Interface Seams
| Notes on Guindon |
April, 1999 |
| Notes on Guindon - "Designing the Design
Process"
Reviewer: William C. Wake, 9-16-94.
"Designing the Design Process: Exploiting Opportunistic
Thoughts", by Raymonde Guindon. Human-Computer Interaction,
1990, V5, pp. 305-344.
From Guindon's abstract:
"This study shows that top-down decomposition is problematic in
the early stages of design. Instead, an opportunistic decomposition
is better suited to handle the ill-structuredness of design
problems. ..."
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Abstract
- Top-down decomposition is problematic...
- Opportunistic decomposition is better suited...
- ... interleaving decisions at various levels...
- Verbal protocols...
- ... causes of opportunistic design
- A top-down decomposition... a special case
- Two cognitive models
- Implications
This Study Shows...
... the design process frequently deviates from a top-down
approach. But more importantly, it shows that these deviations are
not noise or special cases resulting from bad design habits or
performance breakdowns. Rather, they are a natural consequence of
the ill-structuredness of problems in the early stages of
design.
Deviations Occur When...
- Artifact is new to designer
- Integration of multiple knowledge sources
- Subproblems appeared
-
- critical,
- very different, or
- had an immediately known solution
Early Design
Specification of Requirements
- Informal
- Incomplete
- Ambiguous
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==> transforms to ==> |
High-level design
- Notation
- Refined requirements
- Software subsystems
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Design Problems are Ill-Structured
Simon (1973)
- Incomplete and ambigous specification of goals
- No pre-determined solution path
- The need for integration of multiple knowledge domains
Evidence for Prescriptive Design Models
- Jeffries et al '81: Two novices, four experts. Showed some
deviations form top-down design.
- Adelson and Soloway, '84, '85: Two novices, three experts.
Expert designs were systematic and unbalanced. Designs are
unbalanced when a mental model exists.
- Kant and Newell '84: Two PhD students. Problem-solving and
refinement.
- Parnas and Clements, Mills, Dijkstra, ...
Design Decomposition is Opportunistic
- Data-driven rules (not goal-driven)
- Opportunistic planning
-
- "Blackboard architecture"
- Take immediate advantage of discoveries
- Occasional performance breakdowns (e.g., memory
limitations)
Categorization Rules
| |
Unbalanced |
Balanced |
| Solution Development |
- Drifting
- Immediate recognitions of solution in other part of
problem
- Simulating scenarios
- Immediate solution for inferred requirement
|
- Design schema
- Design method or notation
- Solution schema
- Design heuristics
|
| Solution Evaluation |
If solution unbalanced |
- Test cases
- Systematic requirements review
- If solution balanced
|
| Requirements |
Inferences and additions |
- Systematic strategy
- Simulating scenarios
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The Lift Problem
N elevators for M floors
"Ecologically valid"
Method
- 8 protocols ==> 3 in depth ==> 2 reported
- "Prototypical" style
- Styles guided by
-
- Software design method,
- Past experience, or
- Programming paradigm
Analysis
- Videotape and transcript reviewed by 4 researchers:
brainstorming
- Prompted review session with participant
- Iterative development of analysis scheme (Templates: activity
and level)
- In-depth analysis
Causes of Opportunistic Design Decomposition
- Sudden discovery of unbalanced partial solutions
-
- Partial solution from rest of problem
- Simulation bug
- Low-level solution before decomposition
- Data-triggered rules
- Immediate solution development for new requirements (60% of new
requirements solved immediately)
- Drifting
-
- Follow train of thought; little cognitive cost
- Partial solutions may provide critical insights
- Solution development by problem domain scenarios: Triggered
recognition of unbalanced solution or new requirements
Differences between Designers
- Specialized design schema
- Designer 2 schema allowed straightforward decomposition
- Frequent and varied deviations
Different Psychological Models (Caricatured)
- Anderson: top-down design process with hierarchical goal
structures
- Hayes-Roth: Flexible and easily re-organizable goal structures
and online planning.
- "...very difficult to demonstrate empirically the validity of
one psychological model against another."
- Could embody both, and compare.
Implications for Training, Methods, and Environments
- "...until the proper design decomposition... the design process
should be opportunistic"
- Don't force a strict order of activites
- Allow rapid shifts between tools for objects and their
representations
- Allow easy navigation between objects, but support an
agenda
More Implications
- Representations should have smooth progression from informal to
formal
- Easy editing and reorganization
- Requirements traceability
- Representation of interim and partial design objects
Critique
- 4 studies, 15 subjects
- Strict top-down decomposition is a strawman
- "Drifting" == end-to-end tracing
- Hidden agenda for environments (?)
- Environment sounds like Fischer's CPS
- Wrong question?
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