Nural network AI (courtesy google)

What is a Neural Network?
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.
The term neural network was traditionally used to refer to a network or circuit of biological neurons.[1] The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus the term has two distinct usages:
1. Biological neural networks are made up of real biological neurons that are connected or functionally related in the peripheral nervous system or the central nervous system. In the field of neuroscience, they are often identified as groups of neurons that perform a specific physiological function in laboratory analysis.
2. Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex: artificial neural network algorithms attempt to abstract this complexity and focus on what may hypothetically matter most from an information processing point of view. Good performance (e.g. as measured by good predictive ability, low generalization error), or performance mimicking animal or human error patterns, can then be used as one source of evidence towards supporting the hypothesis that the abstraction really captured something important from the point of view of information processing in the brain. Another incentive for these abstractions is to reduce the amount of computation required to simulate artificial neural networks, so as to allow one to experiment with larger networks and train them on larger data sets.
Why use neural networks?
Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyse. This expert can then be used to provide projections given new situations of interest and answer "what if" questions.
Other advantages include:
1. Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.
2. Self-Organisation: An ANN can create its own organisation or representation of the information it receives during learning time.
3. Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.
4. Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.

The brain, neural networks and computers
Neural networks, as used in artificial intelligence, have traditionally been viewed as simplified models of neural processing in the brain, even though the relation between this model and brain biological architecture is debated, as little is known about how the brain actually works
A subject of current research in theoretical neuroscience is the question surrounding the degree of complexity and the properties that individual neural elements should have to reproduce something resembling animal intelligence.
Historically, computers evolved from the von Neumann architecture, which is based on sequential processing and execution of explicit instructions. On the other hand, the origins of neural networks are based on efforts to model information processing in biological systems, which may rely largely on parallel processing as well as implicit instructions based on recognition of patterns of 'sensory' input from external sources. In other words, at its very heart a neural network is a complex statistical processor (as opposed to being tasked to sequentially process and execute).
Neural coding is concerned with how sensory and other information is represented in the brain by neurons. The main goal of studying neural coding is to characterize the relationship between the stimulus and the individual or ensemble neuronal responses and the relationship among electrical activity of the neurons in the ensemble.It is thought that neurons can encode both digital and analog information.
Human and Artificial Neurones - investigating the similarities
2.1 How the Human Brain Learns?
Much is still unknown about how the brain trains itself to process information, so theories abound. In the human brain, a typical neuron collects signals from others through a host of fine structures called dendrites. The neuron sends out spikes of electrical activity through a long, thin stand known as an axon, which splits into thousands of branches. At the end of each branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity from the axon into electrical effects that inhibit or excite activity in the connected neurones. When a neuron receives excitatory input that is sufficiently large compared with its inhibitory input, it sends a spike of electrical activity down its axon. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on another changes.


Components of a neuron The synapse


2.2 From Human Neurones to Artificial Neurones
We conduct these neural networks by first trying to deduce the essential features of neurones and their interconnections. We then typically program a computer to simulate these features. However because our knowledge of neurones is incomplete and our computing power is limited, our models are necessarily gross idealisations of real networks of neurones.
The neuron mode
Neural networks and artificial intelligence
A neural network (NN), in the case of artificial neurons called artificial neural network (ANN) or simulated neural network (SNN), is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionistic approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network.
In more practical terms neural networks are non-linear statistical data modeling or decision making tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data.
However, the paradigm of neural networks - i.e., implicit, not explicit , learning is stressed - seems more to correspond to some kind of natural intelligence than to the traditional symbol-based Artificial Intelligence, which would stress, instead, rule-based learning
Applications of natural and of artificial neural networks
The utility of artificial neural network models lies in the fact that they can be used to infer a function from observations and also to use it. Unsupervised neural networks can also be used to learn representations of the input that capture the salient characteristics of the input distribution, e.g., see the Boltzmann machine (1983), and more recently, deep learning algorithms, which can implicitly learn the distribution function of the observed data. Learning in neural networks is particularly useful in applications where the complexity of the data or task makes the design of such functions by hand impractical.
