![]() Meanwhile, in node 5, it is always false. ![]() In node 4, the condition always holds true, since x = 1. The second condition x = 1, corresponds to the two cloned nodes 4 and 5. In the second “clone”, x ! = 1 and y is 3. In the first “clone” variable, x holds the value 1 (since it corresponds to the positive branch of the if statement) and y holds the value 2 (which was stored in the node 2). They appear twice – one for the Then branch of the if statement and the second for the Else branch. The subsequent nodes of the control flow graph are duplicated. To support this kind of evaluation, CLion splits the exit statements of the if statement into two different contexts: Here we have two if blocks, and the way the first block is executed influences the functionality of the second block. Now, let’s look at a more complex example: Function foo always returns the value 2.In node 4, the variable y may only hold the value 2, since the control flow may come only from node 2 and never from node 3. This being the case, CLion concludes that the condition x = 1 at node 1 will always be true, and so the control flow never goes to node 3. This is because there’s only one call site for the function foo, which passes the value 1 in the argument. In the example above, CLion knows that at nodes 0 and 1, the parameter x always equals 1. By visiting the nodes of this graph from the start node towards the exit node, CLion can collect some valuable information.įor instance, CLion remembers which values may be stored in each variable for each statement. Each graph has one start node and one exit node, which correspond to the function’s entry and exit. This is a graph on which vertices are the statements in the program and edges are the control flow jumps between these statements (direct code execution, conditional jumps, loops, breaks, gotos, etc.).įor example, the control-flow graph at the right represents the function foo on the left:ĬLion builds the corresponding graphs for each function. Control Flow GraphĪll data flow inspections rely on the control-flow graph. Today, we’ll look at the basics of data flow analysis, including how it works in general, while presenting several real-world examples where it can help you write better code. We’re starting a series of blog posts to explain how some of these inspections work in CLion. Examples of these useful checks are checks for constant conditions, dead code, null pointer dereferences, memory leaks, and array index issues. It can reveal various code problems that might later lead to runtime issues, security breaches, and other vulnerabilities. ![]() CLion comes with a built-in data flow analyzer, which runs constantly when you are writing your code and helps improve your code’s quality.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |