Establishing Requirements

• The importance of requirements
• Data gathering for requirements
• Techniques in data gathering
• Data gathering guidelines
• Establishing requirements
• Different kinds of requirements
• Data interpretation and presentation Based on material from:

1. Preece, J., Rogers, Y. and Sharp, H. (2015). Interaction Design Beyond Human-Computer Interaction. 4th Ed. Sussex: John Wiley & Sons Ltd.

• Explain and describe the process of data gathering (how to plan and run a successful data gathering program)

• What
  • Two aims:

1. Understand as much as possible about users, task, context
2. Produce a stable set of requirements

• How:
  • Data gathering activities
  • Data analysis activities
  • Expression as ‘requirements’
  • All of this is iterative

1. Setting goals
   • Decide how to analyze data once collected
2. Identifying participants
   • Decide who to gather data from
3. Relationship with participants
   • Clear and professional
   • Informed consent when appropriate
4. Triangulation
   • Look at data from more than one perspective
5. Pilot studies
   • Small trial of main study

1. Interviews:

• Props, e.g. sample scenarios of use, prototypes, can be used in interviews
• Good for exploring issues
• But are time consuming and may be infeasible to visit everyone

Focus groups:

• Group interviews
• Good at gaining a consensus view and/or highlighting areas of conflict
• But can be dominated by individuals

• Unstructured/open-ended - are not directed by a script. Rich but not replicable
• Structured - are tightly scripted, often like a questionnaire. Replicable but may lack richness
• Semi-structured - guided by a script but interesting issues can be explored in more depth. Can provide a good balance between richness and replicability
• Group interviews – small group guided by a facilitator

• Two types:
  • ‘closed questions’ have a predetermined answer format, e.g., ‘yes’ or ‘no’
  • ‘open questions’ do not have a predetermined format
• Closed questions are easier to analyze
  • Given similar questions to all participants (standardized)
  • Short and worded exactly the same to all participants
  • Consists pre-determined options
  • Eg: Which of the following web sites do you visit most frequently: amazon.com, MPH.com,?
  • How often do you visit this web site: every day, once a week?

Open questions eg:

Which music websites do you visit most

frequently?<answer......stresses on particular web site)

Why?<Answer> Tell me more......

Avoid:

• Long questions
• Compound sentences - split them into two
• Jargon and language that the interviewee may not understand
• Leading questions that make assumptions e.g., why do you like ...?
• Unconscious biases e.g., gender stereotypes

• Introduction – introduce yourself, explain the goals of the interview, reassure about the ethical issues, ask to record, present any informed consent form.
• Warm-up – make first questions easy and non-threatening.
• Main body – present questions in a logical order
• A cool-off period – include a few easy questions to defuse tension at the end
• Closure – thank interviewee, signal the end, e.g, switch recorder off.

• The impact of a question can be influenced by question order.
• Do you need different versions of the questionnaire for different populations?
• Provide clear instructions on how to complete the questionnaire.
• Strike a balance between using white space and keeping the questionnaire compact.
• Decide on whether phrases will all be positive, all negative or mixed.

• Make sure purpose of study is clear
• Promise anonymity
• Ensure questionnaire is well designed
• Offer a short version for those who do not have time to complete a long questionnaire
• If mailed, include a stamped addressed envelope
• Follow-up with emails, phone calls, letters
• Provide an incentive
• 40% response rate is high, 20% is often acceptable

• Responses are usually received quickly
• No copying and postage costs
• Data can be collected in database for analysis
• Time required for data analysis is reduced
• Errors can be corrected easily

• Sampling is problematic if population size is unknown
• Preventing individuals from responding more than once
• Individuals have also been known to change questions in email questionnaires

1. Researching similar products:

• Good for prompting requirements

1. Observation:

• the action or process of observing something in order to gain information

• Co-operation of people being observed is required
• Informants are useful
• Data analysis is continuous
• Interpretivist technique
• Questions get refined as understanding grows
• Reports usually contain examples

• Virtual, Online, Netnography
• Online and offline activity
• Interaction online differs from face-to-face
• Virtual worlds have a persistence that physical worlds do not have
• Ethical considerations and presentation issues are different

• Join www.ActiveWorlds.com or go to another chat room of your choice
• ActiveWorlds is a 3-D chatroom environment in which you can visit different environments and chat with the people that you meet there
• To use ActiveWorlds you will need to check the instructions that they provide and download the appropriate software to run on your computer

• Select one of the worlds to visit and choose an avatar (a graphical personification) to represent you.
• Spend one to two hours doing an ethnographic study.
• Use one of the frameworks discussed in the previous slides to guide you and write a one or two page report about your study.
• Also notice and report on any usability issues you encounter and on user experiences in this environment.

• The observation main data gathering methods: interviews, questionnaires,
• Five key issues of data gathering: goals, choosing participants, triangulation, participant relationship, pilot
• Interviews may be structured, semi-structured or unstructured
• Questionnaires may be on paper, online or telephone
• Observation may be direct or indirect, in the field or in controlled setting
• Techniques can be combined depending on study focus, participants, nature of technique and available resources

• What do users want? What do users ‘need’?
  • Requirements need clarification, refinement, completion, re-scoping
  • Input: requirements document (maybe)
  • Output: stable requirements
• Why ‘establish’?
  • Requirements arise from understanding users’ needs
  • Requirements can be justified & related to data

• Functional:
  • What the system should do
  • Historically the main focus of requirements activities
• (Non-functional: memory size, response time...)
• Data:
  • What kinds of data need to be stored?
  • How will they be stored (e.g. database)?

