OORPT Object-Oriented Reengineering Patterns and Techniques

OORPT Object-Oriented Reengineering Patterns and Techniques
5. Software Visualization Prof. O. Nierstrasz

Roadmap Introduction Static Code Visualization
OORPT — Software Visualization Roadmap Introduction SV in a Reengineering Context Static Code Visualization Examples Dynamic Code Visualization Understanding Packages Understanding Evolution Conclusion © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Program Visualization
OORPT — Software Visualization Program Visualization Reduction of complexity Generate different views on software system Visualization is powerful. But Can be complex (active research area), Efficient space use, crossing edges, focus... Colors are nice but there is no convention Nice pictures do not imply valuable information Where to look? What is important? © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

A Bit of Vocabulary Visualization Software Visualization
OORPT — Software Visualization A Bit of Vocabulary Visualization Information Visualization Software Visualization Algorithm Visualization Program Visualization Static Code Visualization Dynamic Code Visualization The overall goal is to reduce complexity © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

(Information) Visualization
OORPT — Software Visualization (Information) Visualization Bertin assessed three levels of questions Lower perception (one element) Medium perception (several elements) Upper perception (all elements/the complete picture) In Information Visualization it’s all about the reduction of complexity Information Collection What to visualize How to visualize © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Software Visualization
OORPT — Software Visualization Software Visualization “Software Visualization is the use of the crafts of typography, graphic design, animation, and cinematography with modern human-computer interaction and computer graphics technology to facilitate both the human understanding and effective use of computer software.” Price, Baecker and Small, “Introduction to Software Visualization” 2 main fields: Algorithm Visualization Program Visualization The main conceptual problem: “Software is intangible, having no physical shape or size. Software visualisation tools use graphical techniques to make software visible by displaying programs, program artifacts and program behaviour.” [Thomas Ball] © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

In a Reengineering Context
OORPT — Software Visualization In a Reengineering Context Work on old systems, dialects New tools are not processing your (C++) dialect Approaches Scalability is crucial Efficient (time/information obtained) Need a clear focus Solutions Minimize tools support Use existing proven tools (Rigi, CodeCrawler, Jinsight) Do it yourself but simple thing first Limited resources Work on old systems, dialects New tools are not processing your specific dialect of C++ Minimize tools support Do it yourself but simple thing first Approaches Scalability is crucial Efficient (time/information obtained) Need a clear focus © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

The Reengineering Life-cycle
OORPT — Software Visualization The Reengineering Life-cycle (0) requirement analysis Requirements (2) problem detection (3) problem resolution Designs (2) problem detection issues Tool support Scalability Efficiency (1) model capture Code (4) program transformation © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Program Visualization
OORPT — Software Visualization Program Visualization “Program visualization is the visualization of the actual program code or data structures in either static or dynamic form” [Price, Baecker and Small] Static code visualization Dynamic code visualization Generate different views of a system and infer knowledge based on the views Complex problem domain (current research area) Efficient space use, edge crossing problem, layout problem, focus, HCI issues, GUI issues, … Lack of conventions (colors, symbols, interpretation, …) © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Program Visualization II
OORPT — Software Visualization Program Visualization II Level of granularity? Complete systems, subsystems, modules, classes, hierarchies,... When to apply? First contact with a unknown system Known/unknown parts? Forward engineering? Methodology? © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Static Code Visualization
OORPT — Software Visualization Static Code Visualization The visualization of information that can be extracted from the static structure of a software system Depends on the programming language and paradigm: Object-Oriented PL: classes, methods, attributes, inheritance, … Procedural PL: procedures, invocations, … … © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Example 1: Class Hierarchies
OORPT — Software Visualization Example 1: Class Hierarchies Jun/OpenGL The Smalltalk Class Hierarchy Problems: Colors are meaningless Visual Overload © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Example 2: Tree Maps Pros Cons Useful for the display of HDDs
OORPT — Software Visualization Example 2: Tree Maps Pros 100% screen Large data Scales well Cons Boundaries Cluttered display Interpretation Leaves only Useful for the display of HDDs © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Examples 3 & 4 Euclidean cones Hyperbolic trees Pros: Cons:
OORPT — Software Visualization Examples 3 & 4 Euclidean cones Pros: More info than 2D Cons: Lack of depth Navigation Hyperbolic trees Good focus Dynamic Copyright © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Kind of Code Maps From Marcus,Feng, Maletic Software Visualization’03
OORPT — Software Visualization Kind of Code Maps From Marcus,Feng, Maletic Software Visualization’03 Simple Overview File-based One “Dot” = one line © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Nesting Level © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz
OORPT — Software Visualization Nesting Level © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Control Flow © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz
OORPT — Software Visualization Control Flow © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Evaluation Simple to draw Good overview Limited semantics
OORPT — Software Visualization Evaluation Simple to draw Good overview Limited semantics Patterns difficult to identify because of line breaks © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

