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The Visual Code Navigator: An Interactive Toolset For Source Code Investigation. F. Boerboom, A. Janssen, G. Lommerse, F. Nossin, L. Voinea, A. Telea. Eindhoven University of Technology , the Netherlands. Outline. The Visual Code Navigator (VCN):

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f boerboom a janssen g lommerse f nossin l voinea a telea

The Visual Code Navigator: An Interactive Toolset

For Source Code Investigation

F. Boerboom, A. Janssen, G. Lommerse, F. Nossin, L. Voinea, A. Telea

Eindhoven University of Technology, the Netherlands



The Visual Code Navigator (VCN):

  • an environment for interactive visualization of industry-sizesource code projects
  • tuned for C/C++ code bases stored in CVS
  • targets understanding code evolution and code structure
  • based on three views with complementary purposes

How can we extract facts from source code?

What can the VCN source code views show?


Fact extraction

  • Notoriously difficult problem…Requirements (roughly):
  • completeness:- extracts all elements & cross-refs from source code - extracts correct information - complies with latest C/C++ standard - includes preprocessor facilities
  • tolerance:
  • - handles incomplete/incorrect/ambiguous code
  • efficiency:
  • - memory/speed efficient on industry-size code bases
  • availability:- can be built from source code, preferably cross-platform

Existing fact extractors

Testing: - get the tool as binary/source; try to build it

- analyze very large systems (>0.5MLOC) - select extremely messy C/C++ code - try with/without includes (incomplete) - check output for size, correctness, completeness, throughput

- investigate limitations’ causes

++ very good

+ good

o could be better

- limited

-- unacceptable/missing

? insufficiently tested



  • Many surprises:
  • most tools extract interface data quite ok
  • … but badly fail at parsing implementation (function bodies)
  • tolerance and completeness are mutually exclusive
  • completeness and performance are also complementary
  • GLR grammar based tools are by far the best
  • Overall, we found just one reasonably good tool: Columbus
  • However, it is:
  • closed-source
  • limited in some technical respects (template handling)
  • quite slow (1 hr 20 min for ~150000 LOC)

How can we do better than the above tools?


EFES: An own C/C++ fact extractor

  • We chose to build an own extractor:
  • based on the Elkhound C/C++ GLR parser
  • uses a modified preprocessor, for tolerance
  • extends the parser, for tolerance vs incomplete/incorrect code & handling templated code
  • uses compression techniques to compact/speed up output
  • So far:
  • tests on very large projects (>200 MLOC) look good
  • we are 3..7 times faster than Columbus
  • we produce the ‘bare’ info, no metrics yet

Hard, but unavoidable endeavour


EFES Architecture

source: any C/C++ project, possibly incomplete/incorrect code

preprocessor: libcpp, also used by GNU CPP

parser: Elsa – uses the Elkhound GLR parser generator

type checker: disambiguates code with type information

filter: limits output to a set of interest (e.g. files, scopes, …)

output generator: efficiently writes the output information to a file


EFES Enhancements

Several enhancements to ‘standard’ fact extraction:

preprocessor: enhanced CPP to produce exact location information (needed later for construct visualization & comparison)

parser & enhanced Elsa to:type checker: - parse incorrect code with extra grammar rules – errors are caught at scope level

- extended Elsa’s template support

- added checkpoints at top-form level to trap internal errors

filter: novel element; reduces output size dramatically, e.g. by skipping standard header information

output added compact binary output; reduces output size 10 times

generator: increases output speed 5 times

project lets users customize extraction (C++ dialect, filtering, parser

concept: strictness, what to output, etc)


Performance & Results


We are 3..7 times faster




  • We’ve build a powerful C/C++ fact extractor:
  • works on large projects (>200 MLOC)
  • handles incorrect/incomplete code well
  • extracts virtually all raw information there is
  • is 3..7 times faster than a known commercial solution
  • Desired additions
  • distil raw information into more interesting facts (metrics, patterns, etc)
  • add query layer atop basic extractor
  • add interactive visualization layer atop query layer

An evolving project



  • We have now our extracted facts:
  • variables, types, functions, classes…
  • cross-references between all these
  • location information (file, line, column) of each construct
  • We like to show it to the user & answer questions:
  • how is the code structured?
  • how are programming constructs distributed?
  • how has the code changed in time?
  • how are the typical function signatures used in a project?
  • …and so on

