Smart Software Development & Deployment: AI’s Next Frontier

From intelligent assistants like Siri and Google Now to a smart speaker and device embedded virtual assistants like Alexa, we can safely say that Artificial Intelligence (AI) is a part of our lives. From a business perspective, Gartner predicts that almost 37% of all organizations have implemented some form of AI in their business operations. In addition to many consumer-facing avenues, AI is also a highly efficient tool for improving efficiency and productivity in software development.

AI has brought unexpected levels of modernization to software development and deployment. From coding to testing and prototyping, the areas where AI can make contributions are widespread. The ones who invest wisely to on-board the best tools and follow best practices while utilizing the tools will gain a significant advantage over the competition. Together with Machine Learning (ML), AI is leveraging data and analytics to smartly improve efficiency, streamline processes and work tirelessly to help design, develop, and deploy software. This revolution is further abetted by elastic compute power and public clouds that allow software development companies to run heavier analysis workloads.

According to IDC, cloud-based infrastructure spends will reach 60% of all IT infrastructure by 2020. Improved processing power is seeing leading IT companies invest in better collection and utilization of data.

Let’s discuss more about how AI will help software development firms through data analysis, prediction capabilities, and more.

AI in Software Development

Automated Software Testing

Software testing is essential for ensuring product quality. But traditional quality assurance (QA) testing is a very time-consuming, tedious, and imperfect task and is prone to human errors and delays. This means that any errors and bugs in the code or any of the software layers can surface after the product has been released and even deployed. AI is a powerful ally for the QA testing team. It can help improve the testing process to ensure that such glitches are discovered and fixed before the release. Software testing is perhaps where software development has benefited most from AI technologies. IT companies have been able to create robust automated testing processes that require minimal manual effort. AI-powered testing tools are more accurate than human testers and can handle large volumes of code. They can easily scale up or down as required and can be trained to automatically correct bugs or errors in the code.

According to Forbes, “This is already helping improve overall software quality, as using machine learning to test software is the natural next step after automation testing. We’re already seeing testers use bots to find software bugs. Meanwhile, an emerging area involves testing tools that can use AI to help testers find flaws in their software and then fix code automatically after finding a bug.”

Enhancing Software Development

Software developers spend a lot of time going through documentation on project specifics and debugging code that is created. Smart, AI-powered assistants (like bots) can support developers by enabling access to real-time recommendations on code specific documents, best practices as well as examples of code that can fit particular use-cases perfectly. Software developers can also use AI tools to streamline processes and improve code quality. By automating the simpler, repetitive tasks of software development, AI will enable developers to focus on more complex tasks and on finding creative solutions to software problems.

Automating Error Management

Traditionally, when there was a bug in the software created, a developer would need to investigate the problem area and then rectify the error. This process was simple but time-consuming and prone to human error. But by using AI tools, an error can be easily detected and repaired without requiring human intervention, thus keeping costs low while boosting efficiency. While developers do a routine job of reviewing their own code regularly, chances of them skipping errors are high. This is where AI can be a game-changer. Intelligent AI bots can be trained to look for possible errors being committed by developers and infer insights into how potential errors are produced in code blocks. AI bots can review and capture erroneous behavior in code blocks faster and more efficiently than human coders. They can analyze system logs, check with pre-defined syntax, or documented code guides to flag errors before they are sent for next stage compilation or quality assurance phases. Moving forward, the goal is to enable AI-enabled error handling systems to identify, trace the root, and rewrite erroneous code or code blocks without human intervention.

Transforming User Interaction

Implementing AI in the software will dramatically change the way software is used because it will have the capability to learn user behavior and respond by serving variable content, automatically adjusting font size, adjust the placement of icons and buttons, etc. Users will get a dynamic experience that is customized for them basis their history with it.

Smart Prototyping

Most software development projects begin as a business requirement, and translating it into technology and a software product is no easy task. Also, a key reason for delayed project kick-offs and contract signing is the inability of IT companies to demonstrate the proposed solution and convince clients of their capabilities. The prototype they need to support their claims may have delayed implementation time and can possibly lead to loss of customer interest due to excessive delay. AI helps smart and faster prototyping i.e. building of a minimum viable product. However, AI technologies help shorten the entire software development lifecycle (SDLC) thus reducing both the time and effort involved by making it easier for developers of all skill levels to develop software.

Smart AI tools can help solution architects map business functionality into technical prototypes in a matter of minutes or hours as compared to weeks or months. This helps shorten sales cycles and build a critical reference point for the development team when they have to create the actual product. AI algorithms and datasets are used to train developers, while ML helps with the analysis of design and layout data, and ML algorithms can create high fidelity prototypes from low fidelity sketches rapidly.

Automatic Code Refactoring

Several development projects also involve transforming code to meet a firm’s evolving technology landscape. Transformation of legacy applications and large-scale refactoring is a mammoth task made easy by ML that analyzes code and automatically optimizes it for interpretability and performance.

Better Project Management

The benefits of AI go beyond coding. We all know that software development projects often go beyond budget as well as delivery schedules. To give an accurate timeline, it is crucial to understand the context, map resources, and understand the strengths of the implementation team. AI can correlate date from past projects regarding user experience, feature, cost estimates, and actuals to ensure proper planning and accurate budget planning. It helps prioritize features and sort out details that can be eliminated. AI also allows a practical assessment of existing applications and guides developers to identify methods that would maximize impact and minimize risk. Software development companies can leverage AI to build a concise delivery model as AI-based analytics and ML can analyze similar projects and serve useful learnings and insights.

Final Thoughts

AI and software development of the future will go hand-in-hand, and it is important for organizations to understand how AI will change software development and applications so they can adequately respond to the new technology and stay ahead of the competition.

We can expect AI to perform several routines and non-cognitive tasks helping software developers to have more time to focus on complex problem solving and decision-making, thus enhancing the software development process. AI can also be used to highlight existing areas that can be improved by software developers. What’s certain is that AI and software development will grow together.