How DevOps and AI Together Maximize Software Delivery Efficiency
Written by Silvia Davis & Riley Simmons
In a recent webinar, industry experts shed light on scenarios that harness the power of Artificial Intelligence (AI) and Machine Learning (ML) to revolutionize DevOps and, therefore, software delivery. Let’s explore the transformative impact of AI-powered predictive data and how it contributes to a more continuous and efficient DevOps lifecycle.
As per the insights from the “Accelerate State of DevOps 2023” report, it’s evident that the pursuit of improvement is a journey. When you encounter a bottleneck, you must address it promptly before moving on to the next challenge. Considering this perspective, enhancing efficiency requires proactively identifying bottlenecks and risks. Furthermore, in the event of software change failure, it is imperative to be well-prepared for swift recovery.
When we talk about scenarios that AI can contribute, let’s highlight and explore the following three scenarios:
Predicting delays to accelerate software delivery
Predicting risks to avoid software change failure
Leveraging solution patterns to recover faster and create resilience
1. Predicting delays to accelerate software delivery
While continuously delivering software might be straightforward for a single application, scaling this process to thousands of applications introduces significant complexity. It’s essential to grasp application release dependencies and proactively identify and address possible delays to mitigate potential complications.
AI-driven analysis of release dependencies, a vital component of DevOps, isn’t just about risk mitigation; it’s a strategic move towards ensuring continuous flow in the DevOps pipeline. Organizations can progress seamlessly from development to deployment by proactively understanding and managing dependencies.
AI-driven analysis of release dependencies reveals cascading effects, helping teams foresee potential risks.
Proactive risk management minimizes disruptions, allowing for a smoother flow of changes through the pipeline.
It enables a comprehensive view of the release pipeline, preventing delays.
2. Predicting risks to avoid software change failure
Another aspect of streamlining DevOps and accelerating software delivery is automating the process to identify the risk of software change failure and flagging critical changes to teams for further evaluation or moving changes with low risk to a fast lane to production.
One proactive approach to ensure the success of a change is by employing a tool we refer to as the “Change Credit Score.” Just like your credit score assesses the risk associated with financial decisions, our Change Credit Score evaluates the potential risk of failure for changes.
Our solution, known as “Change Risk Prediction,” is integrated within the framework of the Change Credit Score. Predicting the likelihood of a change’s success can substantially influence the release process. The Change Credit Score not only forecasts the probability of change success but also offers valuable insights for continuous improvement efforts.
The Change Credit Score acts as a quantifiable metric, providing an upper limit score configurable by each organization.
Deductions in the credit score are predefined based on the change’s implementation, execution, and impact on incidents.
Similar to a personal credit score, deductions decrease over time, encouraging consistent high performance.
3. Leveraging solution patterns to recover faster and create resilience
While you cannot entirely prevent problems from occurring, you can proactively equip yourself with the right tools to identify the root causes and swiftly expedite their resolution. This approach is instrumental in establishing a resilient process when handling the continuous delivery of hundreds or even thousands of codes daily.
The ML-driven approach to correlating releases with incidents transforms problem resolution into a continuous learning process. The ability to predict incidents and their causes in the pipeline’s continuous flow empowers teams to address issues promptly.
A dedicated dashboard, Change Impact Detection, monitors incidents post-implementation, aiding in quick problem detection.
ML models predict the probability of major incidents in the next seven days, enabling proactive measures.
A similarity score between incidents and changes helps identify probable causes, streamlining the resolution process.
Conclusion: AI-Driven Continuity in DevOps with Change Risk Prediction
Integrating AI and ML into software release management brings a paradigm shift in how organizations approach their release cycles. Proactive risk management, change success prediction, and incident correlation empower teams to streamline their processes, prevent delays, and ensure the overall success of software releases.
As organizations continue to embrace these intelligent solutions, the future of software release management looks poised for efficiency, agility, and unparalleled success. Stay tuned for more updates on how AI and ML continue to shape the landscape of software development and release management.
To learn more about the transformative impact of change risk prediction and other AI-driven solutions in software release management, watch the full webinar here.