Identifying Needs and Establishing Requirements Sonal Kulkarni Veeresh Kinagi Abilash Kittanna Jamare Lane Chapter 7

Objective Describe different types of requirements. Identifying different types of requirements through simple description. Data gathering Techniques Developing a “scenario”, “use case” and an “ essential use case” Performing hierarchical task analysis on a simple description

Aim of this activity Identifying needs- Understanding the users, their work and the context of their work as much as possible. So that, the system under development can support them in achieving their goals. Establishing stable requirements- From the needs identified in the previous step, this step aims at producing stable requirements which serve as sound basis for carrying out design activity.

How do we achieve this? Beginning – We have a lot to find out and clarify Middle- collecting data interpret data extract requirements from the data End- We have stable requirements. Broadly speaking, data gathering is a sequential activity but it gets messier than this and the activities influence one another as process iterates.

Identifying needs and establishing requirements Is an Iterative activity It does not take a set number of weeks or months to finish In practice, requirements evolve as stakeholders interact with design.

Why bother? The importance of getting it right A research conducted in January 2000(Taylor,2000), investigated the causes of IT project failures. The research admits that there is no single cause for project failure but requirements issues figured highly in the findings. The research also involved detailed questioning of 38 IT professionals in UK. When asked about the causes of failure, they mentioned – Requirements definition – Unclear objectives and requirements.

The implication for both producer and customer are Anxiety and frustration Loss in revenue Loss of customer confidence These issues can be addressed by having user centered approach. This implies that the users’ voices and needs should be clearly heard and taken into account.

Why establish Requirements? The activity of understanding what a product should do has been given various labels Requirements gathering Requirements capture Requirements elicitation Requirements analysis Requirements engineering

Requirement gathering and Requirement capture assume that the requirements are out there and the we simply need to pick them up. Requirements elicitation implies that the users know the requirements. However, we cannot expect the user to know all the requirements. Requirement analysis involves investigating and analyzing the initial set of requirements that have been gathered, captured or elicited.

Requirements engineering recognises that developing a set of requirements is an iterative process of evolution and negotiation and one that needs to be carefully managed and controlled. It is better than the others. The term establishing requirements means that requirements arise from data gathering and interpretation activities and have been established from a sound understanding of users’ needs.

What are Requirements? A requirement is a statement about an intended product that specifies what it should do or how it should perform. One of the aims of the requirements activity is to make the requirements as specific, unambiguous, and clear as possible.

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Types of requirements Functional – Describes what the system should do – Main focus of requirements activities Non-Functional – Describes what constraints there are on the system and its development

Types of non-functional requirements(1) Data – What kinds of data need to be stored? – How will they be stored (e.g. database)? Environmental/ Context of Use – What circumstances will the product be expected to operate? Physical? Social? Organizational? Technical?

Types of non-functional requirements(2) User – Characteristics of the intended user – User Profiles Usability – Capture the Usability Goals Effectiveness, Safety, Learnability etc.

Variety of requirements that need to be captured. (not exhaustive) Functional requirements capture what the product should do. Data requirements capture the type, volatility, sizelamount, persistence, accuracy, and value of the amounts of the required data. Environmental requirements or context of use refer to the circumstances in which the interactive product will be expected to operate. User requirements capture the characteristics of the intended user group.

Data Gathering Data gathering is an important part of the requirements activity and also of evaluation The purpose of data gathering is to collect sufficient, relevant, and appropriate data so that a set of stable requirements can be produced

Questionnaires: A series of questions designed to elicit specific information Often used in conjunction with other techniques Can give quantitative or qualitative data Good for answering specific questions and can reach many people with low resource Interviews: talking to people and asking someone a set of question Structured, unstructured or semi-structured Good for exploring issues, but are time consuming and may be infeasible to visit everyone

Focus groups or workshops : −Group interviews −Good at collecting multiple viewpoints highlighting areas of consensus and conflict Naturalistic observation: − Spend time with stakeholders in their day-to-day tasks, observing work as it happens − Gain insights into stakeholders’ tasks − Good for understanding the nature and context of the tasks − But, it requires time and commitment from a member of the design team, and it can result in a huge amount of data