One Case for 3D Most of the time 3D is not worth the trouble but…
OORPT — Software Visualization One Case for 3D Most of the time 3D is not worth the trouble but… © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

OORPT — Software Visualization
Usual Problems with 3D No spatial semantics (is above better than below?) Scalability Extra effort Space localization © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Quantitative Information
OORPT — Software Visualization Quantitative Information 3D useful for quantitative information © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Accessing Hidden Information
OORPT — Software Visualization Accessing Hidden Information © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Evaluation Worthwhile for representing quantitative information
OORPT — Software Visualization Evaluation Worthwhile for representing quantitative information Spatial information is not really sexy Requires more work Requires more tooling © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Class Diagram Approaches
OORPT — Software Visualization Class Diagram Approaches For example UML diagrams… Pros: OO Concepts Good for small parts Cons: Lack of scalability Require tool support Requires mapping rules to reduce noise Preconceived views © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Class Diagram Examples
OORPT — Software Visualization Class Diagram Examples © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Entity-Relationship visualization
OORPT — Software Visualization Example 5a: Rigi Scalability problem Entity-Relationship visualization Problems: Filtering Navigation © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Example 5b: Rigi Entities can be grouped Pros: Cons: Scales well
OORPT — Software Visualization Example 5b: Rigi Entities can be grouped Pros: Scales well Applicable in other domains Cons: Not enough code semantics © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Evaluation Pros Cons Intuitive approaches
OORPT — Software Visualization Evaluation Pros Intuitive approaches Aesthetically pleasing results Cons Several approaches are orthogonal to each other Too easy to produce meaningless results Scaling up is sometimes possible, however at the expense of semantics © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Dynamic Code Visualization
OORPT — Software Visualization Dynamic Code Visualization Visualization of dynamic behaviour of a software system Code instrumentation Trace collection Trace evaluation What to visualize Execution trace Memory consumption Object interaction … © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Example 1: JInsight Visualization of execution trace
OORPT — Software Visualization Example 1: JInsight Visualization of execution trace © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Example 2: Inter-class call matrix
OORPT — Software Visualization Example 2: Inter-class call matrix Simple Reproducible Scales well Excel? © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Mural View The algorithm takes an image of M x N elements and scales it into a mural of I x J pixels. © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

The Algorithm 1) for each i,j set mural_array[i][j] to zero
OORPT — Software Visualization The Algorithm 1) for each i,j set mural_array[i][j] to zero 2) for each element m,n of information a) compute x = m / M * I, y = n / N * J b) determine the proportion of this point that lies in each of the four surrounding mural_array entries (totals to 1.0): mural_array[floor(x)][floor(y)] mural_array[floor(x)][ceil(y)] mural_array[ceil(x)][floor(y)] mural_array[ceil(x)][ceil(y)] c) add each of the proportions determined in the previous step to the existing values of each corresponding mural_array entry i) update max_mural_array_value to keep track of the maximum mural_array[][] value 3) for each i,j in the mural_array a) map the value mural_array[i][j] / max_mural_array_value to a grayscale or color intensity varying scale, or to pixel size, depending on the type of mural being created b) color and draw the pixel at i,j of the mural based on mapping computed in the previous step © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Class View Smith, Munro, Runtime Visualization of Object Oriented Software, Vissoft 02 © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

