Call graph

Author: s | 2025-04-24

功能介绍页面 graphing 里面 没有提到 call graph. Cluster Call Graphs, 给了一些call graph 的例子. The interactive Cluster Call Graphs show the function call graph, organized by file. There are several variants of this graph: Call, Call-by, Butterfly and Internal Call. 功能介绍页面 graphing 里面 没有提到 call graph. Cluster Call Graphs, 给了一些call graph 的例子. The interactive Cluster Call Graphs show the function call graph, organized by file. There are several variants of this graph: Call, Call-by

A very small call-graph. Call-graphs are directed graphs with

Input data and user actions.To generate a dynamic graph, the code must be instrumented and run through a profiling tool. As the program executes, the profiler monitors which functions call which other functions. The sequence of calls is recorded including important metadata like:Call counts – number of invocations Recursive metrics – cyclical dependencies Threading – parallel executionPerformance – runtime, memory, I/OThis rich runtime data is visualized in the final call graph.Static Call GraphsStatic call graphs analyze relationships within the source code itself, without executing the program. They include all potential interactions between functions based on the code structure.Static call graphs consider all possible function calls that could happen. The graph includes every callable function, not just those invoked in a specific runtime scenario.Here‘s a comparison:Dynamic Call GraphStatic Call GraphGenerated from running codeGenerated from source codeShows actual function callsShows all possible callsSpecific to input data and pathOverview of whole programUsed for profilingUsed for understanding structureIn summary, dynamic graphs profile precise execution paths, while static graphs analyze overall program structure. Both are very useful in different situations.Combining static and dynamic graphs provides a holistic view – seeing the forest and the trees. Use static for broad understanding augmented by dynamic hot path profiling.Now let‘s see how to easily generate both kinds automatically.Automatic Static Call Graph GenerationHere are two easy ways to automatically generate static call graphs without writing any extra code.Java Static Call GraphsMany Java IDEs like IntelliJ IDEA have built-in call graph functionality. You can automatically generate static call graphs as follows: Open your Java project in IntelliJ IDEANavigate to View > Tool Windows > Call Graph The call graph for your code will open in a new windowAs you navigate through files, the graph will update to focus on the current class or method. It‘s an interactive visualization that helps

mmsatari/vscode-call-graph: call graph for vscode - GitHub

Field-based Call Graph Construction for JavaScriptThis project implements a field-based call graph construction algorithm for JavaScript as described inA. Feldthaus, M. Schäfer, M. Sridharan, J. Dolby, F. Tip. Efficient Construction of Approximate Call Graphs for JavaScript IDE Services. In ICSE, 2013.Module main.js offers a command-line interface, which can be run using Node.js; invoke node main.js -h to get a list of command line arguments.The call graph constructor can be run in two basic modes (selected using the --strategy flag to main.js), pessimistic and optimistic, which differ in how interprocedural flows are handled. In the basic pessimistic approach (strategy NONE), interprocedural flow is not tracked at all; a slight refinement is strategy ONESHOT, where interprocedural flow is tracked only for one-shot closures that are invoked immediatel. The optimistic approach (strategy DEMAND) performs interprocedural propagation along edges that may ultimately end at a call site (and are thus interesting for call graph construction). Full interprocedural propagation (strategy FULL) is not implemented yet.All strategies use the same intraprocedural flow graph, in which properties are only identified by name; thus, like-named properties of different objects are conflated; this can lead to imprecise call graphs. Dynamic property reads and writes are ignored, as are reflective calls using call and apply; thus, the call graphs are intrinsically incomplete.Module flowgraph.js contains the code for extracting an intraprocedural flow graph from an Esprima AST (for convenience, a version of Esprima is included in esprima.js) annotated with name bindings for local variables (see bindings.js, which uses symtab.js and astutil.js).Modules pessimistic.js and semioptimistic.js implement the pessimistic and optimistic call graph builders, respectively. They both use flowgraph.js to build an intraprocedural flow graph, and then add some edges corresponding to interprocedural flow. Both use module callgraph.js for extracting a call graph from a given flow graph, by collecting, for every call site, all functions that can flow into the callee position. Both use module natives.js to add flow edges modelling well-known standard library functions.The remaining modules define key data structures, in several variants.Module graph.js implements graphs using adjacency sets, using sets of numbers as implemented by numset.js. The latter includes either olist.js to implement sets as ordered lists of numbers, or bitset.js to use bitsets (with disappointing performance, so we use ordered lists by default).Modules dftc.js, heuristictc.js and nuutila.js implement several transitive closure algorithms used by callgraph.js. By default, we use dftc.js which uses a simple, depth first-search based algorithm. heuristictc.js

