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# Important Concepts
## Mapping between ILs
ILs in general are critical to how Binary Ninja analyzes binaries and we have much more [in-depth](bnil-overview.md) documentation for BNIL (or Binary Ninja Intermediate Language -- the name given to the family of ILs that Binary Ninja uses). However, one important concept to summarize here is that the translation between each layer of IL is many-to-many. Going from disassembly to LLIL to MLIL can result in more or less instructions at each step. Additionally, at higher levels, data can be copied, moved around, etc. You can see this in action in the UI when you select a line of HLIL and many LLIL or disassembly instructions are highlighted.
APIs that query these mappings are plural. So for example, while `current_hlil.llil` will give a single mapping, `current_hlil.llils` will return a list that may contain multiple mappings.
## REPL versus Scripts
When you're interacting with the Binary Ninja [scripting console](../guide/index.md#script-python-console), it's important to realize that every time you run a command, the UI is automatically going to update analysis. You can see this by even running a simply command like:
```
print("test")
```
and then checking the `Log` pane where you will see an `Analysis update took #.###` message. This is a convenience the UI does for you just like if you were to change a type or create a function, you almost always want the analysis to be updated. However, in scripts you run yourself as plugins or via the "Run Script" command, you may not get the same results mid-execution until after the entire script is finished running.
To achieve the same results when writing a stand-alone plugin, you'd need to use [`current_function.reanalyze()`](https://api.binary.ninja/binaryninja.function-module.html#binaryninja.function.Function.reanalyze), [`bv.update_analysis_and_wait()`](https://api.binary.ninja/binaryninja.binaryview-module.html#binaryninja.binaryview.BinaryView.update_analysis_and_wait), or similar methods.
Another difference in the REPL / scripting console is that the scripting console is not executing on the main thread. This means that if you wish to interact with the UI via QT or BN UI APIs from the console (for example, to [trigger an action via string](https://gist.github.com/psifertex/6fbc7532f536775194edd26290892ef7#file-trigger_actions-py)), you'd need to use [`mainthread.execute_on_main_thread_and_wait()`](https://api.binary.ninja/binaryninja.mainthread-module.html#binaryninja.mainthread.execute_on_main_thread_and_wait) or similar.
## Operating on IL versus Native
Generally speaking, scripts should operate on ILs. The available information far surpasses the native addresses and querying properties and using APIs almost always beats directly manipulating bytes. However, when it comes time to change the binary, there are some operations that can only be done at a simple virtual address. So for example, the [comment](https://api.binary.ninja/binaryninja.binaryview-module.html#binaryninja.binaryview.BinaryView.set_comment_at) or [tag](https://api.binary.ninja/binaryninja.binaryview-module.html#binaryninja.binaryview.BinaryView.add_tag) APIs (among others) work off of native addressing irrespective of IL level.
## Instruction Index vs Expression Index
It is easy to confuse ExpressionIndex and InstructionIndex properties in the API. While they [are both integers](https://github.com/Vector35/binaryninja-api/blob/dev/python/highlevelil.py#L49-L50) they mean different things and it's important to keep them straight. The Instruction Index is a unique index for that given IL level for that given function. However, because BNIL is [tree-based](bnil-overview.md), when there are nested expresses the expression index may be needed. These indexes are also unique per-function and per-IL level, but they are _distinct_ from instruction indexes even though they may occasionally be similar since they each start at 0 for a given function!
## Static Single Assignment Basics
Our [BNIL Overview](bnil-overview.md) mentions Static Single Assignment (SSA) without explaining what it is. You can of course always check out [Wikipedia](https://en.wikipedia.org/wiki/Static_single-assignment_form), but here's a quick summary of what it is, why we expose multiple SSA forms of our ILs and how you can use it to improve your plugin writing with BNIL.
At it's simplest, SSA forms of an Intermediate Language or Intermediate Representation are those in which variables are read only. They can be set when created, but not modified. Instead of modifying them, when you make a change, a new "version" of the variable is created denoting that the value has changed in some way. While this certainly makes the form less readable to humans, this means it's actually really easy to walk back and see how a variable has been modified by simply looking at the definition site for a given variable or all possible versions before it. Any time the variable is modified, a new version of that variable will be created (you'll see `#` followed by a number indicating that this is a new version).
But what if the code takes multiple paths and could have different values depending on the path that was taken? SSA forms use a `Φ` (pronounced either as "fee" or "fi" like "fly" depending on which mathematician or Greek speaker you ask!) function to solve this ambiguity. All a `Φ` function does is aggregate different versions of a variable when entering a basic block with multiple paths. So a basic block with two incoming edges where `eax` is modified might have something like: `eax#3 = Φ(eax#1, eax#2)` denoting that the new version of the variable could have come from either of those sources.
