Topics in Information Visualization and Visual Analytics

Topics in

Information Visualization

and Visual Analytics

10. Software Visualization

1. Introduction

2. Static SoftVis

3. Dynamic SoftVis

4. Languages & Domains

10.1 Introduction

Software Visualization

 Definition:

•

“Software visualization is the use of computer graphics and

animation to help illustrate and present computer

programs, processes, and algorithms.”

 Application areas

• Teaching and education

• Program development (especially Software Engineering)

• Designing complex systems

10.1.1 Classification

Software

Visualization

[B. Price, R. Baecker, I. Small: A Principled Taxonomy of Software Visualization, In Journal of Visual Languages and Computing, Vol. 4, No. 3, S. 211-266, 1993.] Algorithm Animation Static Algorithm Visualization

Algorithm Visualization Program Visualization

Static Code Visualization Static Data Visualization Data Animation Code Animation VP PbD

10.1.1 Classification

Algorithm Visualization

 Definition:

• Visualization of high-level abstractions that describe software

 We distinguish:

• Static Algorithm Visualization

 Static visualization of algorithms  E.g.: flow charts, ...

• Algorithm Animation

 Dynamic visualization of algorithms

10.1.1 Classification

Program Visualization

 Definition:

• Visualization of concrete program code or data structures (static/dynamic)

 We distinguish

• Static code visualization

 E.g.: PrettyPrinter, program cards, ...

• Static data visualization

 E.g.: box-arrow diagrams of chained lists, ...

• Code animation

 E.g.: code view, ...

• Data animation

10.2 Static SoftVis

Software

Algorithm Visualization Program Visualization

Static Code Visualization Static Data Visualization Data Animation Code Animation VP PbD

10.2.1 Algorithms

 Static algorithm visualization

Flowcharts [http:// www.rff. c o m/or d e r_ p ro c e s s in g _ o p p o rtu n ity _ flo wc h a rt. h tm] [http:// www.rff. c o m/flowc h a rt_ s a mp les .h tm]

10.2.2 Programs

10.2.2 Programs

 Static code visualization (cont.)

10.2.2 Programs

 Static code visualization (cont.)

10.2.2 Programs

 [Murphy-Hill et al., An Interactive Ambient Visualization for Code Smells, SoftVis 2010]

10.2.2 Programs

Smell Name Short Description

DATA CLUMPS A group of data objects that is duplicated across code.

FEATURE ENVY A method is more interested in some other class than in its own class.

MESSAGE CHAIN A series of method calls to “drill down” to a desired object.

SWITCH STATEMENT A switch statement, typically duplicated across code

TYPECAST The program makes frequent use of the typecast operation.

10.2.2 Programs

Smell Name Short Description

LONG METHOD A method is too long to be easily understood. PRIMITIVE

OBSESSION

The program uses primitive values like int and built-in classes like String instead of domain-specific objects like Range and

TelephoneNumber.

MAGIC NUMBER A literal value is used directly, rather than through a named constant.

COMMENTS Thickly commented code is often bad code.

Refactor the code, and the comments may well become superfluous.

DUPLICATED CODE

The same code structure appears in more than one place.

10.2.2 Programs

 Static data visualization (data structure visualization)

Chained lists p q

h

10.2 Dynamic SoftVis

Software

Visualization

Algorithm Visualization Program Visualization

Static Code Visualization Static Data Visualization Data Animation Code Animation VP PbD

10.3.1 Algorithm Animation

 Definition:

• Is the process of abstracting a program’s data, operations, and semantics, and creating dynamic graphical views of these

abstractions. [Stasko]

 Why is algorithm animation important?

• Better understanding of the algorithm

• Design, troubleshooting, optimization

 Problems to handle

• Find the suitable graphical abstraction for algorithm states (data and operations) and the transition between states

10.3.1 Algorithm Animation

 History

• The first algorithm animation: Video “Sorting Out Sorting”, SIGGRAPH ’81

 Georgia Institute of Technology: Stasko

• XTango/Tango (1990) • Polka + Samba (Frontend) • Polka3D (1992)

 MIT later DEC-SRC: Brown (and Najork)

• Balsa (1985), Balsa II (1988) • Zeus (1992)

• Anim3D, Zeus3D (1993) • CAT (WWW, 1996)

10.3.1 Algorithm Animation

 XTANGO, 1990

[http://www.cc.gatech.edu/gvu/softviz/algoanim/xtango.html]

 Graphical primitives: nodes, edges, boxes, text

 Graphical operations on a higher abstraction level: predefined views

 Granularity: state sequences  transition animations

 Realization of the

path-transition paradigm

 Annotation of the algorithm with

Interesting event

10.3.1 Algorithm Animation

10.3.1 Algorithm Animation

 XTango-Animations (cont.)

Heap sort

Quick sort Bubble sort

10.3.1 Algorithm Animation

 XTango-Animations (cont.)

