What productivity metrics reveal before buying a software sompany

Productivity metrics will not tell an investor whether a software company is "good" or "bad". They are more useful than that. They show how the company actually turns engineering effort into usable business progress.

That matters before an acquisition because software companies often look healthy from the outside. Revenue is growing, the roadmap is active, and the team is shipping to a real customer base. But the operational reality can be different once you look at how work actually moves through the technology organization.

A company can be delivering a lot and still be accumulating risk.

This is why productivity metrics should be part of software due diligence. Not as a scorecard to rank developers, and not as a simplistic "how many tickets were closed" exercise. The useful question is whether the engineering system can keep producing value after the deal closes, after leadership changes, after integration starts, and after the easy roadmap items are gone.

Research from DORA is helpful here because it focuses on software delivery outcomes, not just activity. Its current delivery performance model looks at how quickly changes move through the system, how often software is deployed, how often deployments fail, how long recovery takes, and how much deployment work is unplanned rework. DORA also notes that these metrics can act as both leading and lagging indicators for organizational performance and team well-being.

For an investor, that gives a different view of productivity. A team with frequent releases and low failure rates may have a delivery system that can support growth. A team with long lead times, high change failure rates, and repeated emergency fixes may be carrying operational debt that is not visible in the financial model yet. The point is not that slower teams are always weak or faster teams are always strong. Context matters. DORA itself warns that these metrics should be applied at the application or service level, because comparing very different systems can be misleading.

That warning is important in due diligence. Productivity metrics need interpretation. A regulated platform and a fast-moving SaaS product shouldn't be judged by the same raw numbers, and neither should a legacy system that's still load-bearing for the business. What matters is whether the metrics make sense for the business model and product maturity, and whether they line up with the promises being made in the investment case.

This is also where activity metrics can mislead. A high number of commits or pull requests can look productive, but activity alone does not show whether the work improved the product or simply created more review and coordination overhead. The SPACE framework, developed by researchers from Microsoft Research, GitHub, and the University of Victoria, makes this point clearly: developer productivity cannot be reduced to one metric, and activity should not be used in isolation to reward or penalize teams.

In an acquisition, that distinction matters because inflated activity can hide fragility. A team may be busy simply because the architecture is difficult to change, closing tickets while customer-facing defects quietly increase — and the senior engineers who'd normally be building new capability end up spending their time repairing what's already there instead.

AI-assisted development adds another layer to this. The market is clearly moving toward measuring engineering productivity more seriously. Atlassian's 2025 agreement to acquire DX for $1 billion is a useful signal: productivity analytics is no longer a niche concern for engineering managers; it is becoming part of how companies think about AI investment and developer experience. Atlassian described DX as combining qualitative and quantitative data to show where developer flow breaks and where investments are actually moving the needle.

But AI also makes productivity harder to read. A 2026 systematic review of 39 peer-reviewed studies found that LLM assistants often accelerate development and reduce time spent on routine tasks, but the evidence on code quality is still mixed and context-dependent. Another 2026 paper on GitHub Copilot adoption found that AI-assisted coding increased output, but also increased rework, with experienced developers reviewing 6.5% more code and showing a 19% drop in original code productivity.

If a target company claims AI has improved productivity, the gain and the cost usually show up in different places. Delivery might get faster while quality holds — or it might get faster because review queues are quietly absorbing the difference, with senior engineers doing more of the checking than the building. Technical debt can increase even while output looks better on paper.

Avalia's view is that productivity metrics are most valuable when they connect engineering work to business risk. They should help investors understand whether the company can scale, integrate, modernize, and keep delivering without relying on a few people holding the system together.

Before buying a software company, a few practical signals tend to matter most: how long it takes to move a change from idea to production, how much delivery work is planned versus reactive, which systems slow down the roadmap, where institutional knowledge actually sits, and whether defects are decreasing as output increases or simply being pushed into later remediation.

These questions do not replace financial due diligence. They improve it. A software company's valuation often assumes future delivery: new features, product expansion, integration synergies, AI efficiency, platform scalability. Productivity metrics show whether the technology organization is capable of supporting those assumptions.

If the metrics are strong, they can support confidence in the investment thesis. If they are weak, they do not automatically kill the deal. They help price the work that comes after the deal — the work of untangling whatever the growth numbers were quietly resting on.