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I would like to know the fastest/best way to learn the business logic in a new project.
Most projects have been running for years, some of them are poorly documented, but you still need to know how to work with them. What is the best way to do this? (Use Case Diagram / support from colleagues / code analysis etc.)
The problem with verbose logging is that you may be overloaded with details that do not help, and even if you have the right details, you may misunderstand the big picture.
Moreover, if projects run for years, and are poorly documented, chances are the little available documentation is already obsolete. And chances are, the team did not invest heavily in logging either.
Reverse engineering the code is another approach, but where to start if there are millions of lines of code in the legacy system? Some things can be easily read in code, but many more complex, emergent behavior comes from the interactions between many classes, and this kind of knowledge is the most difficult to extract.
So here is the way to go:
Talk to colleagues. The best approach to move knowledge from one brain to another is direct conversation. It works much better than any formal diagram or any documentation. Unfortunately this is not always possible (e.g. team left)
If 1 is not possible, understand the business user's point of view. May be there is a user manual? Maybe some colleagues of the user-support? if none of those are possible, the ultimate way is to spend some time in the day of the user's life. You will not understand how the system works, but at least you'll get a quick intro in what the system is supposed to do, what matters to the users, and maybe some business rules.
Check for automated test cases. In fact, such test cases are a hidden and up-to date documentation resource.
Check for non-automated test cases, in particular use acceptance tests, and integration tests. If these are not automated, there are chances that they are already obsolete. But it's better than nothing.
Reverse engineer the code. Identify the main classes and how they interact. And yes, some simplified class diagrams will help you to understand how classes are related (no need to document properties and methods: these can be found back in the code). And some sequence diagram will help you to get a picture of the more complex interactions.
Run server, log verbose everywhere. Follow code flow and dig in.
I want to compare the performance of some web frameworks (Ruby on Rails and ASP MVC3) but I don't know how to get started... Should I measure how fast each framework renders e 10k long loop or how fast its renders 10k lines of html? Are there maybe programs that can help you with this? Also how can the server load be monitored? Any help is appreciated!
Thijs
With respect, this is an unanswerable question. Is a Porche faster than a Prius? Well, no, not when the Porche is in the shop :-).
The answer depends on what you're trying to accomplish, how you do it, and how you code it. For example, Rails goes out of its way to transparently cache as much as it can, and then makes it trivially easy to cache stuff on your command. Of course there's a way to do the same in ASP MVC3, but is it as easy?
Can you find, hire, and train a suitable team in that knows how to use the framework? What's the culture of the organization (Windows or Unix?). I could write a really fast application in MS-Access and the same application poorly in Rails against a high-performance database and the MS-Access app would win. It's far from a given that an application will be written well, optimized, or whatever.
These days, a well-written application is typically performance bound on data I/O, and if this is the case, then it's which database you use that might matter. The loop-test you propose would test almost nothing, unless you're writing an application that calculates pi to the billionth place, or something.
I am sure there are published benchmarks of application frameworks available, but again, they need to make assumptions about what the application actually has to do.
The reality is that any reasonable framework (which includes both of the two you mention) is likely to be as fast as necessary for most scenarios, and again, what you do, and how you architect and implement it are the far more likely culprits for performance problems.
Once you do choose, there's a great (awesome) tool called NewRelic RPM which works with several frameworks -- I use it with Rails, and it gives you internal metrics at a level of detail that is beyond belief.
I don't mean to be glib, or unhelpful. But this is a little bit of a sore spot for me -- in so many cases people say "we should use foo instead of bar because foo's faster", and weeks go by as bar is replaced by foo. And then there are little incompatibilities. And an unexpected bug. And then, well, for some reason the new one is a little slower. And then after it gets optimized, it's finally just as fast.
I'll step down from my soapbox now :-)
First i have no experience on parasoft .test or jtest experience. I have read the datasheet that the product could automatically generate unit test.
but I am woundering how useful the auto generated unit test are. Does it really do not need any other effort by developer?
any experience sharing are welcome.
thanks a lot!
We used JTest for our product recently. We didn't use the standard product, we used the Eclipse Plugin. The standard product is built on the OSGI framework (read: it's like Eclipse), but you have to import and create your projects. We were already using Eclipse, so it made sense for us to simply use the plugin, which has all of the same capabilities.
While there are many things that JTest can do for you, there are also many irritating things about it. For example, Jtest's static analysis tool is what is really worthwhile, IMHO. It can look for lots of errors and has a pretty good reporting system. But, while unit test generation is okay, but I think I spent as much or more time fixing and enhancing the generated tests than I would have just making them myself. Administering Jtest is also somewhat complicated and involved.