The tasks to which artificial neural networks are applied tend to fall within the following broad categories:
• Function approximation, or regression analysis, including time series prediction and modeling.
• Classification, including pattern and sequence recognition, novelty detection and sequential decision making.
• Data processing, including filtering, clustering, blind signal separation and compression.
Application areas of ANNs include system identification and control (vehicle control, process control), game-playing and decision making (backgammon, chess, racing), pattern recognition (radar systems, face identification, object recognition, etc.), sequence recognition (gesture, speech, handwritten text recognition), medical diagnosis, financial applications, data mining (or knowledge discovery in databases, "KDD"), visualization and e-mail spam filtering
Applications
There are abundant materials, tutorials, references and disparate list of demos on the net. This work attempts to compile a list of applications and demos - those that comes with video clips.
The applications featured here are:
• CoEvolution of Neural Networks for Control of Pursuit & Evasion
• Learning the Distribution of Object Trajectories for Event Recognition
• Radiosity for Virtual Reality Systems
• Autonomous Walker & Swimming Eel
• Robocup: Robot World Cup
• Using HMM's for Audio-to-Visual Conversion
• Artificial Life: Galapagos
• Speechreading (Lipreading)
• Detection and Tracking of Moving Targets
• Real-time Target Identification for Security Applications
• Facial Animation
• Behavioral Animation and Evolution of Behavior
• A Three Layer Feedforward Neural Network
• Artificial Life for Graphics, Animation, Multimedia, and Virtual Reality: Siggraph '95 Showcase
• Creatures: The World Most Advanced Artificial Life!
• Framsticks Artificial Life n
Not all types of artificial intelligence are able to learn new ways of performing tasks. Expert systems are a variety of AI that is very different from those that use machine learning techniques. Instead of learning through training or even unsupervised learning, an expert system applies logical arguments to information provided as part of a knowledge base. Examples of expert systems include loan application evaluation systems and technical support systems.
Expert System Definitionn
expert system is software that uses a knowledge base of human expertise for problem solving, or clarify uncertainties where normally one or more human experts would need to be consulted. Expert systems are most common in a specific problem domain, and are a traditional application and/or subfield of artificial intelligence (AI). A wide variety of methods can be used to simulate the performance of the expert; however, common to most or all are: 1) the creation of a knowledge base which uses some knowledge representation structure to capture the knowledge of the Subject Matter Expert (SME); 2) a process of gathering that knowledge from the SME and codifying it according to the structure, which is called knowledge engineering; and 3) once the system is developed, it is placed in the same real world problem solving situation as the human SME, typically as an aid to human workers or as a supplement to some information system. Expert systems may or may not have learning components.
Expert systems are a type of AI that uses shells. Shells are small programs that maintain information for a specific function within a larger program framework. Shells also interact with users to gather data, which is applied to the knowledge base using the logical set of rules set up by the system. By combining input with existing data and the logical rules, the expert system will arrive at a conclusion.
Examples of applications
Expert systems are designed to facilitate tasks in the fields of accounting, medicine, process control, financial service, production, human resources, among others. Typically, the problem area is complex enough that a more simple traditional algorithm cannot provide a proper solution. The foundation of a successful expert system depends on a series of technical procedures and development that may be designed by technicians and related experts. As such, expert systems do not typically provide a definitive answer, but provide probabilistic recommendations.
An example of the application of expert systems in the financial field is expert systems for mortgages. Loan departments are interested in expert systems for mortgages because of the growing cost of labour, which makes the handling and acceptance of relatively small loans less profitable. They also see a possibility for standardised, efficient handling of mortgage loan by applying expert systems, appreciating that for the acceptance of mortgages there are hard and fast rules which do not always exist with other types of loans. Another common application in the financial area for expert systems are in trading recommendations in various marketplaces. These markets involve numerous variables and human emotions which may be impossible to deterministically characterize, thus expert systems based on the rules of thumb from experts and simulation data are used. Expert system of this type can range from ones providing regional retail recommendations, like Wishabi, to ones used to assist monetary decisions by financial institutions and governments.
