6 Ways AI Is Making Forays into Software Development

AI is changing every aspect of business which includes software development. Machine learning techniques can be used to not only speed up the traditional software development lifecycle (SDLC) but also present a completely new way of inventing technology.

Traditionally, developing a computer program necessitates defining exactly what you want the system to do ahead of time and then hand-engineering all of the technology’s features. The Pre-AI era has seen the prominence of powerful computers; it was possible to explicitly code many tasks.

Many tasks and decisions, on the other hand, are far too complex for computers to learn in a rigid, rule-based manner. Even a seemingly simple task like determining whether a photograph or video on the internet is of a cat is beyond the capabilities of traditional software development. No team of engineers could enumerate all of the rules that would reliably recognize cats versus all of the other possible objects that can appear in media, given the vast number of possible permutations that cat photos can take.

Machine learning and deep learning are examples of AI techniques. An engineer does not give the computer rules for making decisions and taking action in these approaches. Instead, she gathers and prepares domain-specific data, which is then fed into iteratively trained and improved learning algorithms. Without a human explicitly encoding this knowledge, a machine learning model can deduce from data what features and patterns are essential. The results of machine learning models can even surprise humans by highlighting perspectives or details that we hadn’t considered.


Traditional software development, on the other hand, is not going away. Training a high-performing machine learning model is only the first step toward commercializing AI. Only a fraction of real-world machine learning systems is made up of machine learning code, according to a popular Google paper.

Regular software will still be required to handle critical components such as data management, front-end product interfaces, and security. Machine learning approaches, on the other hand, can benefit technologies developed using the traditional SDLC in the following ways:

  1. Rapid prototyping is a term used to describe the process of creating a prototype quickly. Machine learning has shortening the time it takes to turn business needs into technology products by allowing fewer technical domain experts to develop technologies using natural language or visual interfaces.
  2. Programming Assistants with Intelligence. The majority of a developer’s time is spent reading documentation and debugging code. Smart programming assistants can help cut down on this time by providing just-in-time assistance and recommendations, such as relevant documents, best practices, and code examples. Kite for Python and Codota for Java are two examples of such assistants.
  3. Error Handling and Automatic Analytics Programming assistants can also use their previous experience to identify common errors and automatically flag them during the development phase. Machine learning can be used to analyze system logs to easily and even proactively flag errors once a technology has been deployed. It may also be possible in the future to enable software to change dynamically in response to errors without the need for human intervention.
  4. For team collaboration and long-term maintenance, clean code is essential. Large-scale refactoring is an unavoidable and often painful requirement as businesses upgrade their technologies. Machine learning can be used to analyze code and optimize it for readability and performance automatically.
  5. Software development is notorious for exceeding budgets and timelines. Accurate estimates require a high level of expertise, a thorough understanding of the situation, and familiarity with the implementation team. Machine learning can be trained on data from previous projects to better predict effort and budget, such as user stories, feature definitions, estimates, and actuals.
  6. Debating which products and features to prioritize and which to eliminate takes up a significant amount of time. An AI solution that has been trained on past development projects, as well as business factors, can evaluate the performance of existing applications and assist business leaders and engineering teams in identifying efforts that will maximize impact while minimizing risk.
  7. The majority of interest in applying AI to software development, according to a Forrester Research report on AI’s impact on software development, is in automated testing and bug detection tools.


AI automation in your company will free up time for other tasks, reduce human error (which costs money), and lower wage costs because the company will require less manpower.

What about the out-of-pocket expenses? There’s no denying that implementation costs exist, but they’re well worth it in the long run, as evidenced by a review of the available options. When it comes to implementing a chatbot, for example, you can either buy a ready-made solution or try it out for free; use self-service platforms to build a chatbot within a framework, or build one from the ground up.

Other AI applications have different pricing models, with some requiring hardware purchases rather than cloud-based software subscriptions. However, AI will become more affordable over time, even for small businesses.

Meanwhile, you can begin incorporating AI into your business to boost productivity and revenue by utilizing machine learning automation and predictions.

As you begin this journey and delve deeper into the world of AI, you will discover that the possibilities are endless.