Software Visualization

CS 4460 - Information VisualizationMany slides courtesy John Stasko, with edits by J. Foley

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“The use of the crafts of typography,graphic design, animation, andcinematography with modern human-computer interaction and computergraphics technology to facilitate boththe human understanding and effectiveuse of computer software.”

Price, Baecker and Small, ‘98

Software Visualization• Definition

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Challenge• Unlike much information

visualization, however, software is often dynamic, thus requiring our visualizations reflect the time dimension- History views- Animation- ...

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Subdomains• Two main subareas of software

visualization- Program visualization

Use of visualization to help programmers, coders, developers

Software engineering focus- Algorithm visualization

Use of visualization to help teach algorithms and data structures

Pedagogy focus

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Caveat• This is a HUGH area• Presentation goal: provide flavor of

kinds of techniques and systems that have been created- Lots of screen shots- Some videos

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Program Visualization• Can be as simple as enhanced views

of program source• Can be as complex as views of the

execution of a highly parallel program, its data structures, run-time heap, etc.

PV is a big research Area

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PV is a Big Product Category• Do Google searches on

- Program visualization- Code visualization- Software visualization

• Lots of companies/products

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What Can We Visualize?• ??????

What Can We Visualize?• Structure

- Static code structures• Behavior

- Dynamic (execution) code behavior- Test suite results- Bug issues/relations

• Evolution- Code repository structure/evolution- Program team communications patterns

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Static Code Structures• Call graphs• Object class hierarchy• Scope of variables• Code module sizes

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Execution DataSummaries, such as

- Total running time- Number of times a

method was called- Amount of time CPU

was idle- Bytes read/written- Memory high-water

mark- Etc etc etc

Details, such as- Memory allocations- System calls- Cache misses- Page faults- Pipeline flushes- Process scheduling- Completion of disk

reads or writes- Message receipt- Application phases- Etc etc etc

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Test Results• Hundreds, maybe thousands of tests• For each test:

- Purpose- Result (pass or fail)

Could be per-configuration or per-version- Relevant parts of the code

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Really Detailed Execution Data• Logging virtual machines can capture

everything- Enough data to replay program execution and

recreate the entire machine state at any point in time

- Allows “time-traveling”- For long running systems, data could span

months• Uses:

- Debugging- Understanding attacks

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SoftVis 2010 Program• An Interactive Ambient Visualization for Code Smells • CodePad: Interactive Spaces for Maintaining Concentration in Programming Environments• User Evaluation of Polymetric Views Using a Large Visualization Wall • Software Evolution Storylines 

AllocRay: Memory Allocation Visualization for Unmanaged Languages • Heapviz: Interactive Heap Visualization for Program Understanding and Debugging • A Map of the Heap: Revealing Design Abstractions in Runtime Structures • Trevis: A Context Tree Visualization & Analysis Framework and Its Use for Classifying Performance Failure Reports • Exploring the Inventor's Paradox: Applying Jigsaw to Software Visualization • Dependence Cluster Visualization • Towards Anomaly Comprehension: Using Structural Compression to Navigate Profiling Call-Trees • Embedding Spatial Software Visualization in the IDE: An Exploratory Study • 3D Kiviat Diagrams for the Interactive Analysis of Software Metric Trends • Graph Works - Pilot Graph Theory Visualization Tool • Visualizing Software Entities Using a Matrix Layout • ImpactViz: Visualizing Class Dependencies and the Impact of Changes in Software Revisions • VIPERS: Visual Prototyping Environment for Real-Time Imaging Systems • Towards Automated Analysis and Visualization of Distributed Software Systems • TIE: An Interactive Visualization of Thread Interleavings • GEM: Graphical Explorer of MPI Programs • Fault Forest Visualization • Visualizing Windows System Traces • Understanding Complex Multithreaded Software Systems by Using Trace Visualization • Zinsight: A Visual and Analytic Environment for Exploring Large Event Traces • Jype - A Program Visualization and Programming Exercise Tool for Python Off-Screen Visualization Techniques for Class Diagrams • An Automatic Layout Algorithm for BPEL Processes • Visual Comparison of Software Architectures • Representing Development History in Software Cities • Frank xDIVA: Automatic Animation Between Debugging Break Points • Understanding Relaxed Memory Consistency Through Interactive Visualization 

the Execution of Object Orientated Concurrent Programs 

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Commercial System Screen Shots