Convex hulls Topological sorting

10.3.1 Algorithm Animation



Specification methods:

(1) Event-driven animation (IEs)

(2) State-driven animation

(3) Visual programming (PbD)

(4) Pseudocode-languages/libraries

10.3.1 Algorithm Animation

(1) Event-driven Animation

• Specification through Interesting Events (IEs)

• For the first time realized in 1984 with BALSA

• Used in many systems (e.g., POLKA or GANIMAL)

• Animation takes place in so-called views

 Realization of the design pattern

MVC (model-view-controller)

• Approach:

1. Conceptual design of the animation

2. Identification of the IEs

10.3.1 Algorithm Animation

(1) Event-driven Animation

• Example system: GANIMAL, 2002

 Code and data animation

 Visualization of GANILA programs

• Swapping of two variable values in Heapsort:

s

10.3.1 Algorithm Animation

User

[S. Diehl and A. Kerren. Reification of Program Points for Visual Execution. In Proceedings of the First IEEE International Workshop on Visualizing Software for Understanding and Analysis, pages 100-109, 2002. IEEE Computing Society Press.]

[https://w ww .ad s .tuw ien. ac .at/peop le/ker ren/SV/]

10.3.1 Algorithm Animation

(2) State-driven Animation

• Declarative animation paradigm

• Realized for the first time in 1992 with the PAVANE system

 [G.-C. Roman, K.C. Cox, D. Wilcox and J.Y. Plun. Pavane: a system for

declarative visualization of concurrent computations. J. Visual Languages Comput., 3(2): 161--193, 1992]

• Mapping of program states to visualization states

• Approach:

1. Creation of an initial mapping from data structures to graphical interpretations

10.3.1 Algorithm Animation

(2) State-driven Animation

• Example system:

 Presented in 1999

 Code and data animation  Visualization of C programs  Smooth animations

 Logic-based visualization language Alpha

10.3.1 Algorithm Animation

Linker

C - Preprocessor

C - Compiler

Alpha - Compiler

Virtual

Machine

Control

Inference

Module

Visualizer

10.3.1 Algorithm Animation

 Procedural Approach

 Knowledge-based Approach

Program

Visualizer

Inference

Module

Visualizer

requests

10.3.1 Algorithm Animation

10.3.1 Algorithm Animation

(5) Automatic Animation

• For many developers this is the “silver bullet”

• Extremely difficult to realize

• Outstandingly suitable for error search because it is non-invasive and quick

• Early system was UWPI, 1990

 Try to discover abstract data types

 Expert system chooses the visualization

• Newer systems: e.g., JELIOT 3, 2003

 Semi-automatic paradigm based on a user-defined visual semantics of program structures

10.3.1 Algorithm Animation

10.3.1 Algorithm Animation

 Summary of specification methods in SoftVis

• Event-driven Animation:

 Flexible, powerful, intuitive  Overhead

• State-driven Animation

 Quick, less code  Limited by the system

• Visual Programming (PbD)

 Eventually animation for free  Complex VP system

• Pseudocode-Language/Libraries

 Eventually animation for free  Often domain-dependent

• Automatic Animation

10.3.2 Code/Data Animation

 Definition: Code and data animation is dynamic program visualization

 In principle, this part covers nearly all systems presented in this chapter

 Concrete programs -> many factors play an important role:

• Programming language used

• Application domain

10.3.2 Code/Data Animation

 There are a lot of general techniques/approaches known from the pure InfoVis field.

 Specific techniques for specific problems:

• Base techniques of visualization [Brown,Hershberger:98]

• Color [Brown,Hershberger:98]

• 3D animations

• Distributed visualization (WWW)

10.3.2 Code/Data Animation

 Base techniques

• Use of several views

 Detailed view of an algorithm

 Visualization of several algorithms at the same time

• Representation of the states

 Present dynamic behavior

• Chronological process of the algorithm (History)

 Focus on the key events

• Floating  discrete transitions

• Choice of the input data

 Small instances: details

10.3.2 Code/Data Animation

 Color

• Coding of the state of data structures

 Color represents an additional dimension  Global pattern are easier to recognize

• Identical color for the same data structure in different views

• Highlighting of important activities

 Temporary emphasis

 Should maintain the integrity of the emphasized information

• Clarification of recurring patterns

• History

10.3.2 Code/Data Animation

 3D animations

• Aesthetics and functionality

 Assumption: People are familiar with three dimensions

• Data structures or algorithms are inherently 3D

 Brownian motion  Polyhedrons

 …

• 3D allows to add additional information to a normal 2D representation

• Several views can be aggregated to one object

10.3.2 Code/Data Animation

 3D animations (cont.)

• Example system: ANIM3D, 1993

• Reason for 3D: 3D allows to add additional information to a normal 2D representation

• Input problem: Single Source Shortest Path (Dijkstra)

 The graph lies in the xy-plane

 Z-axis shows the costs for each node  Source has costs = 0

 Shortest path = increasing path with lowest height  Final results: shortest-path tree

10.3.2 Code/Data Animation

 3D animations (cont.)