The built-in mechanisms to make unit tests, stub objects, parameterized unit tests, etc. are not well documented. At least, my little brain couldn't make good use of them in the two years we used the product. However, a lot of their super awesome features (like GUI tracing, command-line interface, the Bug Detective, reporting system etc.) all require extra, very expensive licenses.
Really, Jtest just gives you an easy way to manage the execution of static and unit testing. But it's really expensive. I can't believe they charge thousands of dollars per license of that stuff. You'll also find that they will want to train you, which you almost need because the documentation is pretty bad. Which is odd, because the user's guide is like 900 pages long.
But here's a big hint: you can do it for free. If I had to do it over, I would have pushed hard for using these products (which, oddly enough, look and feel very similar to Jtest)
http://code.google.com/javadevtools/codepro/doc/index.html
I wouldn't get Jtest thinking that this will be a small something to add to your developer's routine. Jtest can become a huge time and process sink.
Jtest is very very useful.Yes it generates it own test cases which requires lot more efforts for fixing them.I use it in different form.I delete all the generated unnecessary test cases.I made one another file which create database connection and set various other parameters sets.Also after configuration the code will work without mocking if all of the code is ready and if it is not ready than you can stubs the required methods.
Static code analyzer is good(for checking null pointer exception)
Checking code conventions is very good.
Write your custom code guidlines as use cases and execute it on your code.
Code coverage.
Debug while testing.
The auto generated unit tests still needs a developer to decide what results are correct or not, so you have to sit down and do the job. A lot of the boiler plate code is of course auto generated, so a small time saver there. I haven't used it much, but did evaluate jtest for an earlier employer. Seemed like a great product, if I remember correctly. :)
Alas there will never be a silver bullet that addresses all unit testing requirements, but JTest & .Test (& C++Test for that matter) about as close as you will get. Uggwar is correct that the developer will still need to verify outcomes for the basic auto generated tests, however there is a whole lot more to it.
These tools can be used to create basic regression tests, these are there to tell you when something has changes, not whether what it is testing is right or wrong. You can also trace a running application and then generate JUnit/NUnit/CPPUnit tests that recreate what was going on in the application. These tend to be far more useful tests, which are used as regression tests for items of functionality.
Other functionality includes the ability to generate stubs, use spreadsheets as datasources and provide an object repository. There is a while lot more too ....
Give them a try.
http://www.parasoft.com
I read the latest coding horror post, and one of the comments touched a nerve for me:
This is the type of situation that test driven design/refactoring are supposed to fix. If (big if) you have tests for the interfaces, rewriting the implementation is risk-free, because you will know whether you caught everything.
Now in theory I like the idea of test driven development, but all the times I've tried to make it work, it hasn't gone particularly well, I get out of the habit, and next thing I know all the tests that I had originally written not only don't pass, but they're no longer a reflection of the design of the system.
It's all well and good if you've been handed a perfect design from on high, straight from the start (which in my experience never actually happens), but what if halfway through the production of a system you notice that there's a critical flaw in the design? Then it's no longer a simple matter of diving in and fixing "the bug", but you also have to rewrite all the tests. A fundamental assumption was wrong, and now you have to change it. Now test driven development is no longer a handy thing, but it just means that there's twice as much work to do everything.
I've tried to ask this question before, both of peers, and online, but I've never heard a very satisfactory answer. ... Oh wait.. what was the question?
How do you combine test driven development with a design that has to change to reflect a growing understanding of the problem space? How do you make the TDD practice work for you instead of against you?
Update:
I still don't think I fully understand it all, so I can't really make a decision about which answer to accept. Most of my leaps in understanding have happened in the comments sections, not in the answers. Here' s a collection of my favorites so far:
"Anyone who uses terms like "risk-free"
in software development is indeed full
of shit. But don't write off TDD just
because some of its proponents are
hyper-susceptible to hype. I find it
helps me clarify my thinking before
writing a chunk of code, helps me to
reproduce bugs and fix them, and makes
me more confident about refactoring
things when they start to look ugly"
-Kristopher Johnson
"In that case, you rewrite the tests
for just the portions of the interface
that have changed, and consider
yourself lucky to have good test
coverage elsewhere that will tell you
what other objects depend on it."
-rcoder
"In TDD, the reason to write the tests
is to do design. The reason to make
the tests automated is so that you can
reuse them as the design and code
evolve. When a test breaks, it means
you've somehow violated an earlier
design decision. Maybe that's a
decision you want to change, but it's
good to get that feedback as soon as
possible."