A Class Methods/#invocation
OORPT — Software Visualization A Class Methods/#invocation © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Method Calling Counts © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Evaluation Entities as objects Quickly spot the important methods
OORPT — Software Visualization Evaluation Entities as objects Quickly spot the important methods For complete scenario may be difficult to reproduce Requires interactivity Layout can be a problem © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Evaluation Can handle large amounts of data
OORPT — Software Visualization Evaluation Can handle large amounts of data Not useful for all kinds of data © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Dynamic SV: Evaluation
OORPT — Software Visualization Dynamic SV: Evaluation Code instrumentation problem Logging, Extended VMs, Method Wrapping, C++ preprocessing is heavy Scalability problem Traces quickly become very big (1Mb/s) Completeness problem: scenario driven Pros: Good for fine-tuning, problem detection Cons: Tool support crucial Lack of abstraction without tool support © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Butterfly for Package Important: first level of structure
OORPT — Software Visualization Butterfly for Package Important: first level of structure Package are complex entities Not always have to be cohesive (subclasses of a framework grouped together) Team-oriented Contain intent of structure and deployment © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Butterfly View © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz
OORPT — Software Visualization Butterfly View © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Butterflies © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz
OORPT — Software Visualization Butterflies © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Evaluation Focus on packages Entities as objects Patterns
OORPT — Software Visualization Evaluation Focus on packages Entities as objects Patterns Shape easily recognisable Lot of information condensed Problems with value normalisation © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Cities Houari et al © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Boxes = Packages Height Color Twist 2D +
OORPT — Software Visualization Boxes = Packages Height Color Twist 2D + © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

© Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz
OORPT — Software Visualization © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Evolution © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz
OORPT — Software Visualization Evolution © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Evaluation Interesting use of pseudo-3D Limited mapping possibility
OORPT — Software Visualization Evaluation Interesting use of pseudo-3D Limited mapping possibility Good overview Good spotting facility Limit of metaphor Are shanty towns that bad? © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Understanding Evolution
OORPT — Software Visualization Understanding Evolution Information is in the history! Overwhelming complexity How can we detect and understand changes? Solutions: Revision Towers TimeWheel, Infobug The Evolution Matrix © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Revision Tower Taylor, Munro, Revision Towers, Vissoft02
OORPT — Software Visualization Revision Tower Taylor, Munro, Revision Towers, Vissoft02 Past is at the bottom Middle section represents release Side section represents history Here .c files compared with .h files Authors are color typed File changed often: lots of rectangle inside a release period © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Revision Tower (II) Simple Entire file File based
OORPT — Software Visualization Revision Tower (II) Simple Entire file File based Few information revealed © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Definitions Glyph: A symbol, such as a stylized figure or arrow on a public sign, that imparts information nonverbally. © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

How can we represent time?
OORPT — Software Visualization How can we represent time? Animation? Good for easily perceived outliers Time Series graph? Good for comparing trends Timewheel © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

TimeWheel (1) Displays trends for a number of attributes at a time
OORPT — Software Visualization TimeWheel (1) Displays trends for a number of attributes at a time Maintain “Objectedness” through Gestalt principals © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Timewheel (II) Multiple time series ordered in a circle
OORPT — Software Visualization Timewheel (II) Multiple time series ordered in a circle Data attributes are color coded Easy recognition of two trends Increasing trend Tapering trend Helps to examine different trends within one object © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Time Series Problems In row In circle Example More time to spot them
OORPT — Software Visualization Time Series Problems In row More time to spot them Less local patterns In circle Weakens reading order implications Rotation invariant Example © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

InfoBug © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz
OORPT — Software Visualization InfoBug © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Infobug Look like an insect
OORPT — Software Visualization Infobug Look like an insect Show many properties while still maintaining “objectedness” Certain patterns preattentively pop out Interactive Represent four classes of software data Code lines, errors (wings) © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

4 Classes of Software Data
OORPT — Software Visualization 4 Classes of Software Data Head (Type of code) Wings (Lines of codes, errors) Body (bar - file changes, Spots - number of subsystems) Tails (added, removed lines) © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Infobug Wings Time series Lines of code vs. Lines of Errors
OORPT — Software Visualization Infobug Wings Time series Lines of code vs. Lines of Errors Red line is current selection (update other aspects) Code quality © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Infobug Head Different types of code Type is color coded
OORPT — Software Visualization Infobug Head Different types of code Type is color coded Relative size is shown by antenna length © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Infobug Tail Triangle shaped Number of deleted lines (height)
OORPT — Software Visualization Infobug Tail Triangle shaped Number of deleted lines (height) Number of added lines (width) Errors in red New function in green © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Infobug Body Bar in the middle — Number of changed files
OORPT — Software Visualization Infobug Body Bar in the middle — Number of changed files Spots — Number of children © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Interface Colors: file types Time scale
OORPT — Software Visualization Interface Colors: file types Time scale © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Evaluation Pros: Cons: Large datasets in small space
OORPT — Software Visualization Evaluation Pros: Large datasets in small space Entities as objects Easy to recognize patterns Trends identification Easy to compare and analyze Interactive Cons: Learning (but is there something we should not learn?) Main focus on Error/Loc ratio Could include more information © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

