Oleg Andreev
Month
The Objective C syntax is poisoned with nested square brackets:
[[[Class alloc] initWithApple:a andOrange:o] autorelease];
First, lets move opening bracket behind the name of the receiver:
Class[alloc][initWithApple:a andOrange:o][autorelease];
You may agree that this is much easier to write now. However, at this point we lose compatibility with ANSI C (think buffer[index]).
Lets omit brackets for messages without arguments and use a space as a delimiter:
Class alloc [initWithApple:a andOrange:o] autorelease;
At this point we may get back compatibility with ANSI C by making a non-context free grammar (parser should recognize that a[b:c] could not be used for index operations).
You can implement exactly that syntax in Io using the standard language features.
Stylesheet and javascript URLs and content should be controlled by application code. Putting static files into public folder is so nineties.
Before starting a work on a distinct feature, you create a branch:
$ git checkout -b myfeature
You write code, create fast commits, merge in master, rewrite code etc.
$ git checkout master$ git merge myfeature --squash
Now you have merged all the changes into the working tree, but not committed in the master branch (because of --squash option)
You may git add some files to produce nice commits as described in the previous article.
These rules are designed for an easy code review using “git log -p”. This command shows the history of commits with patches.
1. Commit message should include task reference number (# of ticket/case in bug tracker, url of wiki etc.). If there’s no reference number, then the ticket must be really trivial or include refactoring only.
2. Commit represents an atomic working patch. No “WIP” commits with undefined behavior are allowed. In your private branches you can do whatever you want, but when merging to master, you must aggregate commits in a set of working patches. If you don’t do that, the single feature would be spread among 30 commits with arbitrary code being written and erased between the start and the end.
3. Commit should be small. You should split a big commit in a few independent ones. More safe commits should be stored first. Good example: you had fixed some performance issue. First, commit a benchmark which shows the previous performance, then commit an updated code. This helps to test the previous code using newer benchmark without manipulating code by hand.
Rule 2 tells you not to pollute master branch with tons of WIP commits and rule 3 tells you to squash WIP commits wisely: do not put everything in a huge patch.
It is much easier to follow these rules when you look what others do with the code using git log each time you pull updates.
“There are two basic type of method: ones that return an object other than self, and ones that cause an effect. […]
As a general philosophy, it’s better to try and make your methods one type or the other. Try to avoid methods that both do something and return a meaningful object. Sometimes it will be unavoidable, but less often than you might expect.”
“But Apple require that this app be paid, not free, in order for us to offer In App Purchase. So lets look at that again, the same user downloads the app for $0.99 assuming it’s a one time payment, then launches the app to find that he only gets 30 days of service for the $0.99 he just paid. Furious he leave one star reviews all over the place even though we went to great lengths in the iTunes description to spell out the exact nature of the subscription and costs (but no one actually ever reads that stuff).”
While searching for “tell, don’t ask” I have got an interesting wikipedia article.
Is there a CS book teaching us how to write big complex programs?
- components identified by URI (“RESTful partials”)
- precise invalidation on data update (no timeout-based silliness)
- easy to extend, test and debug
The biggest advantage of dynamic languages is interactivity. With dynamic language you can open any part of the running system, change something and see how it behaves under these particular conditions, immediately. This dramatically improves design cycles, completely eliminates compile lags and helps to debug efficiently.
Smalltalk/Self guys got it more than 30 years ago.
It is pity to see how current Ruby/Python/JavaScript/etc. frameworks are less interactive than C++/Java within some modern IDE (like Visual Studio).
If the dynamic VM is a move forward, then next step are highly interactive tools. Everything else is just the same old story.
See also real life benefits of dynamic languages at stackoverflow.
Based on hash table vs. message-receivers and activatable slot, not value.
1. Every slot is activated on direct access. Non-activatable slot access raises exception.
2. x := y creates getter method(getSlot(“_x”)) and setter method(v, setSlot(“_x”, v); v).
3. x = y is parsed as x=(y) (i.e. message x= with argument y).