• What factors (environmental, user, usability) would affect the following systems?
• Self-service filling and payment system for a petrol (gas) station
• On-board ship data analysis system for geologists searching for oil
• Fashion clothes website

Scenarios

an informal narrative story, simple, ‘natural’, personal, not generalisable

Use cases

assume interaction with a system assume detailed understanding of the interaction

Essential use cases

abstract away from the details does not have the same assumptions as use cases

“The Thomson family enjoy outdoor activities and want to try their hand at sailing this year. There are four family members: Sky (10 years old), Eamonn (15 years old), Claire (35), and Will (40). One evening after dinner they decide to start exploring the possibilities. They all gather around the travel organizer and enter their initial set of requirements – a sailing trip for four novices in the Mediterranean. The console is designed so that all members of the family can interact easily and comfortably with it. The system’s initial suggestion is a flotilla, where several crews (with various levels of experience) sail together on separate boats. Sky and Eamonn aren’t very happy at the idea of going on vacation with a group of other people, even though the Thomsons would have their own boat. The travel organizer shows them descriptions of flotillas from other children their ages and they are all very positive, so eventually, everyone agrees to explore flotilla opportunities. Will confirms this recommendation and asks for detailed options. As it’s getting late, he asks for the details to be printed so everyone can consider them tomorrow. The travel organizer prints out a summary of the different options available.”

Some alternative courses:

1. If the country name is invalid:

6.1 The system displays an error message.

6.2 The system returns to step 3.

1. If the nationality is invalid:

8.1 The system displays an error message.

8.2 The system returns to step 6.

1. If no information about visa requirements is found:

9.1 The system displays a suitable message.

9.2 The system returns to step 1.

• Task descriptions are often used to envision new systems or devices
• Task analysis is used mainly to investigate an existing situation
• It is important not to focus on superficial activities
  • What are people trying to achieve?
  • Why are they trying to achieve it?
  • How are they going about it?
• Many techniques, the most popular is Hierarchical Task Analysis (HTA)

• Involves breaking a task down into subtasks, then sub-sub-tasks and so on. These are grouped as plans which specify how the tasks might be performed in practice
• HTA focuses on physical and observable actions, and includes looking at actions not related to software or an interaction device
• Start with a user goal which is examined and the main tasks for achieving it are identified
• Tasks are sub-divided into sub-tasks

1. In order to buy a DVD
2. locate DVD
3. add DVD to shopping basket
4. enter payment details
5. complete address
6. confirm order plan 0: If regular user do 1-2-5. If new user do 1-2-3-4-5.

• Getting requirements right is crucial
• There are different kinds of requirement, each is significant for interaction design
• The most commonly-used techniques for data gathering are: questionnaires, interviews, focus groups, direct observation, studying documentation and researching similar products
• Scenarios, use cases and essential use cases can be used to articulate existing and envisioned work practices.
• Task analysis techniques such as HTA help to investigate existing systems and practices

• Quantitative data – expressed as numbers
• Qualitative data – difficult to measure sensibly as numbers, e.g. count number of words to measure dissatisfaction
• Quantitative analysis – numerical methods to ascertain size, magnitude, amount
• Qualitative analysis – expresses the nature of elements and is represented as themes, patterns, stories
• Be careful how you manipulate data and numbers!

• Recurring patterns or themes
  • Emergent from data, dependent on observation framework if used
• Categorizing data
  • Categorization scheme may be emergent or pre-specified
• Looking for critical incidents
  • Helps to focus in on key events

• Spreadsheet – simple to use, basic graphs
• Statistical packages, e.g. SPSS
• Qualitative data analysis tools
  • Categorization and theme-based analysis, e.g. N6
  • Quantitative analysis of text-based data
• CAQDAS Networking Project, based at the University of Surrey (http://caqdas.soc.surrey.ac.uk/)

• Basing data analysis around theoretical frameworks provides further insight
• Three such frameworks are:
  • Grounded Theory
  • Distributed Cognition
  • Activity Theory

• Aims to derive theory from systematic analysis of data
• Based on categorization approach (called here ‘coding’)
• Three levels of ‘coding’
  • Open: identify categories
  • Axial: flesh out and link to subcategories
  • Selective: form theoretical scheme
• Researchers are encouraged to draw on own theoretical backgrounds to inform analysis

www.id-book.com

• Explains human behaviour in terms of our practical activity in the world
• Provides a framework that focuses analysis around the concept of an ‘activity’ and helps to identify tensions between the different elements of the system
• Two key models: one outlines what constitutes an ‘activity’ ; one models the mediating role of artifacts

• Only make claims that your data can support
• The best way to present your findings depends on the audience, the purpose, and the data gathering and analysis undertaken
• Graphical representations (as discussed above) may be appropriate for presentation
• Other techniques are:
  • Rigorous notations, e.g. UML
  • Using stories, e.g. to create scenarios
  • Summarizing the findings