Slow and insufficient definition of requirements, time-consuming coding and error-prone bug detection are just some of the issues that teams face in the software development life cycle . Consequently, a paper published by thesays that only 35% of software development projects are completed successfully. As we navigate through 2024, the potential for AI to improve every phase of the SDLC—from planning and design to coding, testing, deployment and maintenance—has never been more evident.
In our work, for instance, these tools assist us in modeling and simulating scenarios, forecasting potential challenges and refining the requirements and project scope more accurately and timely. Additionally, we use multimodal inputs from speech, images and text, where generative AI can help personalize solutions and better understand the customer profile.AI has gained momentum in the design process as well.
ML-driven allocation and prioritization, additionally, can lead to eliminating human biases from the decision-making process, bettering talent based on their skill set, assessing risks and allocating and predicting future workforce needs. Automating the collection and analysis of user stories, on the other hand, will eliminate inconsistencies, duplicates and complexities.
A study found that software engineers using GitHub Copilot complete tasks 55% faster than those who don’t . Used by over 20 million engineers already, the tool utilizes ML models to suggest whole lines or even blocks of code based on context, reducing the time spent writing boilerplate code., demonstrating its role in conception, prototyping and more. As businesses adopt AI-based tools, they ultimately achieve more successful project completion and a competitive edge in the market thanks to better efficiency, higher speed and precision.