[Marc Brown and Marc Najork. Algorithm Animation Using 3D Interactive Graphics. ACM Symposium on User Interface Software and Technology (November 1993), pages 93-100.]

10.3.2 Code/Data Animation

 Distributed visualization (WWW)

• With rising popularity of the WWW, many distributed animation systems were developed

• Vision of the

Electronic Classroom

• Example system: JCAT (Successor of CAT), 1996

 Java  platform independent

 Views similar to Zeus: window + logic, coupled using IEs

 Views are distributed among several computers with separate interactive control of the clients

10.3.2 Code/Data Animation

10.3.2 Code/Data Animation

 Sound/Audio (Auralization)

• Supports visual perception ( 4D)

• Clarifies recurring patterns

• Signalizes exceptions and important events

• Recursion depth

• Program and data auralization

10.3.2 Code/Data Animation

 Sound/Audio (cont.)

• Example system:

 First introduced: 1996  Simple preprocessor

 Adds calls to functions of a library  Application to Turbo Pascal programs

 Use of a hierarchical guiding theme design  Output using MIDI-interface

 Help to search errors in programs

[www.aur a lisa tio n .o rg ]

10.3.2 Code/Data Animation

Writeln ('Condition true') ; END. PROGRAM demob9 ; VAR a : Boolean ; BEGIN a := False ; IF a THEN

Writeln ('Condition true') ; END.

10.4 Languages & Domains

 Language paradigms

• Imperative programming languages

• Functional programming languages

• Object-oriented programming languages

• Logic programming languages

 Domain-specific animations • Algorithmic geometry • Concurrent programs • Real-time animations • Computational models • Animations of proofs [A. Kerren and J. T. Stasko . Algorithm Animation -C ha p te r Introd u c tio n . In Software Visualiza tio n , volume 226 9 o f L N C S Sta te- of- the-Ar t Su rv e y , pag e s 1-15 . Spring e r, 200 2 .]

10.4.1 Concurrency



Parallel/distributed programs

and/or computers to increase performance

 Two kinds of communication:

• message passing

• shared memory

 Concurrency of parallel programs (e.g., communication) cannot really be shown textually

 Many problems in parallel programming are inherently visual (e.g., deadlocks)



Non-determinism

very good understanding of the program

r1

r2

p1

10.4.1 Concurrency

 Classification

• Visualization of the

performance

• Visualization of the

Semantics

•

online

Advantage: visualization during execution

•

post-mortem

Advantage: lower influence of the program; more flexible visualization

10.4.1 Concurrency

 Example system: PARADE, 1995

[Stasko, John T., “The PARADE Environment for Visualizing Parallel Program Executions: A Progress Report,” Graphics, Visualization, and Usability Center, Georgia Institute of

Technology, Atlanta, GA, Technical Report GIT-GVU-95-03, January 1995.]

 Flexible framework

• Supports standard solutions and application-specific visualizations

 Supports both semantic and performance visualizations

10.4.1 Concurrency

Thread-Visualization

10.4.2 Computational Models

 Automata

• Finite automata (transition diagrams)

• Push-down automata • Cellular automata • …  Turing machines  Statecharts  Abstract machines  Petri nets  Quantum computers

10.4.2 Computational Models

 Example system: GANIFA, 2002

[S. Diehl and A. Kerren. Generierung interaktiver Animationen von Berechnungsmodellen. Informatik - Forschung und Entwicklung, 17(1):12-20, 2002. Springer]

 Realized with GANIMAL-framework

 Generation of finite automata from regular expressions

 Simulation of the general finite automata based on the input of arbitrary words

 Supports new forms of learning

[http:// rw4.cs .u ni-s b. de/~gan imal/GA N IFA /]

[A. Kerren. Generation as Method for Explorative Learning in Computer Science Education. In Proceedings of ITiCSE ‘04, pages 77-81, Leeds, UK, 2004. ACM.]

10.4.2 Computational Models

 Generation of a NFA from a RE

(a|b)*

10.5 Software Engineering

 Construction tools

• Requirements and design

• Visual programming

• Graph layouts for general aims

• …

 Analysis tools

• Program visualization (e.g., for debugging, test runs, evolution)

• Re-engineering

• Reverse-engineering

• …

10.5 Software Engineering

 In many cases, good graph layout tools are important

• Huge need of graph visualization

 Statecharts

 Structure diagrams  Class diagrams

 Unified Modeling Language (UML)  …

• There are many layout algorithms

• There are many different (industrial) tools

10.5.1 UML

 [Gutwenger et al., A New Approach for Visualizing UML Class Diagrams, ACM SoftVis, 2003]

Industry Layout GoVisual

10.5.2 Test Cases

 Visualization of test information

• Example system: TARANTULA, 2001

 Input: Source-Code & Test-Suite

• Visualization

 Coloring to show the contribution of statements to the test result  Users can identify potential wrong statements

Discrete approach: [Take n from a lecture by J. Stasko ]

10.5.2 Test Cases

13: print(“Middle number is:”, m); }

10.5.2 Test Cases