Racing against time as SDLCs get more aggressive, ticking all boxes in a perfect DevOps scenario is a challenge that is surpassing all human capabilities now. The custom application development services market is expected to expand by 30% from 2018 to 2023 globally, and the scale at which software works is growing steadily as more digital power finds its way into people’s lives. The processing costs are going down and the brains putting the code that makes the hardware turn are working at capacity.
As machines become more and more capable of handling complex core scenarios, the DevOps surrounding those scenarios becomes exponentially more convoluted, throttling the pace of development cycles. From streamlined requirement-gathering to elevating the standards of quality assurance and security, AI is already helping drive downtime and inefficiencies, empowering the developers and satisfying the customers. Let’s dive into the fascinating world where Machine learning (ML) and Artificial Intelligence (AI) integrates with DevOps to help build reliable, secure and fast-updating software.
The scale of logs:
With more consumers to satisfy and more use cases to cover, support is facing a mammoth growth in the amount of logs a software produces. Finding the cause of a failure in those logs within relevant time is no human feat. AI is trained to find certain patterns in the logs that can easily discern anomalies from regular data and can help pinpoint the defected areas of code and logic, saving support teams valuable hours and decisively enhancing the pace of development.
Auto Optimization of Code:
One of the major drawbacks of today’s fast delivery prioritized strategies is that the code is not always in its most optimized form and the recent surge in cheap computing power is to blame. But with the hardware no longer following mohr’s law and the complexities only going north, the need to make most of the available resources is back. Another field where the ever-increasing ability of AI to understand patterns is optimizing the code. Based on the code a developer has written, AI can understand the intent and suggest more optimized ways of writing the flow, and enhancing efficiency.
Efficiently Managing Requirements:
Not getting the requirements right in the first attempt can hamper the quality of code and push the deadlines, often frustrating the developers. NLP powered AI can help validate the requirements by looking for ambiguities, incompleteness, and inaccuracies in the requirements in the first go can make the requirements more robust and save significant time and effort in the overall development cycle.
Basic bug detection tools have been around for quite a while. But they have been limited to syntactic linting and few other additional features. Using AI, DevOps teams can find logical and security vulnerabilities in the code making it a highly valuable assistant for developers.
Giving wings to testing:
Covering all the test cases has always been a challenge for testers and developers. There’s always some or the other critical, unhandled use case that manages to evade the scrutiny of a tester, breaking the code in production causing recalls and delays in rolling out new features. AI that is trained to simulate scenarios by altering a given set of premises can automate not only testing but also test case generation, covering areas often ignored by conventional methods and resulting in much robust delivery.
Predicting Failure on Scale:
Scalability is an integral pillar of software development. AI can help predict the points of failure by automatically simulating different scales and stress testing each component and combinations of components and can suggest ways to avoid such failures, making the code truly scalable in every sense of the word.
Deployment, a breeze:
Deploying in different environments can be a dilatory affair, if a minor dependency breaks the whole pipeline. By analysing logs and deployment data from previous releases, AI helps keep track of such environment-specific dependencies, so that the DevOps teams can prepare well in advance to ensure that deployment, no matter the platform, goes smoothly.
Project Manager’s Smart Assistant:
Scoping a project with ever-changing requirements can be tricky and highly inaccurate. By analysing the code and data from the product’s life cycle so far AI can help understand the complexity of a change needed to fulfil a recent requirement and can accurately predict the time and effort needed to achieve the goal and also suggest the most optimal way to go.
With the sheer amount of data available throughout a development cycle and the current state of Artificial Intelligence, it is unavoidable that the two are inextricably linked in the quest for rapid development of secure, robust and scalable software.
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