50 years or so. In this setting having code you don't dare to touch is considered a bigger risk to the long term survival of the company then the prospect of downtime because some under-tested code broke because of refactoring.
I run copy-paste detector and findbugs on all legacy code that comes my way.
I then plan my initial refactoring:
remove unused code, unused variable and unused methods
refactor duplicated code
set up a single step build
build a basic functional test
By that point the code meets the basic minimum for maintainability. It can be easily built and basic errors can be found via an automated test.
I often add code like this:
log.debug("is foo null? " + (foo == null));
log.debug("is discount < raw price ? " + (foo.getDiscount() < foo.getRawPrice()));
Some of that code will be recovered for unit tests when I can refactor to it.
I've worked places where we ship that kind of code.
I try to make sense of it, make the necessary changes, and move on.
Of course, making sense of it usually involves some changes; at the very least, I move around the whitespace and line up corresponding braces in the same column like so:
if(condition){
doSomething(); }
// becomes...
if(condition)
{
doSomething();
}
I'll also often change variable names.
And very often, "the necessary changes" require refactoring. :)
Get the idea of what they're doing and the deadline to finish. A larger deadline, typically rebuild much of the code from the ground up, as I find it a very worthwhile experience to not only decipher terrible code and make it legible and document, but somewhere in your brain those neurons are pressed to avoid similar mistakes in the future.
I was having a discussion with one of my colleagues about how defensive your code should be. I am all pro defensive programming but you have to know where to stop. We are working on a project that will be maintained by others, but this doesn't mean we have to check for ALL the crazy things a developer could do. Of course, you could do that but this will add a very big overhead to your code.
How do you know where to draw the line?
Anything a user enters directly or indirectly, you should always sanity-check. Beyond that, a few asserts here and there won't hurt, but you can't really do much about crazy programmers editing and breaking your code, anyway!-)
I tend to change the amount of defense I put in my code based on the language. Today I'm primarily working in C++ so my thoughts are drifting in that direction.
When working in C++ there cannot be enough defensive programming. I treat my code as if I'm guarding nuclear secrets and every other programmer is out to get them. Asserts, throws, compiler time error template hacks, argument validation, eliminating pointers, in depth code reviews and general paranoia are all fair game. C++ is an evil wonderful language that I both love and severely mistrust.
I'm not a fan of the term "defensive programming". To me it suggests code like this:
void MakePayment( Account * a, const Payment * p ) {
if ( a == 0 || p == 0 ) {
return;
}
// payment logic here
}
This is wrong, wrong, wrong, but I must have seen it hundreds of times. The function should never have been called with null pointers in the first place, and it is utterly wrong to quietly accept them.
The correct approach here is debatable, but a minimal solution is to fail noisily, either by using an assert or by throwing an exception.
Edit: I disagree with some other answers and comments here - I do not think that all functions should check their parameters (for many functions this is simply impossible). Instead, I believe that all functions should document the values that are acceptable and state that other values will result in undefined behaviour. This is the approach taken by the most succesful and widely used libraries ever written - the C and C++ standard libraries.
And now let the downvotes begin...
I don't know that there's really any way to answer this. It's just something that you learn from experience. You just need to ask yourself how common a potential problem is likely to be and make a judgement call. Also consider that you don't necessarily have to always code defensively. Sometimes it's acceptable just to note any potential problems in your code's documentation.
Ultimately though, I think this is just something that a person has to follow their intuition on. There's no right or wrong way to do it.
If you're working on public APIs of a component then its worth doing a good amount of parameter validation. This led me to have a habit of doing validation everywhere. Thats a mistake. All that validation code never gets tested and potentially makes the system more complicated than it needs to be.
Now I prefer to validate by unit testing. Validation definitely happens for data coming from external sources, but not for calls from non-external developers.
I always Debug.Assert my assumptions.
My personal ideology: the defensiveness of a program should be proportional to the maximum naivety/ignorance of the potential user base.
Being defensive against developers consuming your API code is not that different from being defensive against regular users.
Check the parameters to make sure they are within appropriate bounds and of expected types
Verify that the number of API calls which could be made are within your Terms of Service. Generally called throttling it usually only applies to web services and password checking functions.
Beyond that there's not much else to do except make sure your app recovers well in the event of a problem and that you always give ample information to the developer so that they understand what's going on.
Defensive programming is only one way of hounouring a contract in a design-by-contract manner of coding.
The other two are
total programming and
nominal programming.
Of course you shouldnt defend yourself against every crazy thing a developer could do, but then you should state in wich context it will do what is expected to using preconditions.
//precondition : par is so and so and so
function doSth(par)
{
debug.assert(par is so and so and so )
//dostuf with par
return result
}
I think you have to bring in the question of whether you're creating tests as well. You should be defensive in your coding, but as pointed out by JaredPar -- I also believe it depends on the language you're using. If it's unmanaged code, then you should be extremely defensive. If it's managed, I believe you have a little bit of wiggleroom.
