Today, Artificial Intelligence (AI) has a lot of applications across industries and business functions. It can also be used in the development of new age software. AI techniques like Machine Learning and Deep Learning can be used for accelerating the Software Development Life Cycle. Let us look at how AI can revolutionise software development process.

New model of SDLC using Machine Learning

The traditional SDLC model consists of the following steps:

  1. Requirement analysis
  2. Design
  3. Development
  4. Testing
  5. Deployment
  6. Maintenance

The new machine learning development process consists of the following steps:

  1. Problem and goals definition
  2. Data collection
  3. Data preparation
  4. Model Learning
  5. Model deployment and Integration
  6. Model Management

In the ML model of development, the engineer need not instruct the computer on how to take decisions and actions. Instead, data can be fed into algorithms and the machine learning model deduces features and patterns from the data. In this new model, software development will largely move from being a programming process to a data oriented process.

The traditional model that starts with requirements analysis and undergoes design, development and testing, is a model that has limitations when it comes to complex systems, whether we use it in a waterfall method or agile method. Complex systems when managed by human beings are prone to bugs. However, in the new ML process, the code is written by machine learning methods like back propagation and stochastic gradient descent. The advantages of such a model are:

  • Homogeneity and easier management.
  • High portability.
  • High agility and integrability.
  • Outperforms human coders in some functions

But there are some disadvantages as well:

  • Inability of humans to understand the working of these complex ML systems.
  • Lack of control and inability to prevent problems like algorithmic bias and bigoted bots.

The traditional SDLC model can also be augmented by AI

Artificial Intelligence methods can not only bring about a new model but can also augment the existing SDLC model. Some critical components like data management, security and front end interfaces must still be handled by regular software instead of machine learning code. Thus, in many situations, SDLC model is appropriate, but can be augmented by AI. The augmentation can be done in the following ways:

1. Rapid prototyping

Normally, turning business requirements into prototypes takes a lot of time, but machine learning can shorten the process.

2. Intelligent Programming Assistants

Intelligent Programming Assistants like Kite and Codota can save time for developers by offering them support in reading documentation and debugging. This support is in the form of recommending relevant documents, code examples and best practices.

3. Strategy decisions

AI solutions can save time for business leaders and development teams by helping them quickly identify which features of a product to be changed, by taking into account past projects and business factors.

4. Project cost and timeline estimation

Many development projects suffer from poor estimation of costs and timelines. Machine Learning uses data from previous projects to give more accurate predictions of cost and timelines.

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