Binary Ninja uses this capability internally for its own value set analysis and constant dataflow propagation but plugins can also leverage this information to great effect. For example, want to find an uninitialized value? Simply look for an SSA variable being read from with a version of zero that isn't in the list of arguments to the function. Want to implement your own inter-procedural data-flow system? Binary Ninja does not for performance reasons, but in instances where you can prevent the state space explosion problem, you can build on top of the existing SSA forms to implement exactly this. A simple example might look for vulnerable function calls like printf() where the first argument is user-data. While most trivial cases of this type of flaw tend to be found quickly, it's often the case that subtler versions with functions that wrap functions that wrap functions that call a printf with user data are more tedious to identify. However, using an SSA-based script, it's super easy to see that, for example, the first parameter to a `printf` call originated in a calling function as the second parameter, and THAT function was called with input that came directly from some sort of user input. While one or two layers might be easy to check by hand with few cross-references, with a large embedded firmware, there might be hundreds or thousands of potential locations to check out, and a script using SSA can dramatically reduce the number of cases to investigate.
## UI Elements
There are several ways to create UI elements in Binary Ninja. The first is to use the simplified [interaction](https://api.binary.ninja/binaryninja.interaction-module.html) API which lets you make simple UI elements for use in GUI plugins in Binary Ninja. As an added bonus, they all have fallbacks that will work in headless console-based applications as well. Plugins that use these API include the [angr](https://github.com/Vector35/binaryninja-api/blob/dev/python/examples/angr_plugin.py) and [nampa](https://github.com/kenoph/nampa) plugins.
The second and more powerful (but more complicated) mechanism is to leverage the _binaryninjaui_ module. Additional documentation is forthcoming, but there are several examples ([1](https://github.com/Vector35/kaitai), [2](https://github.com/Vector35/snippets), [3](https://github.com/Vector35/binaryninja-api/tree/dev/python/examples/triage)), and most of the APIs are backed by the [documented C++ headers](https://api.binary.ninja/cpp). Additionally, the generated _binaryninjaui_ module is shipped with each build of binaryninja and the usual python `dir()` instructions are helpful for exploring its capabilities.
## Function Starts, Sizes, and Ending
### How big is a Function?
One of the common questions asked of a binary analysis platform is "how big is a function?". This is a deceptively simple question without a simple answer. There are rather several equally valid definitions you could give for what is the size of a function:
- the total sum of all basic blocks?
- the highest virtual address in the function minus the lowest virtual address?
- the address of return instruction subtracted from the entry point
Except that last one is a trick of course. Because not only can functions have multiple return instructions, but they may have multiple entry points (as is often the case with error handling).
Basic blocks have, by definition, a start, and an end. Basic Blocks can therefore have consistent sizes that all binary analysis tools would agree upon (though more formal analysis might stop basic blocks on call instructions while for convenience sake, most reverse engineering tools do not).
Summing up the basic blocks of a function is one way to produce a consistent size for a function, but how do you handle bytes that overlap standard function definitions, for example, via a tail call? Or via a mis-aligned jump where a byte is in two basic blocks? Different tools may resolve those ambiguous situations in different ways, so again, it is difficult to compare the "size" of any one binary analysis tool to another.
In Binary Ninja, there is no explicit `.size` property of functions. Rather, you can choose to calculate it one of two ways:
```
function_size = current_function.total_bytes
# or
function_size = current_function.highest_address - current_function.lowest_address
```
Total bytes is similar to the first proposed definition above. It merely sums up the lengths of each basic block in the function. Because Binary Ninja allows bytes to exist in multiple blocks, this can cause bytes to be "double" counted, but this definition is consistent within BN itself, if not always with other tools in some edge cases.
### When does a Function stop?
One reason that having an "end" might be useful in a function (as opposed to the `.highest_address` in Binary Ninja), would be to make it a property that controls the analysis for a function. Unlike some other systems where it's possible to define the start and end of a function, in Binary Ninja, you merely define the start and allow analysis to occur naturally. The end results when all basic blocks terminate either in:
- an invalid instruction
- a return instruction
- a call to a function marked as `__noreturn__`
- a branch to a block already in the function
- any other instruction such as an interrupt that by its definition stops analysis
So how do you tell Binary Ninja how big a function is? The answer is you don't directly, but you can instead direct its analysis. For example, if a function is improperly not marked as a noreturn function, edit the function properties via the right-click menu and set the property and all calls to it will end analysis at that point in any callees.
Likewise, you can do things like change memory permissions, patch in invalid instructions, [change an indirect branch's targets](https://github.com/Vector35/binaryninja-api/blob/dev/python/examples/jump_table.py), or use [UIDF](https://binary.ninja/2020/09/10/user-informed-dataflow.html) to influence analysis such that it ends where desired.
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