-Kristopher Johnson
[about testing interfaces] "A test would insert some elements,
check that the size corresponds to the
number of elements inserted, check
that contains() returns true for them
but not for things that weren't
inserted, checks that remove() works,
etc. All of these tests would be
identical for all implementations, and
of course you would run the same code
for each implementation and not copy
it. So when the interface changes,
you'd only have to adjust the test
code once, not once for each
implementation."
–Michael Borgwardt
One of the practices of TDD is the use of Baby Steps (which could be very boring in the beggining) which is the use of really small steps in order for you to understand your problem space and make a good and satisfactory solution for your problem.
If you already know the design of your application you aren't doing TDD at all. We should design it while doing your tests.
So the suggestion I would give is for you to concentrate on the baby steps in order to get a proper testable design
I don't think any real practitioner of TDD will claim that it completely eliminates the possibility of error or regression.
Remember that TDD is fundamentally about design, not about testing or quality control. Saying "all my tests pass" does not mean "I'm finished."
If your requirements or high-level design change drastically, then you may need to throw away all your tests along with all the code. That's just how things are sometimes. It doesn't mean that TDD isn't helping you.
Properly applied, TDD should actually make your life a lot easier in the face of changing requirements.
In my experience, code that is easy to test is code that is orthogonal from other subsystems, and which has clearly defined interfaces. Given such a starting point, it is much easier to rewrite significant portions of your application, since you can work with confidence knowing that a) your changes will be isolated to a few subsystems, and b) any breakage will quickly show up as failing tests.
If, on the other hand, you're just slapping unit tests on your code after it has been designed, then you may well have problems when requirements change. There's a difference between tests that fail quickly when subsystems change (because they're effectively flagging regressions) and those that are brittle, because they depend on too many unrelated pieces of system state. The former should be fixable by a few lines of code, while the latter may leave you scratching your head for hours trying to unravel them.
The only true answer is it depends.
There are ways to do TDD wrong, such
that it doesn't fit in with your
environment and eats effort with
minimal benefit.
There are ways to do TDD right, such
that it both cuts costs and increases
quality.
There are ways to something
similar-but-different to TDD, which
may or may not get called TDD, and
may or may not be more appropriate in
your particular situation.
It's a strange quirk of the market for software tools and experts that, to maximise the revenue for those pushing them, they are always written as if they somehow apply to 'all software'.
Truth is, 'software' is every bit as diverse as 'hardware', and nobody would think of buying a book on bridge-making to design an electronic gadget or build a garden shed.
I think you have some misconceptions about TDD. For a good explanation and example of what it is and how to use it, I recommend reading Kent Beck's Test-Driven Development: By Example.
Here are a few further comments that may help you understand what TDD is and why some people swear by it:
"How do you combine test driven development with a design that has to change to reflect a growing understanding of the problem space?"
TDD is a technique for exploring a problem space and creating and evolving a design that meets your needs. TDD is not something you do in addition to doing design; it is doing design.
"How do you make the TDD practice work for you instead of against you?"
TDD is not "twice as much work" as not doing TDD. Yes, you'll write a lot of tests, but that doesn't really take much time, and the effort isn't wasted. You have to test your code somehow, right? Running automated tests are a lot quicker than manually testing whenever you change something.
A lot of TDD tutorials present highly detailed tests of every method of every class. In real life, people don't do this. It is silly to write a test for every setter, every getter, and so on. The Beck book does a good job of showing how to use TDD to quickly design and implement something, slowing down to "baby steps" only when things get tricky. See How Deep Are Your Unit Tests for more on this point.
TDD is not about regression testing. TDD is about thinking before you write code. But having regression tests is a nice side benefit. They don't guarantee that code will never break, but they help a lot.
When you make changes that cause tests to break, that's not a bad thing; it's valuable feedback. Designs do change, and your tests aren't written in stone. If your design has changed so much that some tests are no longer valid, then just throw them away. Write the new tests you need to be confident about the new design.
it's no longer a simple matter of
diving in and fixing "the bug", but
you also have to rewrite all the
tests.
A fundamental creed of TDD is to avoid duplication both in the production code AND in the test code. If a single design change means you have to rewrite everything, you weren't doing TDD (or not doing it correctly at all).
Ideally, in a well-designed system with proper separation of concerns, design changes are local, just like implementation changes. While the real world is rarely ideal, you still usually get something in between: you have to change some of the production code and some of the tests, but not everything, and the changes are mostly simple and may even be done automatically by refactoring tools.
Coding something without knowing what will work best in the UI, while at the same time writing unittests. That is very time consuming. It's better to start out making some prototypes of the GUI to get the interaction right.. and then rewrite it with unittests (if you employer allows you).