The Evolution Matrix Growth Stabilisation Removed Classes Last Version
OORPT — Software Visualization The Evolution Matrix Last Version First Version Major Leap Removed Classes TIME (Versions) Growth Stabilisation Added Classes © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Dayfly & Persistent Dayflies: Exists during only one or two versions. Perhaps an idea which was tried out and then dropped. Persistent: Has the same lifespan as the whole system. Part of the original design. Perhaps holy dead code which no one dares to remove. © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Visualizing Classes Using Metrics
OORPT — Software Visualization Visualizing Classes Using Metrics Object-Oriented Programming is about “state” and “behavior”: State is encoded using attributes Behavior is encoded with methods We visualize classes as rectangles using for width and height the following metrics: NOM (number of methods) NOA (number of attributes) The Classes can be categorized according to their “personal evolution” and to their “system evolution”: Pulsar, Supernova, Red Giant, Stagnant, DayflyPersistent Foo Bar © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Pulsar & Supernova Pulsar: Repeated Modifications make it grow and shrink. System Hotspot: Every System Version requires changes. Supernova: Sudden increase in size. Possible Reasons: Massive shift of functionality towards a class. Data holder class for which it is easy to grow. Sleeper: Developers knew exactly what to fill in. © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

White Dwarf, Red Giant, Idle
OORPT — Software Visualization White Dwarf, Red Giant, Idle White Dwarf: Lost the functionality it had and now trundles along without real meaning. Possibly dead code. Red Giant: A permanent god class which is always very large. Idle: Keeps size over several versions. Possibly dead code, possibly good code. © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Example: MooseFinder (38 Versions)
OORPT — Software Visualization Example: MooseFinder (38 Versions) © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Evaluation Easy to draw
OORPT — Software Visualization Evaluation Easy to draw Scalable via other grouping entities (packages) Good overview of history Limit of the metaphor… © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Do It Yourself Considerations
OORPT — Software Visualization Do It Yourself Considerations A decent graph layout can be a hard task... Algorithmic aspects may be important Efficient space use (physical limits of a screen) Colours are nice, but... there are no conventions! Trade-off between usefulness and complexity Keeping a focus is hard: Beautiful graphs are not always meaningful Where should we look? What should we look for? Which approach be reproduced by reengineers in work context and provides useful information? © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Conclusions SV is very useful when used correctly
OORPT — Software Visualization Conclusions SV is very useful when used correctly An integrated approach is needed, just having nice pictures is not enough In general: only people that know what they see can react on that: SV is for expert/advanced developers The future of software development is coming…and SV is part of it © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Lessons Learned Visualization is not just smoke and mirrors!
OORPT — Software Visualization Lessons Learned Visualization is not just smoke and mirrors! Complexity reduction, abstraction But it should be adapted to your goal (first contact, deep understanding), time (2 days - a month), size (a complete system or 3 classes) Minimize tool support if you are not familiar Simple approaches give 80%, the last 20% are hard to get © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

Your fair use and other rights are in no way affected by the above.
OORPT — Software Visualization License Attribution-ShareAlike 2.5 You are free: to copy, distribute, display, and perform the work to make derivative works to make commercial use of the work Under the following conditions: Attribution. You must attribute the work in the manner specified by the author or licensor. Share Alike. If you alter, transform, or build upon this work, you may distribute the resulting work only under a license identical to this one. For any reuse or distribution, you must make clear to others the license terms of this work. Any of these conditions can be waived if you get permission from the copyright holder. Your fair use and other rights are in no way affected by the above. © Stéphane Ducasse, Serge Demeyer, Oscar Nierstrasz

OORPT Object-Oriented Reengineering Patterns and Techniques

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OORPT Object-Oriented Reengineering Patterns and Techniques

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