Another 1970s and 1980s application of expert systems, which we today would simply call AI, was in computer games. For example, the computer baseball games Earl Weaver Baseball and Tony La Russa Baseball each had highly detailed simulations of the game strategies of those two baseball managers. When a human played the game against the computer, the computer queried the Earl Weaver or Tony La Russa Expert System for a decision on what strategy to follow. Even those choices where some randomness was part of the natural system (such as when to throw a surprise pitch-out to try to trick a runner trying to steal a base) were decided based on probabilities supplied by Weaver or La Russa. Today we would simply say that "the game's AI provided the opposing manager's strategy."

Financial Expert Systems: Loans
In the finance realm, these systems are used to evaluate criteria based on pre-set logical guidelines and information. When creating the expert system, the programmer must rely on true experts to provide the rules for evaluation. For example, a consumer loan application made by an unemployed person with a poor credit score and no income would quickly be rejected. This allows banks and other lenders to quickly pre-screen credit offers without devoting man-hours to the evaluation process.
Technical Support Systems
A technical support system powered by shell programming can also be extremely effective for screening out simple solutions. Once the system is populated with common problems and solutions, users can benefit from the individual applications to each situation, while the tech department benefits from a reduced number of routine calls.
Expert System Applications in Real Life
Examples of expert system applications, such as loan evaluators and technical support systems, show how shell programming is very useful to businesses in real life. Although there are many benefits, it's worthwhile to keep in mind that this type of system is limited both by the quality of the data in the knowledge base, and the thoroughness of the rules by which the system operates. A low quality of information or poorly expressed logical arguments can result in the failure of the system to reach appropriate conclusions. In other words, expert systems are the embodiment of the phrase, "Garbage in, garbage out."
Artificial Intelligence Applications
Looking for more information about artificial intelligence applications in real life? Check out ways to use smart home appliances for a better quality of life: Combining artificial intelligence with home automation is great for anyone with mobility or dexterity issues, including the elderly and disabled. Also, disabled students are often at a disadvantage in the classroom. Voice recognition software improves communication, enables note-taking, and increases participation in classroom activities. Speaking of kids, robots are acting as therapy assistants to help parents and therapists in teaching special needs children with autism. Imagine - fuzzy logic, helping autistic kids!


(courtesy & source google)

SOFTware engineering(courtsey google)

Requirements Analysis Process: Requirements Elicitation, Analysis And Specification
Requirements Analysis is the process of understanding the customer needs and expectations from a proposed system or application and is a well-defined stage in the Software Development Life Cycle model.
Requirements are a description of how a system should behave or a description of system properties or attributes. It can alternatively be a statement of ‘what’ an application is expected to do.
Requirements analysis in systems engineering and software engineering, encompasses those tasks that go into determining the needs or conditions to meet for a new or altered product, taking account of the possibly conflicting requirements of the various stakeholders, such as beneficiaries or users.
Requirements analysis is critical to the success of a development project.[2] Requirements must be documented, actionable, measurable, testable, related to identified business needs or opportunities, and defined to a level of detail sufficient for system design. Requirements can be architectural, structural, behavioral, functional, and non-functional.
Conceptually, requirements analysis includes three types of activity:
• Eliciting requirements: the task of communicating with customers and users to determine what their requirements are. This is sometimes also called requirements gathering.
• Analyzing requirements: determining whether the stated requirements are unclear, incomplete, ambiguous, or contradictory, and then resolving these issues.
• Recording requirements: Requirements might be documented in various forms, such as natural-language documents, use cases, user stories, or process specifications.
Requirements analysis can be a long and arduous process during which many delicate psychological skills are involved. New systems change the environment and relationships between people, so it is important to identify all the stakeholders, take into account all their needs and ensure they understand the implications of the new systems. Analysts can employ several techniques to elicit the requirements from the customer. Historically, this has included such things as holding interviews, or holding focus groups (more aptly named in this context as requirements workshops) and creating requirements lists. More modern techniques include prototyping, and use cases. Where necessary, the analyst will employ a combination of these methods to establish the exact requirements of the stakeholders, so that a system that meets the business needs is produced.