Studying documentation: Procedures and rules that often written down in manuals are good source of data about the steps involved in an activity Good for understanding legislation, and getting background information and is used with other techniques More information about Data gathering techniques in HCI book, P211 – P215

Data gathering techniques used to analyze COOL The Merchants of Cool Primary objective is to understand the behaviors of teen spending habits They spend their days sifting through reams of market research data. They conduct endless surveys and focus groups. They comb the streets, the schools, and the malls, hot on the trail of the "next big thing" that will snare the attention of their prey--a market segment worth an estimated $150 billion a year. They are the merchants of cool: creators and sellers of popular culture who have made teenagers the hottest consumer demographic in America qwLd7LTcDg&q=focus+group+cool&hl=en&view=2# Source : video.google.com and

Exercise Suggest one key functional, data, environmental, user and usability requirement for the following scenario: A system that is used for a university’s self service cafeteria – Data? – Environmental Factors? – Users? – Usability?

Choosing between techniques Data gathering techniques differ in two respects: 1.Amount of time, level of detail and risk associated with the findings 2.Knowledge the analyst requires about basic cognitive process Tasks can be classified along three scales: 1.Sequential steps or overlapping series of subtasks? 2.High information with complex displays or low information with simple signals? 3.Task for a layman or a skilled practitioner?

Some basic guidelines Focus on identifying the stakeholders’ needs Involve all the stakeholder groups Involve more than one representative from each stakeholder group Use a combination of data gathering techniques

Some basic guidelines Support the process with props such as prototypes and task descriptions Run a pilot session You will need to compromise on the data you collect and the analysis to be done, but before sensible compromises, you need to know what you’d really like Consider carefully how to record the data, video recording, audio recording and note taking

Discussion we should choose appropriate data gathering techniques in the different situations. What kind of data gathering should be appropriate in this situation and How you might use these techniques. - Election exit polls

Data Interpretation and Analysis Start soon after data gathering session Initial interpretation before deeper analysis Different approaches emphasize different elements e.g. class diagrams for object- oriented systems, entity- relationship diagrams for data intensive systems

Observation Observation is a useful data gathering technique at any stage during product development.

Field Observation Helps fill in details Can be complicated

Field Observation Frameworks are used to structure and focus their observation. Colin Robson’s Framework – Space, actors, activities, objects, acts, events, time, goals, and feelings

Field Observation Degree of participation – Passive Observer – will not take part in the study environment at all – Participant Observer – attempts to become a full member of the group being studied

Field Observation Tips for successful field observations – Frameworks – Participation – Records – Acceptance

Ethnography In an ethnography study, the observer adopts a participant observer role as much as possible.

Data Collection Interaction Logs Think-aloud – Direct Diaries – Indirect

Data Collection TechniqueGood forKind of dataAdvantagesDisadvantages InterviewsExploring issues Some quantitative but mostly qualitative Interviewer has control Time- consuming Focus groupsCollecting multiple viewpoints Some quantitative but mostly qualitative Consensus or conflict. Some members may be dominant QuestionnairesAnswering specific questions Quantitative and qualitative Can reach many people with little effort Design is crucial Direct observation in field Understanding context of user activity Mostly qualitative Very insightfulTime- consuming. Lots of data

Data Collection TechniqueGood forKind of dataAdvantagesDisadvantages Direct observation in a controlled environment Learning about procedures, regulations and standards Quantitative and qualitative Can focus on the details of a task without interruption Results may have limited use in the normal environment because the conditions were artificial Indirect observation Observing users without disturbing their activity; data captured automatically Quantitative (logging) and qualitative (diary) User doesn’t get distracted by the data gathering A large amount of quantitative data needs tool support to analyze; participants’ memories may exaggerate

Observation Factors determining the type of observation are – The participants – Technique – Resources