In recent years, Agile and DevOps have boosted the velocity of software product delivery. Any further reductions in time to market could result in significant advantages against competitors. Ways to tweak efficiency are in high demand, and for many developers, the future lies in the use of Artificial Intelligence to realize efficiency gains across the product delivery lifecycle.
In no more than a few years’ time, Agile has become the dominant methodology the software world. It didn’t take long before product developers in all industries understood that the use of fast, iterative and incremental strategies wouldn’t jeopardize product quality, but could greatly reduce delivery times. Today, Agile is a de-facto standard practice, and early adopters have long moved on to implementing DevOps strategies to further accelerate and enhance the continuous development of their software products.
Like so many other industries, software development seems to have reached a point of maturity where the way forward is in optimizing efficiency. Rather than revolutionary ideas, companies can now stay ahead of the curve by mastering Agile and DevOps practices to shave valuable work hours off their delivery processes. An exciting area of innovation investigates the use of Artificial Intelligence to provide such advantages.
Artificial Intelligence for Agile/DevOps
Key to the success of DevOps is the use of automation to cut the time and effort costs of certain software development processes. Due to the high number of relatively simple and often repetitive tasks, smart strategies to automate can help realize great efficiencies in the CI/CD pipeline. With AI, the range of tasks that can be automated is greatly expanded, and its use cases stretch beyond that.
There are many possible avenues of applying machine learning and AI in software development. Intland Software’s approach, targeting predictive development, involves the use of Artificial Intelligence to enable accurate forecasts on various phases of the software delivery lifecycle.
Predictive strategies apply machine learning to historical data sets, enabling the acceleration and enhanced accuracy of planning, effort estimation, development, testing, and production. Through transfer learning, these predictive algorithms can then be applied to practically any data set. This way, AI has the potential to positively affect every stage of software delivery, accelerating the entire process.
“Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task.”
Intland Software’s AI concept
Because its feature set covers the entire process of software development from idea to release and maintenance, codeBeamer ALM’s central database store a wealth of lifecycle information. By extracting valuable information all that data, Artificial Intelligence can make forecasts on future projects to help decision-making.
Intland’s concept on applied machine learning could, for instance, judge the state of a current sprint and predict future performance without a retrospective analysis of anti-patterns. Relying on the processing of story point information by an AI algorithm, the solution could forecast future trends on your burndown trajectory.
In effect, codeBeamer’s AI module will be able to warn scrum masters early on in case it “thinks” that the current sprint’s deadline might be in jeopardy. Having access to this information well in advance, scrum masters will be able to alter their resource allocation decisions to make sure the sprint is finished on time.
Similarly, codeBeamer ALM’s AI algorithm will be able to analyze historical team velocity data and tell you the expected average velocity of your teams. This will greatly support efforts to optimize the size of sprint backlogs, and can help master Agile delivery by also uncovering resource limitations.
Overall, the application of predictive Artificial Intelligence-based algorithms in the development of complex software products can help accelerate Agile/DevOps delivery. By using machine learning to draw insights from vast amounts of lifecycle data, codeBeamer ALM’s AI features will enable better capacity planning, improved product quality, and a lower overall risk level.