4. No ::=operator.
5. Method definition: obj setSlot(“add”, method(x, self + x))
5.1. Method definition macro: obj def add(x, self + x) (could be implemented in Io itself)
Pros:
- cleaner setters and hooks for setters;
- smaller syntax;
- uniform message dispatch: each message is processed by a method;
- safety: no need to use getSlot(“x”) for method arguments when activatable value could be passed (relevant to any abstract algorithms).
Cons:
- performance hit since local variable access should perform double hash table lookup; this could be optimized by storing hidden variables (_x) in a plain array.
What do you think?
1. Use divs with float:left/position:absolute and negative margins ONLY for the global page layout.
2. For inner modular things like “thumbnail with centered image and centered caption” NEVER EVER use layout tricks mentioned above. Always make sure the module does not require specific outer tags and styles. This is generally possible using tables.
Reasoning: when the smart object is inserted into unprepared environment (div or table) it is nearly impossible to put it into correct position since it has lost its height which should stretch outer container.
This is an important addition to the previous article.
Yesterday I have stated that every incremental development process suffers from increasing module coupling by definition. Smaller steps give you flexibility to turn around a current point in the development process, but not to jump out of it.
In previous article I have completely missed the first statement and started talking about “refactoring 2.0”. In fact, when you have reached first N lines of code in your project you should start a new feature from scratch (literally: create new folder, git init, etc.) This action could be considered as a small jump out of the current environment towards the latest requirements.
When you start building something side by side with the existing environment, you are forced to define some minimal API for the existing code to communicate with the new feature. This could be object-oriented API, config file or network protocol. Maybe you would need to refactor existing code in order to provide such API. In result you would produce two less coupled modules which will give you more flexibility as project gets bigger.
An observation: smaller module is easier to fit into a reasonable API. Complexity grows exponentially in respect to code size.
2. Immutable state is something I don’t care for, so it worries me that referring to map, filter, etc as “functional programming” may give people the impression that they have to swallow this immutable state business in order to use these things.
The Danger of Equating Map and Filter with Functional Programming
Inspired by You Can’t Get There From Here c2.com article
Every incremental development process suffers from increasing module coupling by definition. Smaller steps give you flexibility to turn around a current point in a development process, but not to jump out of it. With incremental process you are reaching local optimum: the best solution for the problem you are not solving today. But this is not the real issue (at least, you can sell it to someone else). The issue is that you can’t move incrementally from the local optimum due to high coupling. The only way out is to take independent components which are suitable for the new task, jump out of the current point and set up new process based on these components. Efficiency of this jump is measured in total relevance of all these components.
In other words, we need some insurance that some critical amount of investment (1 month, $100K etc.) is not thrown away as a whole thing. To achieve this we should keep the work splitted into small distinct pieces, each of the acceptably low cost.
It is usually recommended to refactor the code in order to extract abstract entities and generalize their API. However, it looks like a stupid game in the same playground: a single project directory tree with 1000 files in it.
Lets take a look at search tree balancing principle: each node should have some optimal number of children. If it has too many children, we have to evaluate linear search in the node. If it has too few, we have to evaluate linear search through the linked list instead of a tree.
Our asset is the code. The efficient evaluation of the code requires to keep it in a good shape. This could mean the following:
- N lines of code per method
- M public methods per class/module (+ M private)
- F modules/files per folder
- L levels of folders per library/dependency
- D libraries/dependency per product/another library.
Each figure is average. You can have 10*N lines method as long as there are ten N/10 line methods. The ultimate goal is to have maximum L*F*M*N lines of code per program (as well as M*N lines per class).
Figures could be something like that: N=7, M=7, F=17, L=3, D=7.
The idea is to limit the amount of code you work with. If you do so you would be pushed to extract least coupled parts out of the project, therefore making them more valuable individually and giving more focus to the essentials.
This implies slightly different mindset comparing to traditional refactoring. You do not look for a way to restructure the program just for making it cleaner: you look for a way to keep as little code as possible by extracting least relevant code into separate external modules.
“Another example of the inefficiency of large organizations. Individuals have little to gain in successfull projects (the company won’t make them rich), but much to lose in unsuccessful ones (they could loose their job). So the rational decision is to avoid risk, as it is not balanced by return.”
See also Python paradox