AI has transformed almost all business functions including software development. AI can be used to speed up the traditional SDLC (software development lifecycle) as they present a new paradigm to invent technology.
The traditional approach required you to specify everything in advance. However, there are several decisions and tasks that are too complex to teach a computer in a rule-based, rigid manner. Traditional software development cannot do something as simple as identifying a video or a photo on the internet is of a dog. It’s simply beyond its reach.
This is where AI enters. Deep learning and machine learning will completely transform the software development process. In both the approaches, an engineer isn’t required to specify the rules for how to take actions and make decisions. In fact, she prepares and curates domain-specific data which is updated into learning algorithms which are continuously improved and iteratively trained.
Machine learning models can conclude from the given data what patterns and features are important and don’t require a human to explicitly encode this knowledge. These models have the capability to highlight details and perspectives humans are yet to explore.
AI has unravelled how humans execute, perceive and define software development and that is the most profound impact so far.
According to Pete Warden research engineer, scientist and author, programming will not be involved in most software jobs.
However, traditional software development is here to stay. Data management, security and front-end interfaces are all critical components and will be handled by the regular software.
According to Mariya Yao, CTO of Metamaven, machine learning can benefit technologies developed by traditional SDLC in the following ways:
1. Rapid Prototyping
By developing technologies using visual interfaces or natural language, machine learning is significantly shortening the process.
2. Better Programming Assistants
These assistants offer recommendations and just-in-time support like code examples, best practices, and relevant documents. Kite (Python) and Codota (Java) are two such assistants.
3. Automatic Analytics and Error Handling
Programming Assistants can not only spot common errors but also automatically flag them.
4. Automatic Code Refactoring
Analyzing code and automatically optimizing it for performance and interpretability can be done by machine learning.
5. Precise Estimates
Predicting a reliable budget and effort requires familiarity, understanding and deep expertise of the context – all of which can be done by machine learning.
6. Strategic Decision Making
AI solutions can help engineering teams and business leaders spot efforts that would minimize the risk and maximize impact.
No Future for Programmers?
It inoculates the idea that in a few years, programmers and software developers will become obsolete.
Which means self-programming machines will soon replace programmers.
AI systems have just started to become more reliable but we can safely assume that AI will grow leaps and bounds in terms of importance and have only a defined role for programmers and developers.
But this will not happen tomorrow while 2020 is the best time to start a career in the software development industry. Visit bootcamprankings.com get more details on this career path.
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