If you have tests, and some other developer tries to decimate your code, the tests will fail. But then again, it depends on test coverage on your code (if there is any).
I try to write code that is more than defensive, but down right hostile. If something goes wrong and I can fix it, I will. if not, throw or pass on the exception and make it someone elses problem. Anything that interacts with a physical device - file system, database connection, network connection should be considered unereliable and prone to failure. anticipating these failures and trapping them is critical
Once you have this mindset, the key is to be consistent in your approach. do you expect to hand back status codes to comminicate problems in the call chain or do you like exceptions. mixed models will kill you or at least drive you to drink. heavily. if you are using someone elses api, then isolate these things into mechanisms that trap/report in terms you use. use these wrapping interfaces.
If the discussion here is how to code defensively against future (possibly malevolent or incompetent) maintainers, there is a limit to what you can do. Enforcing contracts through test coverage and liberal use of asserting your assumptions is probably the best you can do, and it should be done in a way that ideally doesn't clutter the code and make the job harder for the future non-evil maintainers of the code. Asserts are easy to read and understand and make it clear what the assumptions of a given piece of code is, so they're usually a great idea.
Coding defensively against user actions is another issue entirely, and the approach that I use is to think that the user is out to get me. Every input is examined as carefully as I can manage, and I make every effort to have my code fail safe - try not to persist any state that isn't rigorously vetted, correct where you can, exit gracefully if you cannot, etc. If you just think about all the bozo things that could be perpetrated on your code by outside agents, it gets you in the right mindset.
Coding defensively against other code, such as your platform or other modules, is exactly the same as users: they're out to get you. The OS is always going to swap out your thread at an inopportune time, networks are always going to go away at the wrong time, and in general, evil abounds around every corner. You don't need to code against every potential problem out there - the cost in maintenance might not be worth the increase in safety - but it sure doesn't hurt to think about it. And it usually doesn't hurt to explicitly comment in the code if there's a scenario you thought of but regard as unimportant for some reason.
Systems should have well designed boundaries where defensive checking happens. There should be a decision about where user input is validated (at what boundary) and where other potential defensive issues require checking (for example, third party integration points, publicly available APIs, rules engine interaction, or different units coded by different teams of programmers). More defensive checking than that violates DRY in many cases, and just adds maintenance cost for very little benifit.
That being said, there are certain points where you cannot be too paranoid. Potential for buffer overflows, data corruption and similar issues should be very rigorously defended against.
I recently had scenario, in which user input data was propagated through remote facade interface, then local facade interface, then some other class, to finally get to the method where it was actually used. I was asking my self a question: When should be the value validated? I added validation code only to the final class, where the value was actually used. Adding other validation code snippets in classes laying on the propagation path would be too defensive programming for me. One exception could be the remote facade, but I skipped it too.
Good question, I've flip flopped between doing sanity checks and not doing them. Its a 50/50
situation, I'd probably take a middle ground where I would only "Bullet Proof" any routines that are:
(a) Called from more than one place in the project
(b) has logic that is LIKELY to change
(c) You can not use default values
(d) the routine can not be 'failed' gracefully
Darknight
Surely some of you have dealt with this one. It tends to happen when programmers get a bit too taken by OO and forget about performance and having a database.
For an example, lets say we have an Email table and they need to be sent by this program. At start-up, it looks for anything that needs to be sent as follows:
Emails = find_every_damn_email_in_the_database();
FOR Email in Emails
IF !Email.IsSent() THEN Email.Send()
This is a good from a do-not-repeat-yourself perspective, but sometimes it's unavoidable and it should be:
Emails = find_unsent_emails();
FOR Email in Emails
Email.Send()
Is there a name of this one?
I'll have a go at it and coin the name "the lazy filter (anti) pattern".
I saw that once. That programmer wasn't around too long.
We called that the "firehose method".
To me it's Joel Spolsky's leaky abstraction.
It's not exactly an anti-pattern, but whoever wrote this code, didn't really understand where Active Record pattern abstraction leaks.
I call that "The Shotgun Approach".
I'm not sure this is necessarily database related, since you could have a complex and expensive procedure (e.g., more than a flag) for applying a filter for a group.
I don't think there's a name to it, since the first design is simply not good, and it violates the one-responsibility-only principle. If you search, filter, and print the filtered you are doing multiple things, so you need to refactor it into "searched filtered" and print.
The only thing different than a simple refactoring here is that it also affects performance, in the same way that inner loops can be designed in ways that harm performance.
Appear to have derived from the following anti-patterns:
Standing On The Shoulders Of Midgets
If It Is Working Dont Change
The original developer would have possibly not been allowed to write the find_unsent_emails() implementation, and would therefore have reused the midget function. And then, why change it after development and testing?