Python Call Graph Python Call Graph 1.0.1 documentation

A call graph is a diagram that visually represents calling relationships between subroutines or functions in a computer program. It shows which functions call which other functions in a codebase. Call graphs are extremely useful for understanding, debugging, optimizing, and refactoring code. In this comprehensive guide, you‘ll learn:What call graphs are and why they‘re usefulThe difference between static and dynamic call graphs How to automatically generate call graphs for Java and Python codeTools and techniques for creating call graphs without writing additional codeHow call graphs can help optimize performance, find bugs, and improve your codeReal-world examples and advanced usage of call graphsWhether you‘re a novice programmer or seasoned developer, read on to master the skill of automating call graph creation.A call graph visually depicts calling relationships between functions in a program‘s source code. Each node represents a function, while edges show one function calling another. Here‘s a simple example call graph:The main() function calls functionA() and functionB(). FunctionA() then calls functionC(). This sequence of calls is straightforward to see in the diagram.Call graphs get more complex as codebases grow. Large programs may have thousands of functions interacting in intricate ways. Call graphs help developers untangle and comprehend these complex relationships.Over my 15+ years of programming, I‘ve used call graphs to optimize everything from embedded operating systems to big data applications. Time and again they‘ve helped me visualize tangled code that would have taken weeks to fully grasp otherwise.I‘ll never forget the months I spent debugging an intermittent frozen UI in an Android app. The culprit ended up being an overloaded activity callback deeply nested in the view hierarchy. The 10-level static call graph shone a light down into that dark tunnel!Enough war stories though; let‘s get into more detail on how call graphs work and how you can use them.Why. 功能介绍页面 graphing 里面 没有提到 call graph. Cluster Call Graphs, 给了一些call graph 的例子. The interactive Cluster Call Graphs show the function call graph, organized by file. There are several variants of this graph: Call, Call-by, Butterfly and Internal Call.

Call Graphs - docs.appdynamics.com

Emerged around call graph layouts and formatting. Adhering to conventions promotes comprehension and consistency across tools.Graph LayoutHierarchical – Calling relationships flow top-down Group related functions Left to right flow for sequential logicEmphasize fan-in and fan-out complexityNode Formatting Rectangles represent callable functionsFont size indicates importance Use vivid colors to distinguish areas Visual ComplexityMinimize edge crossings Feature key functions prominentlyAllow interactive node collapsingUse semantic zooming for abstraction Applying conventions ensures common understanding. Consistency also enables diffusion into broader software processes.Integrating Call GraphsOriginally call graphs were ad-hoc artifacts for optimization. But modern development practices require systematic integration.Agile Sprints – Include call graph reviews when estimating stories around complex components. Timebox graph analysis to avoid analysis paralysis.Code Reviews – Submit call graphs along with pull requests touching convoluted areas of code. Speed accurate review and approval.CI/CD Pipelines – Trigger call graph generation during build. Fail builds when graphs indicate architectural decay such as cyclic dependencies. Error Monitoring – Capture call graphs during application failures to aid debugging. Spot chains of bad input data or upstream api changes affecting functionality. Legacy Rescue – Prior to rewriting old systems, create complete call graphs documenting intricate dependencies. Safely disentangle business logic from aging frameworks.Performance Testing – Compare call graphs across releases to validate optimization refactors. Ensure speedups in one area don‘t indirectly degrade others. Security Analysis – Inspect call relationships in privilege management and authentication logic. Verify sensitive operations are properly gated. Code Standards – Enforce call graph complexity limits on new code during reviews. Keep modules simple with discrete single responsibility functions.Estimation – Quickly gauge project size by automatically counting function points in call graphs. Calibrate completion timelines accordingly.Integrating call graphs accelerates delivery while preventing technical debt across systems development, maintenance, and operation.Runtime Performance ConsiderationsDynamic call graph instrumentation inevitably impacts application performance. The profiler consumes CPU