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Dependency Graph

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Ndepend commercial product; http://www.ndepend.com/SampleReports/OnDb4o/NDependReport.html#/?screen=Main

Graph and Matrix Rep’n

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NDepend

Code Hierarchy Treemap; Lines of Code

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NDepend

Open Source System Screen Shots • From The Source-NavigatorTM IDE

- http://sourcenav.sourceforge.net/index.html

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Hierarchy Browser Window

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Cross-Reference Browser Window

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Cross-Reference Browser Window

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Open Source System Screen Shots • From jBixbe

- http://www.jbixbe.com/index.html

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Structure Diagram

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Message Exchanges

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Multi-threading

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Research Examples• The following are discussions of a

variety of research prototypes• Lots and lots of papers in Software

Visualization Conference Proceedings

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Pretty Printing – Common Example

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More Sophisticated• But not so common to apply more

sophisticated graphic design• Baecker & Marcus, Design Principles

for the Enhanced Presentation of Computer Program Source Text, Proceedings CHI ’86

• No user testing • Looks nice

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Baecker&Marcus Pretty Printing

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SeeSoft System• Pulled-back, far away view of source

code• Map one line of source to one line of

pixels- Maintain indentation & length- Color code lines in meaningful way

• Like taping your source code to the wall, walking far away, then looking back at it

Stephen G. Eick, Joseph L. Steffen and Eric E. Sumner, Jr. “SeeSoft – A Tool for Visualizing Line-Oriented Software Statistics.” IEEE Transactions on Software Engineering, 18(11):957-968, November 1992.

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SeeSoft View

15,000 linesof code in 52 files

Selected code

Details of selected

code

Heat map for color-coded

characteristic

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SeeSoft• Code tracking (typically means

mapping a data attribute to line color) - Code modification (when, by whom)- Location of bug fixes for specific bug report- Location of code to implement a specific

feature- Code coverage or hotspots

• Interactive- Change color mappings- Link from heat map to code overview- Brush views – back to source code

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Tarantula• Developed at GT• Utilizes SeeSoft code view

methodology

• Takes results of test suite run and helps developer find program faults

• Key is the clever color mappingEagan, Harrold, Jones & Stasko InfoVis ’01Jones, Harrold & StaskoICSE ‘02

Tarantula ViewRed – code

executed by failed tests

Green - executed by passed tests

Yellow - executed by both passed and failed

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Tarantula – Selective Display

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Failed tests only

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Tarantula: Continuous Colour Mapping

• Extend discrete colour mapping by- Interpolating between red and green- Adjusting brightness according to

number of tests• Possibilities:

- Number of passed or failed tests- Ratio of passed to failed tests- Ratio of % passed to % failed

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Tarantula: Continuous Colour Mapping

• For each line L- Let p and f be the percentages of

passed and failed tests that executed L- If p = f = 0, colour L grey- Else, colour L according to

Hue: p / ( p + f ), where 0 is red and 1 is green

Brightness: max( p, f )

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Tarantula: Future Work• Does it help find bugs?

- Seems like it should• Link red lines to the tests

- Static- Dynamic

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Gammatella - Visualization of Program-Execution Data for Deployed Software

Orso, Jones and Harrold, Proc. Of ACM Symp. on Software Visualization, June 2003, pp. 67-76.

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Gammatella: Tri-Level Representation

• System level- Treemap of package/class hierarchy- Size – code lines- Smaller areas colored differently

according as code lines colored differently

• File level:- SeeSoft-like view of code

• Statement level:- Source code (colored text)

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Gammatella screen shot

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Multiple

linked views

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Gammatella• Displays program execution data

- From data base of executions• Visualization of data from many

executions- Code coverage and profiling data- Execution properties

OSJava versionErrorsEtc etc etc

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Color Coding• Two variables

- Hue: Red to yellow to green- Intensity: Dark to light

• Various attributes can be mapped to color- Execution profile: red frequent, green

infrequent; intensity not used- Other mappings not discussed

What could be done???

