AI Based Tools are Transforming Software Development Sector

Software is the foundation for all developments in our lives. Software development technologies have encountered a massive transformation over the recent years, whether it is Amazon’s drone deliveries or Snapshot with AR offerings. Businesses have embraced almost all upcoming technologies in their software development, and AI is no exception. The impact of AI based tools on software development changes how companies run and make software smarter.

AI has impacted all sectors & digital arenas beyond measure; software development is no exception. As industry professionals claim, software development lifecycle’s every aspect is likely to be benefited by artificial and machine learning technologies. From the time of forecasting project timeline, software delivery estimation to bug fixing and coding assistance & testing, AI & ML can help to program in various ways.

AI Based Tools

As of now, AI and ML have only been used through some tools developed for particular development purposes. In the upcoming years, AI & ML can be part of the SaaS (Software as a Service) package as well. Cloud services already take up the AI-enabled algorithms, and now it’s time for artificial intelligence tools to get packaged via subscription-based services.

AI integration in the software development process

Let us look at some real-life instances from the past about integrating AI into the software development cycle before describing AI’s true advantages in the software development process.

Google bugspot tool w3c: As most of the code changes within every month along with the rise in the entire codebase & developer team size, it gets increasingly harder to track all the bugs & errors. This bug prediction tool from Google uses machine learning algorithms & more in-depth statistical analysis to track the erroneous code or bugs and unreliable code lines. The tools enable using various useful metrics in detecting such flaws, including needed lines of code, dependencies, etc.

Stack Overflow AutoComplete: This autocomplete tool is a classic example of an AI-based software development tool. This tool enables autocompleting completely functional code lines based on Stack Exchange data of current code about the developer intentions.

Deep Code: A Zurich-based startup introduced an AI-based programming tool referred to as DeepCode that works as the coding assistant for CRM software development projects. This tool has the powerful capability of learning from an enormous volume of 250k coding rules available from public & private GitHub repositories. According to the appropriate context and based on these rules, the tools tell software developers on how to fix the code. It has already been considered a helpful tool for optimizing code and fixing bugs.

Now let’s describe the critical ways AI can play a huge role in software development.

Accurate Delivery Estimation

An experienced app development company that worked on several projects of different lengths & intricacies can easily provide a proper estimation of software’s delivery. But some factors like many coding errors & accommodating client requests during the projects can lead to unfavorable delays beyond the delivery schedule. In this aspect, AI can help development companies give the most accurate delivery time as AI-powered analytics & machine learning can examine various information from similar projects.

Artificial intelligence development can guarantee to forecast more accurate project timelines & deadlines by using all software project data, including features, an anticipated timeline for each feature, staff profiles, & track record, project timeline for similar projects, etc. Artificial intelligence can make a more accurate prediction than estimation by humans that are less likely to consider each detailed aspect.

AI-Powered Programming Assistants

AI-powered programming assistants are becoming popular among advanced developers. For instance, Kite for Python programmers can render timely support & recommendations when trying to understand the documentation or conduct a debugging exercise. The tool can regularly help developers with pertinent code examples and suggestions of pertinent documents that can be useful for the aim and context. Such tools can help developers prevent redundant workloads from focusing more on important aspects like delivering a better user experience.

Bug Fixing

Bug fixing is one of the crucial aspects of software development that has been entirely refurbished by artificial intelligence. As the data volume needed to be tested increases substantially, the chances of human-made errors and neglected bugs also rise parallelly. The bug fixing tools enabled by artificial intelligence & machine learning algorithms can play an important role here. The bug fixing tools boosted with AI algorithms help auto-correcting the codebase without needing manual interference.

Enriched Code Compiler

Compilers’ task is to convert the written code with a high-level programming language into ML or particular instructions for the hardware to follow. In doing this, a smart compiler doesn’t need the original source code, which fastens the process. AI enables code compilers to finish a task of code-regeneration for the hardware in some hours rather than months.

Artificial Intelligence in Testing

The major role of artificial intelligence in the software development life cycle is played in testing. AI in software testing refers to the following things:

  • AI-based tools for software testing
  • Testing AI-powered products & other deliverable

Either way, artificial intelligence plays a significant role in facilitating software development companies to transfigure software testing into a powerful self-driving exercise with the least manual interference. For instance, TestComplete and Eggplant tools in their recent releases included some AI-enabled features.

Despite all the buzz and efforts for artificial intelligence to make our apps smarter, still, we aren’t able to give complete independence to an AI tool for developing a full solution for our purpose. For now, AI-powered tools are there to amplify our efforts to build and deliver a better product. So, while app developers’ role will continue to stay, how better we can use these AI-based tools will matter utmost.