Software visualization aims to provide common views for communication, help discover phenomena through visual patterns, and aid in comprehending software aspects by navigating views. However, choosing the best visualization technique is difficult, with a chance of only 2 in a million given the many options and required mappings. To address this, the document proposes systematically reviewing literature to classify visualization tools, techniques, data types, user needs, and contexts to enable more suitable automatic software visualizations tailored to specific developer tasks.

1. Software
Visualization
Leonel Merino
merino @ inf.unibe.ch
Software Composition Group
University of Bern

2. what?
Communication
providing a common view for presentation or
discussion in a tea
Discoverability
finding phenomena through visual patterns
Comprehension
navigating views of aspect of software

3. why?
3
Layout
Shape
Granularity
Edges
Color Size
TransparencyNormalization
Technique
Aggregation
Chance of choosing the best
visualization?
e.g 100 techniques x 7
mappings (avg.)
2 in a million

4. how? automatic visualization
Systematic Literature Review
classify tools & techniques
classify user needs
Data
data types
relationships
statistics
…
User (needs)
assessing a refactoring
understanding a legacy
system
…
Context
user behavior
quality metrics
…

5. how? pattern recognition
PolyCell
Explora
Force-directed
layout clustering
Hasse
diagram

6. Towards suitable automatic software
visualization
Leonel Merino Jan 2016 merino@inf.unibe.ch
Communication
Comprehension
Discoverability
what? why? chance of
choosing the best
visualization?
2 in a million
how?
Automatic Visualization Pattern Recognition
Although many visualizations seem useful for general questions, choosing
a suitable technique depends not only on the data structure but also on
the user needs, because most developer tasks have a specific context.
Why software visualization is rarely used by developers in a daily basis?
Data | User | Context