9 Ways To Implement Artificial Intelligence and Agile-Powered Management in Software Development

Everyone agrees that since its inception in 1956, AI has revolutionized the way businesses make decisions and deploy their resources.

Over the decades, AI has proven its worth by helping businesses in a variety of industries flourish. From robots in a vehicle manufacturing plant to predicting currency and stock movements to traders, AI is part of our lives.

Today, organizations use AI to automate mundane tasks, making possible what was once considered impossible. Let's look at how AI helps agile-powered management and how to implement it.

AI in Software Development

AI has transformed every business function, and software development is not exempt. Machine learning can help accelerate the contemporary software development lifecycle. AI redefines how developers build products.

Normally, software development requires that you specify what you want the system to do before you build it. More on this later.

There are steps and decisions that are just too complicated to be taught to computers in a rigid manner. For example, how do you teach a computer that a certain photo contains a fire hydrant? Tricky, right?

Continuing with the fire hydrant photo example, there are simply so many permutations like weather differences, distance, angle, and clarity, making it quite impossible to enumerate all of them in a program.

Let us now see how you can implement AI in your agile development process.

9 Practical Ways To Introduce ML Techniques In Agile Development

Let’s face it: traditional software development is here to stay. So now the million dollar question is: how we can use machine learning to augment our software development process?

It’s a fact that the major application components like software interfaces and data management will still use regular software. However, you can introduce ML techniques into your SLDC as follows:

1. Coding Assistants: Most of a developer’s time is spent debugging code and reading the documentation. With smart coding assistants implemented using ML, developers can get quick feedback and recommendations based on the codebase, saving a lot of time. Great examples include Java’s Codota and Python’s Kite.
2. Automatic Coding Refactoring: It is important to have clean code because it makes collaboration a lot easier. Maintenance of clean code is also orders of magnitude easier than unclean code. Here's the deal; whenever an organization scales, refactoring becomes a painful necessity. With ML, it is easy to analyze code and optimize for performance by identifying potential areas for refactoring.
   

3. Making Strategic Decisions: A large chunk of a developer’s time is spent debating the features and products to prioritize. An AI model trained with data from past development projects can assess how applications perform, helping business leaders and engineering teams to identify methods of minimizing risk and maximizing impact.

4. Providing Precise Estimates: The profession of software development is known for exceeding budgets and timelines. To make a good estimate, it’s important to have a deep understanding of both the context and the development team. You can train an ML model using data from past projects like user stories, cost estimates, and feature definitions. This can prove very helpful in predicting effort and budget.

5. Analytics and Error Handling: Coding assistants based on ML can identify patterns in historical data and identify common errors. If the engineer makes such an error during development, the coding assistant will flag this. And that’s not all…after deployment, ML can be used to analyze logs and flag errors that can then be fixed. This makes the software developer proactive in solving errors. Who knows? Maybe in the future ML will correct software based on errors without the need for human intervention.

6. Rapid Prototyping: Converting business requirements into technology takes months at best or years to turn into technology. Today, however, ML is reducing development time by helping individuals with less technical knowledge to develop technologies.
   

7. Using AI for Project Planning: The human brain is an astonishingly great knowledge powerhouse. And what’s even more surprising is that we all have different cognitive abilities from one another. No two project managers will have the exact same thoughts on the same project. Enter ML. By replicating human intelligence, ML can create various permutations of a situation similar to the human brain.

8. Risk Estimation: Making informed decisions on risk estimation in software development is complex and factors in budgeting and scheduling constraints. In the beginning, healthy completion levels appear likely for every project. But here’s the kicker, when you start the project, the external environment and project interdependencies alter the probabilistic scenarios. Our limitation as humans is limited by the capacity to store and reproduce information.
   
   ML allows you to retrieve parameterized information on demand. You can train the AI model with past data of project start and end dates. This way, it will give you a realistic timeline for the current development project.

9. Project Resource Management: Delivering a software product depends on having the right people working on the project. Again, AI goes deep into the data on the history of past projects. It can give you information in real time on which developers are engaged in other projects. This makes it easy for you to know which developers are ready for deployment. Based on the ML prediction, you can either increase or reduce the number of developers.

Based on the project at hand, AI can get your developers up and running as soon as possible by providing training materials that help them enhance their skills and knowledge. Onboarding and project delivery is very fast.

Here’s why this is important:

When you allocate optimal workloads using AI, you guarantee that throughout the year, you will utilize your staff 100 percent. Moreover, by automating repetitive tasks, you have more time to make project-centric decisions.

But then the question arises: How will AI change the way we build software? Read the next section to find out.

How We Will Build Software In The Future

In AI, the software engineer does not give the computer the steps required to make a decision or take an action. Rather, they curate data that is specific to the domain, input it into learning algorithms.

The best part?

The model recognizes patterns in the data that are important in making the decision. When given test data, the ML algorithm compares to what it already has in its database and makes the decision.

The amazing thing is that there is no knowledge encoding on the engineer’s part. In fact, the results from an AI model usually uncovers strange and interesting patterns that are hard for humans to intuitively recognize.

The result?

AI has changed software development by exposing human perception, definition, and execution of programming. In fact, Google’s Pete Warden believes that in a decade, most software development jobs won’t involve programming.

According to Andrej Karpathy, OpenAI’s ex-research scientist and current Tesla Director of AI, future programmers won’t maintain complex repositories, analyze running times or create intricate programs.

They’ll collect, sanitize, label, analyze, and visualize data feeding neural networks.

Just to appreciate how much this AI and agile will change the way we build software. let’s look at the difference between the two.

Traditional Development Process vs. Machine Learning Development Model

In the traditional approach to building software, an engineer gives explicit steps to a computer using a programming language like Java or C++. Before writing a single piece of code, though, there are several steps.

The steps are the requirement definition, followed by design, then development. After building, there is Quality Assurance (QA), which involves running tests to ensure that the software does what’s expected of it.

After receiving a green light from QA, the code gets deployed to the production environment. Engineers must then continuously maintain the code.

Agile fastens the software development process. In agile, developers choose a smaller feature or group of features that they focus on during the 2 to 4-week sprints. At the basic level, therefore, agile and waterfall are similar.

However:

In the ML software development model, developers define the problem and list the goals they’d like to achieve, collect data, prepare the data, feed the data into a learning algorithm, deploy, integrate, and manage the model.

Conclusion

There is no doubt that AI has proven essential to business prosperity since its conception in 1956. It is no surprise that many firms are leveraging the potential presented by AI to automate mundane tasks.

Using AI in agile development brings even more business benefits.

Among other things, you can make credible budgeting predictions, have a 100 percent developer utilization rate, error detection in production, and development environment and code refactoring suggestions.