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System-Level View

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Execution Bar

Gray: Never executed

code

Colored subdivisions within

a module block indicate amounts of code with various

usages

Execution Bar• Each small vertical bar represents one

program execution (test run)- Color code – results of test run

• May be hundreds of test runs• Treemap shows information for selected test

run(s)- Select one or range of runs by pointing- Select multiple non-contiguous runs via data

base query on test run properties- For multiple executions, color codings are

averages

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Gammatella: Critique• Complete system – not just a

visualization• Nicely links code to structure• Trial usage discovered useful but

high-level information- Mainly relied on system view

• Needs more usage to determine utility

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CVSscan: Visualization of Code Evolution

“CVSscan: Visualization of Code Evolution”, Voinea, Telea, and van Wijk, SoftVis 2005

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Original presentation by Summer Adams

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Overview• Objective - understand code evolution over

multiple versions - support maintenance• Want to answer questions such as

- What code lines were added, removed, or altered?When? By whom?

- Which parts of code are unstable?- How are changes correlated?- How are development tasks distributed?- Context in which code segment appeared?

Screen Shot

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View of code, line by

line and version by

version

Versions

Portion of code from selected version

Approach• Takes information from CVS

(Concurrent Versioning System) source code repository

• Uses UNIX’s diff command to compare versions

• Visualizes each version as column- Lines of code represented as in SeeSoft

As thin horizontal color-coded strip

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Two Code Visualization Formats• File-based• Line-based

Local: like SeeSoft, just current lines

Global: includes future inserts

Code Visualization - Examples• 65 software

versions• Color code

- Green – constant- Yellow – modified- Red - modified by

deletion- Light blue - modified

by insertion- Light gray in global

view - inserted and deleted lines

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Code clean-up

stage

Global: includes future inserts

Local: like SeeSoft, just current lines

Metrics• Metrics for

each version- Code size

• Metrics for each line of code- Author- Lifetime at

current line• Other ideas

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Per version metric

Per code line

metric

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Overall View

Preset zoom levels

Use Scenario

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Status Construct

Author

The same code, viewed with three different attributes being color-coded.

The modified piece of code (yellow status) is a comment (green status) and was modified by programmer whose color is purple

(would be nice to have color key )

CVScan Evaluation• Modest – two users, each on different

code repository- Perl Script, 65 versions, 457 lines- C program, 60 versions, 2900 lines

• 15 minutes training, 15 minutes use• Assessment

- General usability- think out loud- Effectiveness –code understandings

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CVScan Evaluation Results• Perl (65 versions, 457 lines)

- Familiar with overall organization of the file- Focus of each developer- Areas of significant modifications- What modifications referred to

• C (60 versions, 2900 lines)- Did not have clear image of code’s evolution- Did conclude that was legacy code adapted by

mainly two users to support IPv6 network protocol

- Pointed out major modification to memory manager

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CVScan Critique• Very modest evaluation –

inconclusive• Use of metrics not very well

developed• Nicely leverages SeeSoft to

visualizing multiple versions of same code

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Stepping Up• Next step in program visualization is

to help debugging and performance optimization by visualizing program executions- Data, data structures, run-time heap,

memory, control flow, data flow, hot spots, ...

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Graph Visualization• Area that we’ve already studied

which is very important to SV• Graphs pop up everywhere in

software• Common use: Visualize a call graph,

visualize a flow chart, ...

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Sample CallGraph View

Software Projects• Come up a level, to group of

developers working on a code base• What might you want to understand?

- For instance, in an open source project?

