A renowned leader in the industry, with a roster of software and security companies including Palo Alto Networks, Atlassian, Check Point Software, ForgeRock, Salesforce, and Motorola, CloudShare helps companies reach higher customer acquisition and retention by enabling them to create and scale engaging, customized hands-on virtual experiences in minutes, anywhere in the world, at any time, in any cloud, no matter how complex the software*
The engineering team had expanded over the years to support new products and clients. But in mid-2020, months into the remote work reality of COVID, engineering leadership realized that it needed a new approach to drive the next stage of the company’s growth.
Spurred by the upheaval of COVID and the opportunity to transform how the engineering team operated, CloudShare Chief Technology Officer Muly Gottlieb spearheaded an engineering modernization initiative with three goals.
The first goal was to maintain CloudShare’s competitive advantage by continuously embracing the best technologies and methodologies.
The second was to develop a common, data-driven language across leadership functions. “When you go into an executive meeting, everyone knows how to contribute to sales and marketing discussions of funnels and conversion rates,” Gottlieb said. “Historically, the same hasn’t been true of engineering. Non-technical executives are often left out of the discussion because we haven’t developed and socialized the same kind of KPIs.”
Finally, Gottlieb identified an opportunity to focus innovation efforts by synthesizing data from multiple sources – to see the full picture and sift the signal from the noise.
“This was always critical,” says Gottlieb. “But COVID showed us just how much we need this additional intelligence.” Gottlieb suspected that teams across the engineering organization had an opportunity to boost productivity and code quality, but needed data-driven guidance on how to improve.
Energized about the opportunity to modernize and improve engineering at CloudShare, Gottlieb sought out a partner that could help bring new alignment and focus to the company’s innovation efforts.
CloudShare began working with Acumen in late 2020. Within 24 hours, Acumen connected Cloudshare’s GitHub and JIRA data in its platform – and trained machine learning models to link people and projects across systems. By recognizing development patterns from across the team, Acumen quickly surfaced a clear picture of team performance.
As Gottlieb had intuited, the team’s velocity and code quality both showed opportunity for improvement relative to industry benchmarks. The real question was why.
Using a complete, unified view of developer behavior in Acumen – stitched together from GitHub and JIRA – the team identified that overly-large task size was one of the leading causes of both high cycle time (the time from open to merge of a project – read more here) and a high number of bugs.
As team lead Adi Sashkis describes it: “We saw that large task size was hurting us in a few ways. First, developers were much slower to pick up large tasks for review. Second, they (naturally) took longer to review – which led to more merge conflicts, rework, and a higher likelihood of bugs being introduced.”
The team established an engineering improvement initiative designed to systematically break down large tasks into smaller ones.
The benefits of moving to smaller task sizes were immediate. Within a matter of months, the team saw its Cycle Time decreased by 30%, and Bugs Fix Ratio improved by 57%.
And because smaller task size leads to more frequent deployment, the CloudShare team reported a boost in team motivation and focus as engineers began completing tasks on a daily basis.
One team, for example, had a major project that involved taking a legacy part of the application and rewriting the infrastructure. Previously, the project would have been planned in coarse-grain “chunks.” But under the guidance of leadership, the engineering team decomposed it into much smaller-sized tasks. The result? The team delivered the project on time and at an impressive level of quality.
One more important metric is the team’s throughput, Acumen shows 3 dimensions of the throughput metric (1) resolved tickets; (2) merged PRs; and (3) Maker Time, based on these metrics the throughput of Cloudshare improved by 15%.
For Gottlieb, uncovering opportunities to improve the engineering team’s productivity and quality were a major breakthrough. He also discovered that using Acumen helped him both hold his team accountable to engineering best practices – around code review, for example – and introduce engineering KPIs like cycle time to CloudShare’s leadership team.
The benefits of moving to smaller task sizes were immediate. Within a matter of months, the team saw its Cycle Time decreased by 30%, and Bugs Fix Ratio improved by 57%.
And because smaller task size leads to more frequent deployment, the CloudShare team reported a boost in team motivation and focus as engineers began completing tasks on a daily basis.
One team, for example, had a major project that involved taking a legacy part of the application and rewriting the infrastructure. Previously, the project would have been planned in coarse-grain “chunks.” But under the guidance of leadership, the engineering team decomposed it into much smaller-sized tasks. The result? The team delivered the project on time and at an impressive level of quality.
One more important metric is the team’s throughput, Acumen shows 3 dimensions of the throughput metric (1) resolved tickets; (2) merged PRs; and (3) Maker Time, based on these metrics the throughput of Cloudshare improved by 15%.
For Gottlieb, uncovering opportunities to improve the engineering team’s productivity and quality were a major breakthrough. He also discovered that using Acumen helped him both hold his team accountable to engineering best practices – around code review, for example – and introduce engineering KPIs like cycle time to CloudShare’s leadership team.
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