This is frequently due to it being a lot easier to use an existing query and then filtering in code than getting a new SQL query added. Maybe because the DBAs control all queries and getting a new query approved takes days, or maybe because the ORM tool you're using makes it very difficult to define your own custom queries.
If I were to name it I'd call it the "Easy Way Out" (anti)pattern. Whether it's an antipattern or not really depends on the individual situation. If it will always be a fairly small number of items you need to retrieve, doing the filtering in code really isn't a big problem. But if the number of items is large and has the potential to continually grow, then obviously the filtering should be done on the server.
I've seen similar issues elsewhere, where instead of a simple array of things to do, there was a "transaction cluster" based on a "list cluster" based on a "collection cluster" based on a "memory cluster". Needless to say, the simplest thing turned into a great big freakin' deal.
I called it galloping generality.
Stoopid Amateurs.
Seriously, I've only seen this one in people with Computer Science degrees and no professional experience at all. When I was teaching at Duke, my advisor and I ran a "Large Scale Programming" class where we made people look at exactly these sorts of errors.
The performance of the first one can actually be fine, depending on the type of Emails. If it's just an iterator (think of std::vector::begin() in C++) then it's fine and better than storing all unsent e-mails in some container first.
This antipattern has several possible names.
"Don't-know-SQL" antipattern
"Fascist-DBA" antipattern
"What-does-'latency'-mean?" antipattern
There is a nice example at The Daily WTF.
Inspired partly by 1800's "the lazy filter (anti) pattern", how about "dysfunctional programming" (ie the opposite of functional programming)?
I want to know if there are method to quickly find bugs in the program.
It seems that the more you master the architecture of your software, the more quickly
you can locate the bugs.
How the programmers improve their ability to find a bug?
Logging, and unit tests. The more information you have about what happened, the easier it is to reproduce it. The more modular you can make your code, the easier it is to check that it really is misbehaving where you think it is, and then check that your fix solves the problem.
Divide and conquer. Whenever you are debugging, you should be thinking about cutting down the possible locations of the problem. Every time you run the app, you should be trying to eliminate a possible source and zero in on the actual location. This can be done with logging, with a debugger, assertions, etc.
Here's a prophylactic method after you have found a bug: I find it really helpful to take a minute and think about the bug.
What was the bug exactly in essence.
Why did it occur.
Could you have found it earlier, easier.
Anything else you learned from the bug.
I find taking a minute to think about these things will make it far less likely that you will produce the same bug in the future.
I will assume you mean logic bugs. The best way I have found to capture logic bugs is to implement some sort of testing scheme. Check out jUnit as the standard. Pretty much you define a set of accepted outputs of your methods. Every time you compile your system it checks all of your test cases. If you have introduced new logic that breaks your tests, you will know about it instantly and know exactly what you have to fix.
Test driven design is a pretty big movement in programming right now. You will be hard pressed to find a language that doesn't support some kind of testing. Even JavaScript has a multitude of test suites.
Experience makes you a better debugger. Pay close attention to the bugs that you AND others commonly make. Try to figure out if/how these bugs apply to ALL code that affects you, not the single instance of where the bug was seen.
Raymond Chen is famous for his powers of psychic debugging.
Most of what looks like psychic
debugging is really just knowing what
people tend to get wrong.
That means that you don't necessarily have to be intimately familiar with the architecture / system. You just need enough knowledge to understand the types of bugs that apply and are easy to make.
I personally take the approach of thinking about where the bug may be in the code before actually opening up the code and taking a look. When you first start with this approach, it may not actually work very well, especially if you are pretty unfamiliar with the code base. However, over time someone will be able to tell you the behavior they are experiencing and you'll have a good idea where the problem is located or you may even know what to fix in the code to remedy the problem before even looking at the code.
I was on a project for several years that maintained by a vendor. They were not very good debuggers and most of the time it was up to us to point them to an area of the code that had the problem. What made our problem worse was that we didn't have a nice way to view the source code, so a lot of our "debugging" was just feeling.
Error checking and reporting. The #1 newbie coder debugging mistake is to turn off error reporting, avoid checking for whether what's going on makes sense, etc etc. In general, people feel like if they can't see anything going wrong then nothing is going wrong. Which of course could not be further from the case.
Instead, your code should be chock full of error conditions that will make lots of noise, with detailed reporting, someplace you will see it. (This doesn't mean inside a production web page.) Then, instead of having to trace an error all over the place because it got passed through sixteen layers of execution before it finally got someplace that broke, your errors start happening proximately to the actual issue.
It seems that the more you master the
architecture of your software ,the
more quickly you can locate the bugs.
After understanding the architecture, one's ability to find bugs in the application increases with their ability to identify and write extensive tests.
Know your tools.