Requirements engineering
Systematic requirements analysis is also known as requirements engineering.[3] It is sometimes referred to loosely by names such as requirements gathering, requirements capture, or requirements specification. The term requirements analysis can also be applied specifically to the analysis proper, as opposed to elicitation or documentation of the requirements, for instance. Requirements Engineering can be divided into discrete chronological steps:
• Requirements elicitation,
• Requirements analysis and negotiation,
• Requirements specification,
• System modeling,
• Requirements validation,
• Requirements management.
Requirement engineering according to Laplante (2007) is "a subdiscipline of systems engineering and software engineering that is concerned with determining the goals, functions, and constraints of hardware and software systems."[4] In some life cycle models, the requirement engineering process begins with a feasibility study activity, which leads to a feasibility report. If the feasibility study suggests that the product should be developed, then requirement analysis can begin. If requirement analysis precedes feasibility studies, which may foster outside the box thinking, then feasibility should be determined before requirements are finalized.
Stakeholder identification
See Stakeholder analysis for a discussion of business uses. Stakeholders (SH) are people or organizations (legal entities such as companies, standards bodies) which have a valid interest in the system. They may be affected by it either directly or indirectly. A major new emphasis in the 1990s was a focus on the identification of stakeholders. It is increasingly recognized that stakeholders are not limited to the organization employing the analyst. Other stakeholders will include:
• anyone who operates the system (normal and maintenance operators)
• anyone who benefits from the system (functional, political, financial and social beneficiaries)
• anyone involved in purchasing or procuring the system. In a mass-market product organization, product management, marketing and sometimes sales act as surrogate consumers (mass-market customers) to guide development of the product
• organizations which regulate aspects of the system (financial, safety, and other regulators)
• people or organizations opposed to the system (negative stakeholders; see also Misuse case)
• organizations responsible for systems which interface with the system under design
• those organizations who integrate horizontally with the organization for whom the analyst is designing the system
Stakeholder interviews
Stakeholder interviews are a common technique used in requirement analysis. Though they are generally idiosyncratic in nature and focused upon the perspectives and perceived needs of the stakeholder, very often without larger enterprise or system context, this perspective deficiency has the general advantage of obtaining a much richer understanding of the stakeholder's unique business processes, decision-relevant business rules, and perceived needs. Consequently this technique can serve as a means of obtaining the highly focused knowledge that is often not elicited in Joint Requirements Development sessions, where the stakeholder's attention is compelled to assume a more cross-functional context. Moreover, the in-person nature of the interviews provides a more relaxed environment where lines of thought may be explored at length.
Joint Requirements Development (JRD) Sessions
Requirements often have cross-functional implications that are unknown to individual stakeholders and often missed or incompletely defined during stakeholder interviews. These cross-functional implications can be elicited by conducting JRD sessions in a controlled environment, facilitated by a trained facilitator, wherein stakeholders participate in discussions to elicit requirements, analyze their details and uncover cross-functional implications. A dedicated scribe and Business Analyst should be present to document the discussion. Utilizing the skills of a trained facilitator to guide the discussion frees the Business Analyst to focus on the requirements definition process.
JRD Sessions are analogous to Joint Application Design Sessions. In the former, the sessions elicit requirements that guide design, whereas the latter elicit the specific design features to be implemented in satisfaction of elicited requirements.
Contract-style requirement lists
One traditional way of documenting requirements has been contract style requirement lists. In a complex system such requirements lists can run to hundreds of pages.
An appropriate metaphor would be an extremely long shopping list. Such lists are very much out of favour in modern analysis; as they have proved spectacularly unsuccessful at achieving their aims; but they are still seen to this day.
Strengths
• Provides a checklist of requirements.
• Provide a contract between the project sponsor(s) and developers.
