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From chaos to goals: Requirements change can be predicted. Johan F. Hoorn Vrije Universiteit Computer Science Information Management and Software Engineering [email protected] Contents. Status Problem Analysis Model Method Case Results Conclusions. Johan F. Hoorn, 2004.

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Presentation Transcript
slide1
From chaos to goals:

Requirements change

can be predicted

Johan F. Hoorn

Vrije Universiteit

Computer Science

Information Management

and Software Engineering

[email protected]

slide2
Contents
  • Status
  • Problem
  • Analysis
  • Model
  • Method
  • Case
  • Results
  • Conclusions

Johan F. Hoorn, 2004

slide3
Status
  • Sept. 1, 2001 – Aug. 31, 2005
  • Supervisors: Gerrit van der Veer

Hans van Vliet

  • Four international publications
  • Industries:
  • Human-Machine Interaction

Johan F. Hoorn, 2004

slide4
Business model 1

Traditional office

Mainframe with

thin clients

Business model 2

Flexible workplace

Laptops with

bluetooth

A change request during development is

extremely costly and frustrating

Problem

  • Requirements change

Johan F. Hoorn, 2004

slide5
From chaos to goals:

Nobody knows how

requirements change

can be predicted

slide6
Analysis
  • Where do change requests come from?

Business model 1

Business model 2

  • Change in business sub goals

- Main goals: Profit

- Sub goals: Cost-effectiveness, efficiency

  • How come business goals change?
  • Change in sub goals (strategic management)

- Main goals: Earn my living

- Sub goals: Fire employees (not me),

improve IT to guarantee same output

Johan F. Hoorn, 2004

slide7
Model
  • Change of Stakeholder Requirements

(CoStaR) (Hoorn & Van der Veer, 2003a; 2003b)

One of the hypotheses:

Valence

Requirements

Goals

Stakeholder evaluation:

Does a system feature support my goals?

Does a system feature obstruct my goals?

(after Frijda, 1986)

Johan F. Hoorn, 2004

slide8
Method
  • REquest, the Requirements Engineering

questionnaire

General approach: Items that combine

- a must or a won’t requirement, with

- support or obstruction of

- a goal to achieve with the system or

a goal state to avoid,

scored for agreement on a 6-point Likert scale

Johan F. Hoorn, 2004

slide9
Case
  • Eighteen managers of a logistic

warehouse management system

must/won’t support/obstruct goal approach/avoid

E-mail ordering increases efficiency

E-mail ordering decreases efficiency

E-mail ordering increases inefficiency

E-mail ordering decreases inefficiency

Paper ordering forms increase efficiency

Paper ordering forms decrease efficiency

Paper ordering forms increase inefficiency

Paper ordering forms decrease inefficiency

Example items

Johan F. Hoorn, 2004

slide10
Results (1)
  • Original hypothesis:

Valence

Requirements

Goals

- Indeed, goals, valence, and requirements

all evoked significant effects on agreement

to requirements statements

Johan F. Hoorn, 2004

slide11
Results (2)

↑ Grand mean agreement

5

4

3.67

(1.14)

2.78

3

2.5

2.41

(1.04)

2.19

(.96)

1.8

(.98)

2

(1.44)

(1.09)

1

0

Goals (to

Goals (to

Requirements

Requirements

Valence

Valence

approach)

avoid)

(must have)

(won't have)

(support)

(obstruct)

MANOVA (must vs won’t) * (support vs obstruct) * (goal approach vs avoid)

Pillai’s Trace = .51, F(2,16)= 8.40, p= .003, ηp2= .51

Johan F. Hoorn, 2004

slide12
- Goal-driven RE models should be unipolar

Goals

(approach)

Requirements

(must have)

Valence

(support)

Valence

(obstruct)

Goals

(avoid)

Requirements

(won’t have)

Results (3)

  • Original hypothesis:

Valence

Requirements

Goals

  • Bipolar conception does not hold
  • Regression: R2= .03, R2adj= -.03, F(1,16)= .47, p= .504

Johan F. Hoorn, 2004

slide13
Results (4)
  • However, original structure should be

completely revised

Goals

Requirements

(to approach)

(won’t have)

Valence

(support)

Valence

(obstruct)

Requirements

Goals

(must have)

(to avoid)

Johan F. Hoorn, 2004

slide14
no predictive power

Yet, valence does have influence.

Requirements vs. goals: Parameter coefficient= -.56, t= -4.04, p= .001, ηp2= .49

no predictive power

70%!!

90%!!

Results (5)

R2= .79, R2adj= .70

F(5,12)= 9.01, p= .001

Goals

(approach)

Requirements

(won’t have)

Valence

(support)

Valence is a moderator!

Valence

(support)

Goals

(avoid)

Requirements

(must have)

R2= .93, R2adj= .90

F(5,12)= 30.30, p= .000

Johan F. Hoorn, 2004

slide15
Conclusions (1)
  • RE should be oriented to goals
  • Requirements validation should be done
  • with structured questionnaires
  • (e.g., REquest)
  • Goals to achieve predict won’t requirements
  • Goal states to avoid predict must requirems
  • Like the weather, valence does not predict
  • mood (i.e. agreement) but it does influence it

Johan F. Hoorn, 2004

slide16
Conclusions (2)
  • Most important RE questions are:
  • What are the things you want to achieve

with the system?

  • What should the system NOT have to

support that?

  • What are the things you want to avoid
  • with the system?
  • What should the system offer to support

that?

Johan F. Hoorn, 2004

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