Building Custom Software with Artificial Intelligence

Artificial Intelligence is branching out to fill up our daily lives through better and more effective routes. This makes it inevitable for the Custom Software Development firms to employ AI Technology in their development process. With business models across the global market experimenting with AI-based tools, one should not be surprised by the number of ways Artificial Intelligence can affect Custom Software development. In this article, we discuss a few SDLC processes that are already affected or are certain to be influenced in a not so distant future. Let us have a look.


Requirement Gathering

It won’t be an overstatement to call Requirement Gathering as the most crucial step in SDLC. Adding a layer of Artificial Intelligence for this phase can prevent the teams from unnecessary loopholes and ignorance. Essentially a human dependant step requirement gathering can still be assisted by Artificial Intelligence tools for better execution. There already are AI-based requirement-gathering tools developed by trusted sources in the market that can help the teams to find loopholes before the actual design and development process starts. Moreover, a lot of work is being done on Natural Language Processing or NLP – a study in AI Technology that can enable machines to comprehend human languages. The right breakthrough in this might completely change Requirement Gathering from what we see today.


Delivery estimation 

AI essentially feeds on past experiences. While experienced developers can give some estimation of the time required to get the work done, Artificial Intelligence can also factor-in coding errors, additional client requests, and some unforeseeable delays. The timelines prepared on these factors will obviously be more precise and helpful for both, the organization and the clients. Powered by Machine Learning, AI Technology can produce even more accurate timelines based on team performance, past similar projects and other such data points. It must, however, be pointed out that the accuracy of AI Technology in this regard, depends on how much past data can be fed to it.


Automatic Code Generation

With technologies like Microservices and Micro-frontends, we already have code reusability enabled in Custom Software development to a lot extent. Artificial Intelligence, in addition to that, is capable of building a suggestive code library that can help organizations to invest more time on innovation. As of now, the developers have Case-Based Reasoning, Template-Based programming and Routine Design at their disposal.


Testing and Bug Fixing

Software development data is increasing exponentially with every new project. Building scalable test cases and diagnosing for every possible bug is now way more difficult. Artificial Intelligence and Machine learning-based algorithms can assist testers in this front. As of now, there AI Technology has progressed enough to deal with testing and bug fixing issues like:

  • Test design
  • Scripting and implementation
  • Codeless automation script
  • Intuitive Dashboards
  • Code Auto-correction 
  • Faster patch creation, submission, and merge
  • More unique solutions
  • Save time and money on bug-fixing.



Any complex enterprise system will require a lot of data collection through sensors and/or third party Custom Software. It is, therefore, essential that this data is securely processed to fetch meaningful patterns without any leakage or breach. Moreover, irregular data may also be harmful to the solution being developed. Artificial Intelligence provides this two-way protection to the system. It is more equipped to detect deviations from expected behavior and draw more accurate conclusions. This also helps the organization to be proactive and reliant about the Custom Software and data security.


Conclusion and the Future

With more use cases coming its way, Artificial Intelligence is destined to refashion the Custom Software development process. We already saw the predicted as well as ongoing effects of AI Technology in various SDLC phases. It’s just a matter of a little time when concrete tools and platforms will be built that would primarily focus on enabling Artificial Intelligence-based Custom Software development. Enterprises will recognize the potential benefits of AI Technology and will start investing to encourage the same. The market will have a new vertical for the industries to compete with. We are already at the doorway to the future of SDLC. Let us plan our way with Intelligence.

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