During last decades of software development, software analysis, development and maintenance has evolved dramatically. As programming languages, development methodologies, frameworks are evolving, resulting simplicity in management of software development life cycle, on the other hand software systems are becoming more complex every day. The Requirement Analysis as a result nowadays is a crucial phase in software development life cycle as it was previously.
With the new trends of generative Artificial Intelligence (AI), many activities in software development phases can be automated. In requirement analysis phase however, many activities appear to include human interaction and decision making. The requirements analysis is a very important phase in software development life cycle and even can determine the success or failure of the software project. It is obvious that human interaction in this phase is still critical but AI can help in many activities in this phase as we will mention later, but first lets have an introduction to the requirement analysis phase.
What is Requirement Analysis
Requirement Analysis is concerned with the elicitation, evaluation, specification, analysis, and evolution of objective, functionalities, qualities, and constraints of a software system, within an organization or physical environment.
To ensure a software solution correctly solves a particular problem, we must first correctly understand and define what problem needs to be solved. It may seem obvious at first, however figuring out what the exact problem is and its boundaries that we should address, can be surprisingly difficult.
The problem to be solved arises within some broader context. It is in general rooted in complex organizational, technical, or physical world. There is a system-as-is and a system-to-be that solves the problem in that environment. The software to be developed or modified is just one component of the system-to-be. Other components will in general pertain to the machines surrounding world. They will form the environment of the software-to-be, such as:
- People or business units playing specific roles
- Physical devices operation under specific tasks
- Legacy software that software-to-be needs to interact
In this regards, we may conclude that requirement analysis is evaluating, documentation, consolidation, revising, and adopting the objectives, capabilities, qualities, constraints and assumptions that the software-to-be should meet based on problems raised by system-as-is, and opportunities provided by new technologies and innovations.
The Requirement Life Cycle
The requirement analysis process is composed of different activities, yielding various products and involving various actors and stakeholders. The activities composing requirements analysis include:
- Domain Understanding. This activity consists of studying the system-as-is within its organizational and technical context.
- Requirement Elicitation. This activity consists of discovering requirements and assumptions that will shape the system-to-be based on the weaknesses of system-as-is as they emerge from domain understanding.
- Evaluation and Agreement. The aim of this activity is to make informed decisions about issues raised during the elicitation process and produce preliminary draft proposal.
- Specification and Documentation. This activity consists of detailing, structuring, and documenting the agreed characteristics of the software-to-be as they emerge from evaluation activity.
- Requirements Consolidation. The purpose of this activity is quality assurance. The specifications resulting from the preceding activity must be carefully analysed. They should be validated by stakeholders to ensure inconsistencies are found, before software requirements are transmitted to software design team.
Requirements analysis may affect next phases in software development life cycle substantially and any error in this phase may cause huge costs of even failure of the whole project.
Artificial Intelligence (AI) Role in Requirement Analysis
Many activities in the process of requirements analysis needs human interaction and decision. However, AI can be used to automate many tasks involved in requirements analysis.
AI can be used to generate surveys, analyse natural language, and create prototypes to help elicit requirements from stakeholders.
AI can be used to identify inconsistencies, ambiguities, and incompleteness in requirements. It can also be used to asses the feasibility of requirements and to identify potential risks.
Decision about the priorities in a software system could be difficult and AI can be used to prioritize requirements based on their importance, urgency, and cost.
AI can be used to track changes to requirements, to manage dependencies between requirements, and to ensure requirements are complete and consistent.
In addition to automating tasks, AI can be used to augment human capabilities in requirements analysis. AI can be used to provide decision support, collaboration, and learning tools that can help analysts manage complexity and uncertainty of requirements analysis.
The requirements analysis phase has an important effect of the later phases of design, implementation, testing, deployment, and maintenance of a software system. In fact the requirements analysis is at the heart of software development, since it will determine the problem to be solved and its scope. AI can help requirement engineers provide better specifications and documentations that is consumed by software engineers in design phase.