Continuous Integration (CI) is one key. If your tests run automatically every time you check in to source control (and everyone else sees it if they fail), it's easier to avoid "stale" tests and stay in the green.
As Mr. Dias mentioned, Baby Steps are important. You make a small refactoring, you run your tests. If tests break, you immediately determine if this is expected (design change) or a failed refactoring. When tests are truly independent (comes with practice), this is seldom very difficult. Evolve your design slowly.
See also http://thought-tracker.blogspot.com/2005/11/notes-on-pragmatic-unit-testing.html - and definitely buy the book!
EDIT: Perhaps I'm looking at this the wrong way. Say you had a legacy codebase that you wanted to redesign. The first thing I would try to do is add tests for the current behavior. Refactoring without tests is risky - you might change behavior. After that, I would start to clean up the design, in small steps, running my unit tests after each step. That would give me confidence that my changes weren't breaking anything.
At some point the API might change. This would be a breaking change - clients would have to be updated. The tests would tell me this - which is good, because I'd have to update any existing clients (including the tests).
Now that's not TDD. But the idea is the same - the tests are specifications of behavior (yes, I'm shading into BDD), and they give me the confidence to refactor the implementation while insuring that I preserve the behavior (as well as letting me know when I change the interface).
In practice, I've found TDD gives me immediate feedback on poor interface design. I'm my first client - I know when my API is hard to use.
We tend to do much less design up front with TDD, knowing it can change. I have taken projects through huge gyrations (it's a web app, no it's a RESTful server, no it's a bot). The tests provide me with the ability to refactor and restructure and evolve your code much more easily than untested code. Although it seems contradictory, it is true-- even though you have more code, you are able to make major changes and have confidence that nothing has broken in the existing functionality.
I understand your concern that fundamental assumptions changing make you throw out tests. This seems intuitive, but I personally haven't seen it. Some tests go, but most are still valid-- often a major change isn't as major as it seems at first. Plus, as you get better at writing tests, you tend to write less brittle ones, which helps.
I tend to do a lot of projects on short deadlines and with lots of code that will never be used again, so there's always pressure/temptation to cut corners. One rule I always stick to is encapsulation/loose coupling, so I have lots of small classes rather than one giant God class. But what else should I never compromise on?
Update - thanks for the great response. Lots of people have suggested unit testing, but I don't think that's really appropriate to the kind of UI coding I do. Usability / User acceptance testing seems much important. To reiterate, I'm talking about the BARE MINIMUM of coding standards for impossible deadline projects.
Not OOP, but a practice that helps in both the short and long run is DRY, Don't Repeat Yourself. Don't use copy/paste inheritance.
Not a OOP practice, but common sense ;-).
If you are in a hurry, and have to write a hack. Always add a piece of comment with the reasons. So you can trace it back and make a good solution later.
If you never had the time to come back, you always have the comment so you know, why the solution was chosen at the moment.
Use Source control.
No matter how long it takes to set up (seconds..), it will always make your life easier! (still it's not OOP related).
Naming. Under pressure you'll write horrible code that you won't have time to document or even comment. Naming variables, methods and classes as explicitly as possible takes almost no additional time and will make the mess readable when you must fix it. From an OOP point of view, using nouns for classes and verbs for methods naturally helps encapsulation and modularity.
Unit tests - helps you sleep at night :-)
This is rather obvious (I hope), but at the very least I always make sure my public interface is as correct as possible. The internals of a class can always be refactored later on.
no public class with mutable public variables (struct-like).
Before you know it, you refer to this public variable all over your code, and the day you decide this field is a computed one and must have some logic in it... the refactoring gets messy.
If that day is before your release date, it gets messier.
Think about the people (may even be your future self) who have to read and understand the code at some point.
Application of the single responsibility principal. Effectively applying this principal generates a lot of positive externalities.
Like everyone else, not as much OOP practices, as much as practices for coding that apply to OOP.
Unit test, unit test, unit test. Defined unit tests have a habit of keeping people on task and not "wandering" aimlessly between objects.
Define and document all hierarchical information (namespaces, packages, folder structures, etc.) prior to writing production code. This helps to flesh out object relations and expose flaws in assumptions related to relationships of objects.
Define and document all applicable interfaces prior to writing production code. If done by a lead or an architect, this practice can additionally help keep more junior-level developers on task.
There are probably countless other "shoulds", but if I had to pick my top three, that would be the list.
