Disruptive technologies are now the norm in software development and have a significant impact on the Software Development Life Cycle (SDLC) and the way development teams work. As a result, Artificial Intelligence (AI) has become the focus of many technology companies.
AI is expected to revolutionize application development among a plethora of things.
"AI is predicted to have an impact on knowledge work over the coming decade that could be as much as $9 trillion."
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So it’s really no surprise that AI is a hot topic these days and even governments can’t ignore its possible wide-ranging consequences. The Obama administration released a report last fall and the multinational professional services network, PwC, just released its own report on AI’s potential global impact.
But at the same time, its impact has been difficult to articulate in the tech industry, even though both vendors and consultants agree that a paradigm shift is taking place.
So what does this all really mean for software development teams? After all, we are in the software development business, so let’s take a look at AI’s potential impact on the industry.
How will AI Transform SDLC?
The most obvious area that will be transformed in SDLC is testing and this has been evidenced by the rising levels of software complexity and automation. The key benefits of incorporating AI into testing processes are as follows:
- Achieve better code coverage
- Identify outliers quickly
- Find more effective ways to engage in testing
Furthermore, by pinpoint production bugs and remedies rapidly, you can also automate decision-making on what to build or test next with confidence.
Beyond testing, AI is expected to impact SDLC at the front end in the form of help with ideation. So not only will it help to clarify thinking, it will also be capable of suggesting new ideas.
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This could come in the form of a natural language processing (NLP) interface where developers can simply type in an idea which can then be translated into executable code. Planning and finally delivering an application will also benefit from continuous AI monitoring through the life cycle from inception to delivery. But at this juncture, we are still not sure as to how long it will take for AI to translate requirements into code.
DevOps is also expected to benefit from AI as it can help quickly identify the root cause of a problem. Further, it can also help prevent that problem from reoccurring.
As a result, AI is expected to have a significant impact on the entire SLDC and the whole software development team.
Software Development Teams will Need to Think Differently
Software development teams have to change the way they approach development. This is because the development process will shift from being rules-based programming to building self-learning capabilities into software.
What this really means is that developers won’t be able to create an application for a particular outcome. Instead, the applications that they build will need to be able to handle a wide variety of outcomes.
The major paradigm shift for developers would be to stop thinking about programming as a step by step process. Pretty soon, they will need to let the system learn and make decisions on how it chooses to move forward and that’s probably going to be difficult to accept.
However, to achieve this, Machine Learning (ML) will need to process an enormous amount of data. As a result, developers will need to start working closer with data analysts to make it a reality. If they get this right, developers will probably be able to build apps that think just like us (which is important when it comes to understanding humans).
But it won’t be confined to human thought as it should be able to see (and make connections) beyond our own human limitations. This will change current thinking in development teams that’s confined to inputs and expected outputs.
With AI and bots joining development teams, they would essentially need to embrace the unexpected. What’s driving the integration of AI and bots in development teams are as follows:
- Accessible data (everything related to building software is sitting on a system)
- It’s becoming easier to process unstructured data
- Data size is no longer a problem
- Team processes are more or less standardized
- Enhanced productivity outweighs inaccuracies
It’s understandable that all this will make a lot of people very nervous and will raise many cultural issues. Roles and job responsibilities will also shift in ways that may not always be anticipated. But as workflows and team productivity improve, it will be hard to ignore the benefits of incorporating AI into software development teams.