• For a large system can provide a high level description.
] Weaknesses
• Such lists can run to hundreds of pages. It is virtually impossible to read such documents as a whole and have a coherent understanding of the system.
• Such requirements lists abstract all the requirements and so there is little context
• This abstraction makes it impossible to see how the requirements fit or work together.
• This abstraction makes it difficult to prioritize requirements properly; while a list does make it easy to prioritize each individual item, removing one item out of context can render an entire use case or business requirement useless.
• This abstraction increases the likelihood of misinterpreting the requirements; as more people read them, the number of (different) interpretations of the envisioned system increase.
• This abstraction means that it's extremely difficult to be sure that you have the majority of the requirements. Necessarily, these documents speak in generality; but the devil, as they say, is in the details.
• These lists create a false sense of mutual understanding between the stakeholders and developers.
• These contract style lists give the stakeholders a false sense of security that the developers must achieve certain things. However, due to the nature of these lists, they inevitably miss out crucial requirements which are identified later in the process. Developers can use these discovered requirements to renegotiate the terms and conditions in their favour.
• These requirements lists are no help in system design, since they do not lend themselves to application.
Alternative to requirement lists
As an alternative to the large, pre-defined requirement lists Agile Software Development uses User stories to define a requirement in every day language.
Measurable goals
Main article: Goal modeling
Best practices take the composed list of requirements merely as clues and repeatedly ask "why?" until the actual business purposes are discovered. Stakeholders and developers can then devise tests to measure what level of each goal has been achieved thus far. Such goals change more slowly than the long list of specific but unmeasured requirements. Once a small set of critical, measured goals has been established, rapid prototyping and short iterative development phases may proceed to deliver actual stakeholder value long before the project is half over.
Prototypes
In the mid-1980s, prototyping was seen as the best solution to the requirements analysis problem. Prototypes are Mockups of an application. Mockups allow users to visualize an application that hasn't yet been constructed. Prototypes help users get an idea of what the system will look like, and make it easier for users to make design decisions without waiting for the system to be built. Major improvements in communication between users and developers were often seen with the introduction of prototypes. Early views of applications led to fewer changes later and hence reduced overall costs considerably.
However, over the next decade, while proving a useful technique, prototyping did not solve the requirements problem:
• Managers, once they see a prototype, may have a hard time understanding that the finished design will not be produced for some time.
• Designers often feel compelled to use patched together prototype code in the real system, because they are afraid to 'waste time' starting again.
• Prototypes principally help with design decisions and user interface design. However, they can not tell you what the requirements originally were.
• Designers and end-users can focus too much on user interface design and too little on producing a system that serves the business process.
• Prototypes work well for user interfaces, screen layout and screen flow but are not so useful for batch or asynchronous processes which may involve complex database updates and/or calculations.
Prototypes can be flat diagrams (often referred to as wireframes) or working applications using synthesized functionality. Wireframes are made in a variety of graphic design documents, and often remove all color from the design (i.e. use a greyscale color palette) in instances where the final software is expected to have graphic design applied to it. This helps to prevent confusion over the final visual look and feel of the application.
Use cases
Main article: Use case
A use case is a technique for documenting the potential requirements of a new system or software change. Each use case provides one or more scenarios that convey how the system should interact with the end-user or another system to achieve a specific business goal. Use cases typically avoid technical jargon, preferring instead the language of the end-user or domain expert. Use cases are often co-authored by requirements engineers and stakeholders.
Use cases are deceptively simple tools for describing the behavior of software or systems. A use case contains a textual description of all of the ways which the intended users could work with the software or system. Use cases do not describe any internal workings of the system, nor do they explain how that system will be implemented. They simply show the steps that a user follows to perform a task. All the ways that users interact with a system can be described in this manner.
Software requirements specification
A software requirements specification (SRS) is a complete description of the behavior of the system to be developed. It includes a set of use cases that describe all of the interactions that the users will have with the software. Use cases are also known as functional requirements. In addition to use cases, the SRS also contains nonfunctional (or supplementary) requirements. Non-functional requirements are requirements which impose constraints on the design or implementation (such as performance requirements, quality standards, or design constraints).