Make sure that you know how to use conditional breakpoints and watches in your debugger.
Use static analysis tools as well - they can point out the more obvious issues.
Sleep and rest.
Use programming methods that produce fewer bugs in the first place.
If to implement a single stand-alone functional requirement it takes N separate point-edits to source code, the number of bugs put into the code is roughly proportional to N, so find programming methods that minimize N. Ways to do this: DRY (don't repeat yourself), code generation, and DSL (domain-specific-language).
Where bugs are likely, have unit tests.
Obviously.IMHO, the best unit tests are monte-carlo.
Make intermediate results visible.
For example, compilers have intermediate representations, in the form of 4-tuples. If there is a bug, the intermediate code can be examined. That tells if the bug is in the first or second half of the compiler.
P.S. Most programmers are not aware that they have a choice of how much data structure to use. The less data structure you use, the less are the chances for bugs (and performance issues) caused by it.
I find tracepoints to be an invaluable debugging tool. They are a bit like logging, except you create them during a debugging session to solve a particular issue, like breakpoints.
Printing the stacktrace in a tracepoint can be especially useful. For example, you can print the hash code and stacktrace in the constructor of an object, and then later on when the object is used again you can search for its hashcode to see which client code created it. Same for seeing who disposed it or called a certain method etc.
They are also great for debugging issues related to window focus changes etc, where the debugger would interfere if you drop in break mode.
Static code tools like FindBugs
Assertions, assertions, and assertions.
Some areas of our code has 4 or 5 assertions for each line of real code. When we get a bug report the first thing that happens is that the customer data is processed in our debug build 99 times out a hundred an assert will fire near the cause of the bug.
Additionally our debug build perform redundant calculations to ensure that an optimized algorithm is returning the correct result, and also debug functions are used to examine the sanity of data structures.
The hardest thing new developers have to contend with is getting their code to survive the assertions of the code gthey are calling.
Additionally we do not allow any code to be putback to toplevel that causes any integration or unit test to fail.
Stepping through the code, examining flow/state where unexpected behavior is occurring. (Then develop a test for it, of course).
Writing Debug.Write(message) in your code and using DebugView is another option. And then run your application find out what is going on.
"Architecture" in software means something like:
Several components
The components interact across clearly-defined interfaces
Each component has a well-defined responsibility
The responsibility of one component is unlike the responsibilities of other components
So, as you said, the better the architecture the easier it is to find bugs.
First: knowing the bug, you can decide which functionality is broken, and therefore know which component implements that functionality. For example, if the bug is that something isn't being logged properly, therefore this bug should be in one of 3 places:
In the component that's responsible for logging (your logging library)
Or, above that in the application code which is using this library
Or, below that in the system code which this library is using
Second: examine the data transfered across the interfaces between components. To continue the previous example above:
Set a debugger breakpoint on the application code which invokes the logger API, to verify whether the logger API is being used correctly (e.g. whether it's being invoked at all, whether parameters are as-expected, etc.).
Doing this tells you whether the bug is in the component above this interface, or in the component that's below this interface.
Repeat (perhaps using binary search if the call stack is very deep) until you've found which component is at fault.
When you come to the point that you think there must be a bug in the OS, check your assertions -- and put them into the code with "assert" statements.
Conversely, as you are writing the code, think of the range of valid inputs for your algorithms and put in assertions to make sure you have what you think you have. Same goes for output: Check that you produced what you think you produced.
E.g. if you expect a non-empty list:
l = getList(input)
assert l, "List was empty for input: %s" % str(input)
I'm part of the QA team # work, and knowing anything about the product and how it is developed, helps a lot in finding bugs, also when I make new QA tools I pass it to our dev team to test it, finding bugs in your own code is just plain hard!
Some people say programmers are tainted, so we cannot see bugs in their own product; we are not talking about code here, we are beyond that, usability and functionality itself.
Meanwhile unit testing seams to be a nice solution to find bugs in your own code, its totally pointless if you're wrong even before writing the unit test, how are you going to find the bugs then? you don't!, let your co-worker find them, hire a QA guy.
Scientific debugging is what I always used, and it greatly helps.
Basically, if you can replicate a bug, you can track its origin. You should then experiment some tests, observe the results, and infer hypotheses on why the bug happens.
Writing about all your hypotheses, attempts, expected results and observed results can help you track down the bugs, particularly if they're nasty.
There are automated tools that can help you with that process, particularly git-bisect (and similar bisection tools on other revision systems) to quickly find which change introduced the bug, unit testing to reproduce a bug and prevent regressions in your code (can be used in combination with bisect), and delta debugging to find the culprit in your code (similar to git-bisect but whereas git-bisect works on the code history, delta debugging works on the code directly).
But whatever the tools you are using, the most important benefit is in the scientific methodology, as this is the formalization of what most experienced debuggers do.