Developing compelling, workable software is challenging for more than a few reasons. There are plenty of examples of software development firms who struggle at creating workable software, despite having lots of money in the bank and employees on the payroll. No matter how large your pile of cash or how smart your people are, your software development goals must be tied to actionable outcomes and usable solutions. Your clients and customers must feel a need is being filled by what you are offering.
Software development done right offers something that those you are marketing it to feel like they cannot live without. Whether a piece of software is good or bad can be quantified and measured by completing a software quality assurance (SQA) test. This is something we here at Clever Coding know quite a bit about. Yet, ensuring a piece of software is worth its digital weight is about more than simple testing. The process must be looked at in its entirety.
The difficulties of software development testing and deployment
Anyone who has ever worked in a role that entails software testing will tell you it isn’t an easy gig. Not only do these intrepid individuals have to ensure the designed product meets the requested internal requirements, but they must also make sure the external requirements – which is to ensure the software does what it is supposed to do – are met.
New pieces of software are incredibly complex and, in some cases, the client knows what they want, but it may be that the requirements are too onerous. When salespeople sell something that has never been built before, you can imagine the technical briar patch the software development team is walking into. Checking the quality of a piece of software is about testing it to meet requirements, but it is also a form of cross-checking that it was developed properly in the first place.
This is where machine learning comes into play. Some in the industry openly wonder at what point we will start seeing software that creates itself. Indeed, examples of this are already popping up. While humans are intelligent, creative, and easily adaptable, there is one thing that artificial intelligence (AI) has over its flesh-and-blood counterparts: Consistency.
Welcome to the machine
Pink Floyd said it first, but did they have any idea how right they were? Welcome to the machine.
Consider this: Companies are already working on and deploying AI solutions designed to test and identify software regressions that allow human testers to take the pain out of functional testing. Regression testing refers to the process by which you create many different software tests and continually run them on the product as it develops.
Let’s say you have a programmer working on a particular piece of software. What they can essentially do is use the AI to run a constant regression test – based on predefined parameters – so that the programmer can continue programming. Before the golden age of AI, and still in many corners of the software development industry, testers would utilize record-and-playback tools.
Now, graduates fresh out of places like Oxford University are using seed money from mega-players like Goldman Sachs to automate traditional coding tasks. They want to put everything from bug fixing to test writing and finding exploits into the hands of an AI. Before long, they will likely be employing AI to even write original code.
When you get down to the heart of the matter, software development is an ever-changing and always-evolving programming realm. And while we here at Clever Coding still proudly employ talented, experienced, and professional software developers, blog topics like these put us at the forefront of thought leadership on the topic. We’re pretty sure we could get good results out of hiring an AI, but we still have plenty of confidence in the talents of our team. How can we put them to work for you?