Recommended approaches for the specification of software requirements are described by IEEE 830-1998. This standard describes possible structures, desirable contents, and qualities of a software requirements specification.
Types of Requirements
Customer Requirements
Statements of fact and assumptions that define the expectations of the system in terms of mission objectives, environment, constraints, and measures of effectiveness and suitability (MOE/MOS). The customers are those that perform the eight primary functions of systems engineering, with special emphasis on the operator as the key customer. Operational requirements will define the basic need and, at a minimum, answer the questions posed in the following listing:[1]
• Operational distribution or deployment: Where will the system be used?
• Mission profile or scenario: How will the system accomplish its mission objective?
• Performance and related parameters: What are the critical system parameters to accomplish the mission?
• Utilization environments: How are the various system components to be used?
• Effectiveness requirements: How effective or efficient must the system be in performing its mission?
• Operational life cycle: How long will the system be in use by the user?
• Environment: What environments will the system be expected to operate in an effective manner?
Architectural Requirements
Architectural requirements explain what has to be done by identifying the necessary system architecture of a system.
Structural Requirements
Structural requirements explain what has to be done by identifying the necessary structure of a system.
Behavioral Requirements
Behavioral requirements explain what has to be done by identifying the necessary behavior of a system.
Functional Requirements
Functional requirements explain what has to be done by identifying the necessary task, action or activity that must be accomplished. Functional requirements analysis will be used as the toplevel functions for functional analysis.[1]
Non-functional Requirements
Non-functional requirements are requirements that specify criteria that can be used to judge the operation of a system, rather than specific behaviors.
Performance Requirements
The extent to which a mission or function must be executed; generally measured in terms of quantity, quality, coverage, timeliness or readiness. During requirements analysis, performance (how well does it have to be done) requirements will be interactively developed across all identified functions based on system life cycle factors; and characterized in terms of the degree of certainty in their estimate, the degree of criticality to system success, and their relationship to other requirements.[1]
Design Requirements
The “build to,” “code to,” and “buy to” requirements for products and “how to execute” requirements for processes expressed in technical data packages and technical manuals.[1]
Derived Requirements
Requirements that are implied or transformed from higher-level requirement. For example, a requirement for long range or high speed may result in a design requirement for low weight.[1]
Allocated Requirements
A requirement that is established by dividing or otherwise allocating a high-level requirement into multiple lower-level requirements. Example: A 100-pound item that consists of two subsystems might result in weight requirements of 70 pounds and 30 pounds for the two lower-level items.[1]

Given the multiple levels of interaction between users, business processes and devices in global corporations today, there are simultaneous and complex requirements from a single application, from various levels within an organization and outside it as well.
The Software Requirements Analysis Process covers the complex task of eliciting and documenting the requirements of all these users, modeling and analyzing these requirements and documenting them as a basis for system design.
A dedicated and specialized Requirements Analyst is best equipped to handle the job. The Requirements Analysis function may also fall under the scope of Project Manager, Program Manager or Business Analyst, depending on the organizational hierarchy.
Software Requirements Analysis and Documentation Processes are critical to software project success. Requirements Engineering is an emerging field which deals with the systematic handling of requirements.
Why is Requirements Analysis necessary?
Studies reveal that inadequate attention to Software Requirements Analysis at the beginning of a project is the most common cause for critically vulnerable projects that often do not deliver even on the basic tasks for which they were designed. There are instances of corporations that have spent huge amounts on software projects where the end application eventually does not perform the tasks it was intended for.
Software companies are now investing time and resources into effective and streamlined Software Requirements Analysis Processes as a prerequisite to successful projects that align with the client’s business goals and meet the project’s requirement specifications.
Steps in the Requirements Analysis Process

(courtsey google) source google
I. Fix system boundaries
This initial step helps in identifying how the new application integrates with the business processes, how it fits into the larger picture and what its scope and limitations will be.
