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. ..."

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

==> transforms to ==>

High-level design

Notation
Refined requirements
Software subsystems

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

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?