61 posts tagged with "engineering-metrics"

Sleuth shipped one of the cleanest DORA implementations on the market. Then in late 2024 it was acquired and folded into a larger DevOps suite, and by 2026 the standalone product feels less actively developed than the platforms around it. That's reason enough for many teams to shop around — not because Sleuth is bad, but because betting your delivery telemetry on a product that's no longer the parent company's headline matters.

This is not a hit-piece. Sleuth's deploy-correlation model still beats most competitors. But if you searched "Sleuth alternative" you already know the deal: you want options. Here are 5 — what each does well, what each gets wrong, and the honest pick for each shape of team.

Every engineering leader asks the same question: how much time do developers actually spend writing code?

Microsoft Research found that developers spend only 30-40% of their time writing code. A 2019 study by Haystack Analytics suggested closer to 2 hours. Our own IDE heartbeat data across B2B engineering teams confirms a median of 78 minutes per day.

Here's what the data actually shows and why it matters.

Forbes Kazakhstan dedicated pages 104–107 of their April 2026 issue to engineering intelligence — and to PanDev Metrics specifically. The article, titled "Доверься «большому брату»" ("Trust the Big Brother"), explored how data-driven development management is gaining traction across Central Asia and beyond.

Rather than republishing the piece, we want to highlight the parts that matter most: what our clients actually said, what the numbers show, and where the industry is heading.

Fred Brooks warned in The Mythical Man-Month (1975) that measuring programmer productivity by volume of code is a trap: adding more code isn't the same as adding more value. Fifty years later, some organizations still equate lines written with work done. The SPACE framework (Forsgren et al., 2021) explicitly cautions against single-dimensional activity metrics — yet the need they address is real: how do you measure whether your engineering team is delivering?

The answer isn't another vanity metric. It's a composite signal we call the Delivery Index.

"This should take two days." Three weeks later, the feature is still in progress.

Steve McConnell, in Software Estimation: Demystifying the Black Art, found that software projects typically overrun initial estimates by 28-85%. Brooks's Law from The Mythical Man-Month explains part of the reason: complexity grows non-linearly with scope, and adding people to a late project makes it later. The PM is frustrated. The developer feels guilty. The roadmap is fiction. And the entire organization has quietly accepted that engineering estimates are unreliable.

This isn't a people problem. It's a measurement problem. And it's fixable.

The Deloitte Global Outsourcing Survey consistently identifies "lack of visibility" as one of the top reasons outsourcing relationships fail. Your client pays for 160 hours per month per developer. But deep down, they wonder: were those really 160 hours of productive work? This single doubt has killed more outsourcing contracts than missed deadlines.

The problem isn't trust — it's the absence of proof.

The Deloitte Global Outsourcing Survey found that lack of transparency is the primary driver of client dissatisfaction in outsourcing relationships. Two companies pitch the same client. Same tech stack, similar rates, comparable portfolios. One says: "We deliver quality work on time." The other says: "Here's a live dashboard showing exactly what your developers are doing right now." Which one wins?

In 2026, the answer is obvious. Transparency isn't a nice-to-have — it's the differentiator.

Research on context switching shows that it takes an average of 23 minutes to fully regain focus after an interruption. Now multiply that by five projects, each with its own Slack channel, Jira board, and stakeholder expectations. You're an outsourcing PM managing five projects for five different clients. Each client thinks their project is your top priority. And every Monday morning, you spend the first two hours trying to remember where each project left off on Friday.

Sound familiar? This is the multi-project management problem — and it's the defining challenge of outsourcing project management.

McKinsey's research on developer productivity found that software engineers spend only 25-30% of their working hours on active coding. The rest goes to meetings, planning, waiting, and context switching. In outsourcing, where every hour has a direct revenue implication, this split matters enormously. Your company has 40 developers. You bill clients for their time. But how much of each developer's available time is actually billable? If the answer is "I'm not sure," you have a profitability blind spot that could be costing you hundreds of thousands of dollars annually.

Developer utilization is the single most important financial metric in outsourcing. And most companies measure it wrong — or don't measure it at all.

Staff augmentation is now the fastest-growing segment of the outsourcing market, according to the Deloitte Global Outsourcing Survey. Yet the model introduces a paradox that most buyers discover too late. You've augmented your engineering team with external developers. They attend your standups, push to your repos, and bill you monthly. But when the invoice arrives, you have an uncomfortable thought: "I have no idea how these people actually spend their days."

You trust your in-house team because you see them in Slack, in code reviews, in the office. The augmented developers? They're a black box. And that black box is costing you tens of thousands of dollars a month.