Several visualization tools


1) Syntactic view: 1 version, N files – code view

  • Basic idea:
  • combine a classical text editor with a pixel-based text display (e.g. SeeSoft) in a single view
  • let users smoothly navigate between the two
  • blend syntactic structures over code text using cushions

syntax tree








profile f(x)

border size x


Cushion vs ‘syntax highlighting’

  • clasical syntax highlighting is actually lexical lighlighting
  • we generalize and enhance syntax highlighting

syntax highlighting

structure cushions


Syntactic view: Navigation

user points the mouse at some code location…


Syntactic view: Spot cursor

…and brings the text in focus above the structure


Syntactic view: Structure cursor

…over a whole syntactic construct, if desired.


Syntactic view - Conclusions

  • Two main uses:
  • Overview:
  • good for showing up to 10-15000 LOC on one screen
  • colors code by construct type
  • easy to spot presence/distribution of constructs in code
  • Detail:
  • good for quick browsing a single source file
  • gives structure context information
  • typical question:
  • “where was that function with that doubly-nested for?”

2) Symbol view: N files, 1 version – interface view

  • Displays public symbols in source files
  • Nested by scope rules (global, namespace, method, argument)
  • Visualized using a cushion treemap, colored by symbol type

‘public’symbolsin files





global vars




Symbol view - Details

  • Treemap node size computation:

- leafs: function bodies: number of LOC in declaration

else number of LOC or sizeof()

- non-leafs: sum of children

  • Shading:- hue: construct type (typedef, function, argument, …)- saturation: construct nesting (global/class scope)
  • Targeted questions:

- “what kind of symbols are in a library’s headers?”

- “how are namespaces used in interface headers?”

- “does a header have a simple / uniform structure or not?”

- “are there heavy functions from a parameter-passing view?”


Symbol view: Example

C global


C++ std namespace


in file




3) Evolution view: M files, N versions

Basic idea: CVSscan tool [Voinea & Telea, ACM SoftVis’05]

time (version) axis

file axis

source code



Evolution view: M files, N versions

  • extends the CVSscan tool[Voinea & Telea, ACM SoftVis’05]
  • stacks several stripped-out file evolution views above each other
  • line color = construct type
  • helps spotting cross-file correlations (e.g. large changes)







function bodies


function headers


Evolution view - Results

  • We look for:
  • Large size jumps = large code changes
  • Size jumps correlating across more files at same version = cross-system changes
  • Less ‘wavy’ patterns = stable(r) files
  • Horizontal patterns = unchanged code


  • Method & materials: - VTK C++ library (1 MLOC, 100 versions) - 3 users with C++ but no VTK knowledge - 1 user with C++ and VTK knowledge (evaluator)
  • - quantitative and qualitative questions to be answered on VTK with and without VCN





are files fine/coarse grained?

what is the typical class interface structure?

what is the typical class implem. structure?

find & describe a few large evolution changes

what is the typical macro usage/frequency?

what is the typical comment usage/frequency?

preferred/first tool

optional/second tool



  • Results:
  • VCN allowed getting answers (much) faster than by pure classical source code browsing
  • views are complementary, serve different tasks in different ways
  • a single view is usually not enough
  • a fine-tuned, fast, integrated system is essential!
  • users reluctant to work with lame/suboptimal tools


fine insight

text editor


symbol view



fine insight


syntax view



  • Syntactic view:
  • cushions: OpenGL textures - superimposed, not blended
  • careful cushion border design (see paper)
  • Symbol view:
  • cushion treemap: OpenGL fragment programs
  • essential for interactive, fast navigation!
  • Evolution view:
  • column cushions: OpenGL textures
  • several LOC / pixel solve by software antialiasing
  • efficient tool design essential for smooth navigation in large code bases important for user acceptance


  • VCN: multi-view visual environment for understanding source code and its evolution
  • Syntax view: 1 version, N files (compiler)
  • Symbol view: 1 version, N version (linker)
  • Evolution view: M versions, N files
  • Dense pixel displays essential for viewing large datasets
  • Cushion techniques effective for visualizing various kinds of visual nesting (syntax,symbol,file,…)
  • Working to extend & generalize the VCN
  • What to do when M,N exceed a few hundred?

Check it out: www.win.tue.nl/~lvoinea/VCN.html