II. Identify the customer
In more recent times there has been a focus on identifying who the ‘users’ or ‘customers’ of an application are. Referred to broadly as the ‘stake holders’, these indicate the group or groups of people who will be directly or indirectly impacted by the new application.
By defining in concrete terms who the intended user is, the Requirements Analyst knows in advance where he has to look for answers. The Requirements Elicitation Process should focus on the wish-list of this defined group to arrive at a valid requirements list.
III. Requirements elicitation
Information is gathered from the multiple stakeholders identified. The Requirements Analyst draws out from each of these groups what their requirements from the application are and what they expect the application to accomplish.
Considering the multiple stakeholders involved, the list of requirements gathered in this manner could run into pages. The level of detail of the requirements list is based on the number and size of user groups, the degree of complexity of business processes and the size of the application.
a) Problems faced in Requirements Elicitation
• Ambiguous understanding of processes
• Inconsistency within a single process by multiple users
• Insufficient input from stakeholders
• Conflicting stakeholder interests
• Changes in requirements after project has begun
A Requirements Analyst has to interact closely with multiple work-groups, often with conflicting goals, to arrive at a bona fide requirements list. Strong communication and people skills along with sound programming knowledge are prerequisites for an expert Requirements Analyst.
b) Tools used in Requirements Elicitation
Traditional methods of Requirements Elicitation included stakeholder interviews and focus group studies. Other methods like flowcharting of business processes and the use of existing documentation like user manuals, organizational charts, process models and systems or process specifications, on-site analysis, interviews with end-users, market research and competitor analysis were also used extensively in Requirements Elicitation.
However current research in Software Requirements Analysis Process has thrown up modern tools that are better equipped to handle the complex and multilayered process of Requirements Elicitation. Some of the current Requirements Elicitation tools in use are:
• Prototypes
• Use cases
• Data flow diagrams
• Transition process diagrams
• User interfaces
IV. Requirements Analysis Process
Once all stakeholder requirements have been gathered, a structured analysis of these can be done after modeling the requirements. Some of the Software Requirements Analysis techniques used are requirements animation, automated reasoning, knowledge-based critiquing, consistency checking, analogical and case-based reasoning.
V. Requirements Specification
Requirements, once elicited, modeled and analyzed should be documented in clear, unambiguous terms. A written requirements document is critical so that its circulation is possible among all stakeholders including the client, user-groups, the development and testing teams. Current requirements engineering practices reveal that a well-designed, clearly documented Requirements Specification is vital and serves as a:
• Base for validating the stated requirements and resolving stakeholder conflicts, if any
• Contract between the client and development team
• Basis for systems design for the development team
• Bench-mark for project managers for planning project development lifecycle and goals
• Source for formulating test plans for QA and testing teams
• Resource for requirements management and requirements tracing
• Basis for evolving requirements over the project life span
Software requirements specification involves scoping the requirements so that it meets the customer’s vision. It is the result of collaboration between the end-user who is often not a technical expert, and a Technical/Systems Analyst, who is likely to approach the situation in technical terms.
The software requirements specification is a document that lists out stakeholders’ needs and communicates these to the technical community that will design and build the system. The challenge of a well-written requirements specification is to clearly communicate to both these groups and all the sub-groups within.
To overcome this, Requirements Specifications may be documented separately as
• User Requirements - written in clear, precise language with plain text and use cases, for the benefit of the customer and end-user
• System Requirements - expressed as a programming or mathematical model, addressing the Application Development Team and QA and Testing Team.
Requirements Specification serves as a starting point for software, hardware and database design. It describes the function (Functional and Non-Functional specifications) of the system, performance of the system and the operational and user-interface constraints that will govern system development.
VI. Requirements Management
Requirements Management is the comprehensive process that includes all aspects of software requirements analysis and additionally ensures verification, validation and traceability of requirements. Effective requirements management practices guarantee that all system requirements are stated unambiguously, that omissions and errors are corrected and that evolving specifications can be incorporated later in the project lifecycle.