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Stargate: An Author-Centric Approach to Software Project Visualization

Ogawas and Ma, Stargate: An Author-Centric Approach to Software Project Visualization Proceedings of IEEE PacificVis 2008 March, 2008

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Stargate – Start with Modified Sunburst

Stargate – Add Developers• Dots are

developers- Placed close

to files they worked on

- Size => amount of activity

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Stargate – Add Communications

• Email communications among developers

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Stargate – Add File History

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Stargate Video• http://vidi.cs.ucdavis.edu/research/vi

deos/stargate

• (Show from SV folder)

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SV Takeaways• Multiple dimensions to SV

- Static code/program relationships- Dynamic code/program relationships- Testing/coverge- Project evolution

• Many methods we have studied are applicable, such as ways to depict- Time-varying behaviors- Object relationships

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The End

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FIELD• Program development and analysis

environment with a wide assortment of different program views- Integrated a variety of UNIX tools- Utilized central message server

architecture in which tools communicated through message passing

Reiss Software Pract & Exp ‘90

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FIELD Interface

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Dynamic Call Graph View

Currentlyactive

On callstack

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Class Browser

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Heap View

Color can beWhen allocatedBlock sizeWhere allocated

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3D Call Graph

Selected file

Collapsed file

Groups of calls

Visualizing Application Behavior on Superscalar Processors

Chris Stolte, Robert Bosch, Pat Hanrahan, and Mendel RosenblumProc. InfoVis 1999

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Superscalar Processors: Quick Overview• Pipeline• Multiple Functional Units

- Instruction-Level Parallelism (ILP)• Instruction Reordering• Branch Prediction and Speculation• Reorder Buffer

- Instructions wait to graduate (exit pipeline)

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Critique• Most code doesn’t need this level of

optimization, but- The visualization is effective, and would

be useful for code that does- May reduce the expertise needed to

perform low level optimzation• Might be effective as a teaching tool• Bad color scheme:

black/purple/brown• Does it scale with processor

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Concurrent Programs• Understanding parallel programs is

even more difficult than serial• Visualization and animation seem

naturals for illustrating concurrency• Temporal mapping of program

execution to animation becomes critical

Kraemer & Stasko JPDC ‘93

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POLKA• We developed a system, POLKA, that

was designed to help people build visualizations of concurrent programs- Used for both program and algorithm

visualization- Used for different programming models:

message passing, shared memory threads, ...

Stasko & Kraemer JPDC ‘93

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Message Passing Systems

PVM/Conch

Topol, Stasko, Sunderam IJPDSN ‘98

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Shared Memory Threads

Zhao & Stasko TR ‘95Pthreads

Strata Various: Multi Layer Visualization of Dynamics in Software System Behavior

Doug Kimelman, Bryan Rosenburg, Tova RothProc. Fifth IEEE Conf. Visualization ’94, IEEE Computer Society Press, Los Alamitos, Calif., 1994, pp. 172–178.

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Strata Various• Trace-driven program visualization• Trace: sequence of <time, event>

pairs• Events captured from all layers

(strata, hence the name):- Hardware- Operating System- Application

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Strata Various: Why Multi-Strata?• Debugging and tuning requires

simultaneously analyzing behaviour at multiple layers of the system

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Call tree

Accumulated process times

Processrun history

Kernelperformancestats

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Goals• Facilitate debugging by providing

execution time information• Faster assimilation of execution data• Facilitate visual correlations, trends

and anomalies• Several layers including user-level

libraries, OS and hardware

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PV• Trace-driven• Trace: Time ordered sequence of

events of interest• Synchronous and asynchronous

configurations• Multiple concurrent views

representing the different layers

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Views• Process scheduling and System

activity- Scheduling view- Activity view

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Views• Memory Activity and Application

Progress- Memory size view- Source of memory allocation- State of the page of the data segment

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Codeviews

Memory view

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Views• Hardware Activity and Source

Progress- Active loop view- Hardware performance statistics

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Conclusion• A more effective and comprehensive

visualization of software• Worthwhile for developers facing

performance issues• Room for improvement in

visualization• More direct traversal• Presentation of derived data

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Strata Various: Critique• Examples demonstrate usefulness• Fundamentally, a good idea

- Increasing importance as multi-core machines become standard

• Many windows- Titles not meaningful

• Dubious claim that tracing does not alter behavior

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What is Useful? 1991 Survey• Very useful

14,1,18,32,2,19,5

• Useless- 22,33,34

• All rest useful but not essential

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Software Visualization Tools: Survey and AnalysisSarita Bassil and Rudolf K. Keller, 1991

What is Useful?• Very useful

14,1,18,32,2,19,5

• Useless- 22,33,34

• All rest useful but not essential

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