Edit in response to comment:
This is precisely why you need to do these things up front. All of these sorts of practices make continued maintenance easier. As you assume more risk in the kickoff of a project, the more likely it is that you will spend more and more time maintaining the code. Granted, there is a larger upfront cost, but building on a solid foundation pays for itself. Is your obstacle lack of time (i.e. having to maintain other applications) or a decision from higher up? I have had to fight both of those fronts to be able to adopt these kinds of practices, and it isn't a pleasant situation to be in.
Of course everything should be Unit tested, well designed, commented, checked into source control and free of bugs. But life is not like that.
My personal ranking is this:
Use source control and actually write commit comments. This way you have a tiny bit of documentation should you ever wonder "what the heck did I think when I wrote this?"
Write clean code or document. Clean well-written code should need little documentation, as it's meaning can be grasped from reading it. Hacks are a lot different. Write why you did it, what you do and what you'd like to do if you had the time/knowledge/motivation/... to do it right
Unit Test. Yes it's down on number three. Not because it's unimportant but because it's useless if you don't have the other two at least halfway complete. Writing Unit tests is another level of documentation what you code should be doing (among others).
Refactor before you add something. This might sound like a typical "but we don't have time for it" point. But as with many of those points it usually saves more time than it costs. At least if you have at least some experience with it.
I'm aware that much of this has already been mentioned, but since it's a rather subjective matter, I wanted to add my ranking.
[insert boilerplate not-OOP specific caveat here]
Separation of concerns, unit tests, and that feeling that if something is too complex it's probably not conceptualised quite right yet.
UML sketching: this has clarified and saved any amount of wasted effort so many times. Pictures are great aren't they? :)
Really thinking about is-a's and has-a's. Getting this right first time is so important.
No matter how fast a company wants it, I pretty much always try to write code to the best of my ability.
I don't find it takes any longer and usually saves a lot of time, even in the short-term.
I've can't remember ever writing code and never looking at it again, I always make a few passes over it to test and debug it, and even in those few passes practices like refactoring to keep my code DRY, documentation (to some degree), separation of concerns and cohesion all seem to save time.
This includes crating many more small classes than most people (One concern per class, please) and often extracting initialization data into external files (or arrays) and writing little parsers for that data... Sometimes even writing little GUIs instead of editing data by hand.
Coding itself is pretty quick and easy, debugging crap someone wrote when they were "Under pressure" is what takes all the time!
At almost a year into my current project I finally set up an automated build that pushes any new commits to the test server, and man, I wish I had done that on day one. The biggest mistake I made early-on was going dark. With every feature, enhancement, bug-fix etc, I had a bad case of the "just one mores" before I would let anyone see the product, and it literally spiraled into a six month cycle. If every reasonable change had been automatically pushed out it would have been harder for me to hide, and I would have been more on-track with regard to the stakeholders' involvement.
Go back to code you wrote a few days/weeks ago and spend 20 minutes reviewing your own code. With the passage of time, you will be able to determine whether your "off-the-cuff" code is organized well enough for future maintenance efforts. While you're in there, look for refactoring and renaming opportunities.
I sometimes find that the name I chose for a function at the outset doesn't perfectly fit the function in its final form. With refactoring tools, you can easily change the name early before it goes into widespread use.
Just like everybody else has suggested these recommendations aren't specific to OOP:
Ensure that you comment your code and use sensibly named variables. If you ever have to look back upon the quick and dirty code you've written, you should be able to understand it easily. A general rule that I follow is; if you deleted all of the code and only had the comments left, you should still be able to understand the program flow.
Hacks usually tend to be convoluted and un-intuitive, so some good commenting is essential.
I'd also recommend that if you usually have to work to tight deadlines, get yourself a code library built up based upon your most common tasks. This will allow you to "join the dots" rather than reinvent the wheel each time you have a project.
Regards,
Docta
An actual OOP practice I always make time for is the Single Responsibility Principle, because it becomes so much harder to properly refactor the code later on when the project is "live".
By sticking to this principle I find that the code I write is easily re-used, replaced or rewritten if it fails to match the functional or non-functional requirements. When you end up with classes that have multiple responsibilities, some of them may fulfill the requirements, some may not, and the whole may be entirely unclear.
These kinds of classes are stressful to maintain because you are never sure what your "fix" will break.
For this special case (short deadlines and with lots of code that will never be used again) I suggest you to pay attention to embedding some script engine into your OOP code.
Learn to "refactor as-you-go". Mainly from an "extract method" standpoint. When you start to write a block of sequential code, take a few seconds to decide if this block could stand-alone as a reusable method and, if so, make that method immediately. I recommend it even for throw-away projects (especially if you can go back later and compile such methods into your personal toolbox API). It doesn't take long before you do it almost without thinking.
Hopefully you do this already and I'm preaching to the choir.