In today’s market, speed matters.
This is particularly true in the AI-first world we live in, with winner-take-all markets going to companies that frankly, move the fastest.
Meanwhile, the opposite is true for companies with slower deployment times, which can cause reputational damage when unable to rapidly fix performance issues.
With technology being mission critical, if software development workflows aren’t run smoothly and efficiently, the costs can add up quickly.
At the same time, releasing high-quality, bug-free software products in shorter time frames is much more challenging than one might imagine.
From Y Combinator to the Fortune 2000, here are some things to keep in mind.
AI and Microservices
When it comes to speed, AI in particular should be considered to help with API testing for microservices, allowing teams with more attention to spend on infrastructure.
The rise of microservices has seen the entire software engineering industry begin a fundamental shift. Each new computing innovation changes how software architectures are built and the way software products operate.
In turn, a fragmented pipeline has been created where several teams are needed to be involved.
Not only does this make it harder to manage without one team responsible, according to Arjun Iyer of Y Combinator startup Signadot, but “every transition increases the likelihood that delays slip into the process, adding days or even weeks to the overall timeline.”
Further, modern environments mean that sources of waste aren’t one-off anomalie, but are often occurring at a massive scale.
Without examining the entire product development pipeline, it can be impossible to identify all of the pain points and understand how to fix them.
With an infrastructure focus that can use AI and automated screening and testing to manage the entire pipeline, organizations can unlock vast improvements in speed.
In this way, work can be distributed testing across engineering to fix bugs before they reach the later stages of development, and improve testing cycle times.
Beware of environmental sprawl
When it comes to slower development speeds, a second culprit is environmental sprawl, which according to Iyer, “refers to the significant increase in how many development environments are involved when building a software application today.”
Said Signadot’s CEO, “In our experience, we frequently see engineering teams tasked with the maintenance of more than five disconnected pre-production environments, including staging and quality assurance (QA), to mention just a couple of examples.”
“As a result, code updates have to hop back and forth between these various environments with distinct tests taking place at each stage before we can even think about deploying code into the production stage. Further, microservices communicate via a well-defined interface using lightweight APIs."
“However, API behavior is inherently complex, and it's unrealistic to place the burden on the shoulders of software teams with only manual methods,” concluded the entrepreneur.
Don’t overlook Intelligent Engineering
Further, when it comes to velocity, Intelligent Engineering, which is an approach that looks to increase the velocity at which products move through release cycles by eliminating waste, is increasingly being discussed.
This is in part due to bottlenecks that inevitably arise in software development.
According to Mahesh Raja of Ness Digial Engineering, “At present, software management platforms are used to manage the performance of each individual team. However, leaders often lack full visibility that can leverage this information and analyze performance across the entire development pipeline.”
“This means that we can understand not only how well teams are performing, but also how specific KPIs impact the rate of velocity. For example, the system can pinpoint where handover times between teams are causing delays or where quality control challenges are contributing to the number of bugs in the code.”
“In turn, it means that managers have a clearer understanding of what actions need to be taken to solve the problem areas identified during the analysis. For instance, if quality control is affecting productivity, then an automation tool can be actioned to help the team keep pace with testing.”
Don’t forget the importance of KPIs
Amidst the larger conversation on speed, ultimately it’s even more important to be working on the right projects at hand.
According to Rajat Mishra of Prezent, “Sitting at a desk in an office doesn’t guarantee focus or results. The real question isn’t where people work, it’s what they’re delivering. Great leaders create clarity around goals and outcomes, not keystrokes and facetime.”
“Visibility shouldn't be a crutch for leadership. Measure what matters: output, collaboration, and results, not whether someone’s in your line of sight”, concluded the executive.
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