We’ve seen how easier software development has become with the inception of powerful IDEs, how enjoyable it is to write code with a linter, and how useful method extractions are when we want to refactor a program. Those are examples of static solutions that provide tremendous value but what if we could have those tools learning alongside us? What if we could have software that automatically fixes its bugs? That’s the AI promise for software development.
AI is already shaping software engineering
The idea of automating software country wise email marketing list development isn’t new. According to Bataresh and collaborators, artificial intelligence has played a significant role in SDLC since at least 1975. Each stage of software engineering (requirements, design, development, testing, release, and maintenance) has something to gain from artificial intelligence.
Even no-code solutions like Bubble will reap the benefits of more refined AIs, since the basis of these tools is to create algorithms based on a specific set of parameters chosen by the user. The results can be limited, but with AI we will eventually see more dynamic tools that adapt and build code more flexibly.
Here are some of the ways in which artificial intelligence could help software engineers.
Automating requirements
Software developers base the initial mitigating software development costs with nearshore outsourcing goals of their projects on 2 sets of requirements: the needs as established by the client’s vision and the nature of the data. For example, an application that gathers and works with unstructured data is a whole different beast than one that gets information from a relational database.
AIs are a great asset for information gathering which in turn makes them an amazing addition at this stage. Let’s take NLP (Natural Language Processing) as an example. An AI could use it to help software developers analyze their interviews with their clients, flagging important keywords which in turn can help predict features and challenges that may arise down the pipeline.
On the other hand, if the project involves a great amount of unstructured data, it might be hard for the developer to code for all eventualities, and going over the data might not be humanly possible.
In those cases, AI can parse and categorize the data and show irregularities that could cause no small amounts of headaches in the long run.
Software design
Every software development project shops 9177 requires coding, and as any seasoned developer can attest to, working with code is fulfilling, but also extremely frustrating at times. Nothing is quite as maddening as failing to compile code just to realize that you missed a semicolon someplace.
Powerful IDEs like Visual Studio Code and PyCharm are already implementing AI-assisted coding suggestions, offering immediate feedback to the developer about errors and suggesting changes to the code.