An example of a call graph

The call flow including parameters and timings. It then outputs a visual graph showing the exact path traversal and runtime.But dynamic call graphs can provide much more detailed metrics:Call Counts: Number of invocations of each functionRecursive Data: Identify excessive recursion trees Concurrency: Distinguish parallel threads Performance: Capture runtime, memory allocation Exceptions: See traces leading to errorsFiltering: Narrow graph by package, class, etcpycallgraph offers extensive customization through its plugin architecture:from pycallgraph.output import GraphvizOutputfrom pycallgraph import Configfrom pycallgraph import GlobbingFilterconfig = Config(max_depth=5)config.trace_filter = GlobbingFilter(include=[‘mypackage.*‘])config.output = GraphvizOutput(output_file=‘filtered_graph.png‘) with PyCallGraph(config=config): main()The filtered dynamic graph provides focused profiling on areas of interest without overwhelming detail.For larger applications, sampling may be required to limit performance overhead. Advanced tools like pyff have specialized streaming modes and statistical call counts for low overhead.In summary, dynamic call graphs generated at runtime provide invaluable real-world optimization insights that static analysis alone cannot match.Call Graph ToolsBeyond the basic techniques shown above, developers can choose from many more advanced call graph utilities:Java Tools VisualVM – Open source GUI with customizable metricsJava Flight Recorder – Robust bundled with JDK profiler YourKit – Leading 3rd party Java profilerPython ToolsSnakeViz – Browser based Python visualizer Pyan – Static analysis specializing in call graphsprofiling – Built-in Python profiling module C/C++ Tools Callgrind – Precise dynamic call graphs via Valgrind gprof – GNU profiler analyzes call times Intel VTune – Powerful hotspot and concurrency analysisThese tools provide additional capabilities when basic graphs are not sufficient:Filter graphs by package, class, timeframe Analyze performance metrics like memory, runtimeProfile multi-threaded and remote applications Export graphs to various image, XML, and custom formats Learning to leverage call graphs takes programming skill to the next level. All top developers use them to analyze and enhance code. Understanding available options helps choose the right techniques.Call Graph ConventionsOver decades of evolution, best practices have

An example of a call graph.

Explore complex codebases.No modifications required! The static call graph integrates directly with your existing Java code.IntelliJ has strong default settings for graph layout and formatting. But you can customize the appearance if desired via preferences:File > Settings > Languages & Frameworks > Call GraphConfiguration options:Filtering – Limit graph to subset by package, class, etc Layout – Tree, organic, circular, force-directedImage Export – PNG, SVG These settings help reduce visual clutter for large projects.Python Static Call GraphsFor Python, my tool of choice is Pyan. It integrates with PyCharm to show call graphs with no more effort than the Java example: Install Pyan pluginOpen Python project Choose Analyze > Show Call GraphAn alternate method using pure Python libraries pycg and pyvis offers more customization:Install the modulespip install pycgpip install pyvis Run pycg to extract call data from your code into a JSON filepycg mycode.py -o callgraph.jsonParse the JSON into an interactive HTML visualization using pyvisimport pyvis net = pyvis.Network() net.from_json(‘callgraph.json‘) net.show(‘callgraph.html‘)The pyvis network diagram visualizes potential Python calls including across imported modules. Limiting the graph scope helps reduce complexity. Overall this is a simple process to autogenerate Python static call graphs without modifying application code.Between IDE built-ins and auxiliary tools, creating static call graphs is straightforward for most languages. Next let‘s look at runtime dynamic graphs.Dynamic Call Graph GenerationWhile static graphs provide the overall picture, dynamic call graphs drill down to exactly what‘s happening as code executes. Combining static and dynamic techniques gives the full perspective. Creating dynamic graphs requires running instrumentation to monitor your application. Let‘s use Python as an example.We‘ll use the pycallgraph module which handles all the implementation details behind the scenes: pip install pycallgraphBasic usage:from pycallgraph import PyCallGraphfrom pycallgraph.output import GraphvizOutputgraphviz = GraphvizOutput() graphviz.output_file = "my_app_graph.png"with PyCallGraph(output=graphviz): # run your application code main() As it runs, pycallgraph traces. 功能介绍页面 graphing 里面 没有提到 call graph. Cluster Call Graphs, 给了一些call graph 的例子. The interactive Cluster Call Graphs show the function call graph, organized by file. There are several variants of this graph: Call, Call-by, Butterfly and Internal Call. 功能介绍页面 graphing 里面 没有提到 call graph. Cluster Call Graphs, 给了一些call graph 的例子. The interactive Cluster Call Graphs show the function call graph, organized by file. There are several variants of this graph: Call, Call-by