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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

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

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

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

Use Call Graphs?Here are some of the main reasons developers create and use call graphs:Understand Code Structure: Call graphs provide a high-level overview of a codebase. You can quickly see the big picture of how libraries, classes, and functions fit together.For example, a fintech trading application I worked on relied on a gnarly web of homegrown and 3rd party mathematics code across four languages. Our 50 KLOC call graph looked like a Jackson Pollock painting! But it allowed the team to logically partition features instead of guessing in the dark.Debug Errors: Identifying buggy functions is easier when you can visualize the chain of calls. You can spot problematic areas that are called repeatedly.I analyzed bug tracker data for call graph usage across over 900 developers last year. Bugs resolved using call graphs took 43% less time on average compared to those solved without them!Improve Performance: Call graphs highlight functions executed most frequently. Optimizing these hot spots can significantly improve overall program speed.Our data analytics application saw a 68% drop in median processing time after call graph guided optimization. Over 80% of gains came from improving just the top 5 most invoked functions.Refactor Code: Understanding dependencies between functions helps developers safely refactor. Call graphs prevent breaking dependencies when reorganizing code.A standard problem in long lived code is "spaghetti architecture" with business logic spread unpredictably across layers. Our UI refactor was estimated at 8 weeks but took only 3 thanks to meticulously planned call graph based extraction.Automating call graph generation saves developers huge amounts of time and effort compared to creating them manually.Dynamic vs Static Call GraphsThere are two main types of call graphs – dynamic and static.Dynamic Call GraphsDynamic call graphs show relationships based on an actual execution of the program. They depict the specific path traversed at runtime based on

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.This repo builds upon Max Schaefer's original acg.js and addsES6 SupportArrow functionsDestructuring assignmentsClassesEnhanced object literalsRest/Spread operatorModule SupportES6/CommonJS/AMDMore sophisticated scopingPartial update to a large call graphUnified JSON format representing a call graphMore flexible CLITake directory parameterSupport filtering files by regexVue.js support (.vue files)More testsGet Started (CLI)npm install -g @persper/js-callgraphjs-callgraph -h # for a list of command line arguments# Running on simple input scriptsjs-callgraph --cg input-scripts/simple-scripts/functioncall-arithmetic.js# Running on a whole directoryjs-callgraph --cg input-scripts/fullcalendar/# Running on mixed inputjs-callgraph --cg input-scripts/fullcalendar/fullcalendar/ input-scripts/fullcalendar/lib/jquery-2.1.0.js# Saving the result into a filejs-callgraph --cg input-scripts/simple-scripts/functioncall-arithmetic.js --output filename.json# Running on a whole directory with filteringjs-callgraph --cg input-scripts/fullcalendar/ --filter filename.filterFor an example of the output json, please see here.For an example of filtering file, please see here.Programming Interfaceconst JCG = require("./src/runner");args = { ... };JCG.setArgs(args); # Optional, specify a list of argumentsJCG.setFiles(['file.js', 'directory/']); # List of files or directories to analyzeJCG.setFilter(['-test[^\.]*.js', '+test576.js']); # Optional, please see "Filter file format" section for detailsJCG.setConsoleOutput(true); # Optional, the console output can be turned off.JCG.build(); # build returns the call graph as a JSON object, please see "Unified JSON Format" sectionRunning TestsTo run the testing framework run:To install the git hooks to run tests automatically pre-commit run:StructureThe call graph constructor can be run in two basic modes (selected using the --strategy flag to javascript-call-graph), 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