November 27, 2019
In the software development industry, a big problem that most software engineering teams are facing is code collaboration. Code collaboration helps members in the team learn from each other easier (especially newbies who have just joined the team), solve problems together, thus, complete the tasks faster in a collaborative way.
The problem is, imagine that about 40 editors are working on a document at the same time. It is almost certain that your shared word processor platform will be prone to problems when the edited document turns into a mess. But further afield, if that document amounted to 5 million lines, 70 times longer than Leo Tolstoy’s War and Peace, how much worse was the chaos? That’s what software developers are facing when they work on collaborative projects.
That’s why we will need an AI assistant for software engineering teams, helping teams set up a common process for newly engaged engineers to work alongside veteran engineers in public projects.
Software engineers are at the center of today’s innovation, the race to build advanced products and applications that makes companies constantly seeking and recruiting software, so demand is also increasing. So it’s no surprise that according to LinkedIn data, the software is the industry with the highest revenue rate in 2018.
Software engineering has become extremely complex over the past 15 years. In the past, there was a period of 6 months to 1 year to plan new software releases. But now, we release daily, weekly. Customers’ expectations are also surprisingly high. You have to release them on different platforms and different devices like computers, Macs, Android phones, iPhones, tablets, watches …
Moreover, the problem that most startups often face is when they expand technical teams. Their software engineers are at a growth stage. Companies will have to add more software engineers to the team, which slows down the exponential development process with each new employee, adding new lines of code and features.
While most companies may not be able to match Google’s 2 billion lines of code, this is certainly a challenge to keep up with a growing source code base as you add features and new releases.
Just like any other AI startups, the biggest challenge is collecting enough data to train the AI system to compile code (into natural language) more accurately.
As new engineers are being recruited more and more urgently, the innovative AI system should be able to help them quickly adapt when programming code is converted into a natural language that is suitable for all software engineers. This also solves the problem of programmers’ differences when writing in different programming languages.
Established in 2016, InApps Technology has continually evolved over the past years to reach the forefront of being a top software outsourcing company in Vietnam.
We work across the entire lifecycle of taking an idea to market with ideation, validation, product strategy, engineering, ongoing maintenance, and growth. We partner with Start-ups and SMEs as well as enterprises globally to help them solve their toughest challenges, mitigate the risks and bring their great ideas to life.
Learn more about us: https://inapps.net/why-inapps-contact-us-now/
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