In 1776, Scottish economist and philosopher Adam Smith defined two types of labor: productive and unproductive.
There is one sort of labor which adds to the value of the subject upon which it is bestowed; there is another which has no such effect. The former, as it produces a value, may be called productive; the latter, unproductive labor.
Productivity is straightforward in manufacturing (i.e., more goods in less time = more productive), but it’s harder to apply to the knowledge economy and software engineering.
What should you measure as a proxy for software development productivity? Can you optimize individuals’ inputs and outputs? This post will answer these questions.
What is Software Development Productivity?
Development productivity measures an engineering team’s efficiency against selected metric(s) and identifies elements that impede or accelerate their work and creativity.
By measuring development productivity, managers want to understand better how much work the team can handle to improve planning, plus identify common points of friction (e.g., due to unclear requirements or poor handover processes).
Information and technology silos are the biggest constraints to high software development productivity. A Mattermost survey found that 86% of developers lose 1-8 hours weekly on information lookup. Another 45% are frustrated with tool and stack integration, leading to context switching and troubleshooting, while 25% mentioned that collaboration tools don’t fit into developer workflows.
Interruptions are another problem. Almost a third of an engineer’s time is spent in meetings. This lack of focus time for core work leads to lower completion velocity and revenues for your business.
The best software engineering work happens in the flow state — a state of deep concentration and intrinsic enjoyment. But getting into and staying in the flow is hard. A recent study found that 43% of software engineers experience flow only sometimes and 30% often.
Software engineers cite a variety of common barriers to productivity, including external factors (e.g., how work is planned, organized, and performed) and internal factors like emotional and physical well-being.
The bottom line is that to establish high software development productivity organizations need to improve both ‘soft’ factors like collaboration, team alignment, and leadership, as well as establish proper organizational workflows for project planning, knowledge management, and toolkit integration.
How to Measure Software Development Productivity: Frameworks & Principles
Can software engineering productivity be measured using metrics like ‘line of code’ or ‘story points’?
In 2003, Martin Fowler, co-author of the Agile manifesto, argued that the software industry couldn’t reasonably “measure productivity”. But people still tried, successfully or not.
In 2014, a group of computer scientists invented the DevOps Research and Assessment (DORA) framework. It measures software delivery productivity through four key metrics:
- Deployment frequency: Time required to release new code successfully.
- Lead time for change: Time required to commit new code into production
- Change failure rate: Percentage of deployments causing issues in production
- Time to restore service: Time required to recover from a failure in production successfully
The SPACE framework is another popular methodology developed by GitHub, the University of Victoria, and Microsoft to measure individual developer productivity through five dimensions.
- Satisfaction: Levels of developer satisfaction, efficacy, and burnout.
- Performance, measured by the quality and impact of the delivered software.
- Activity, measured via quantifiable developer activities at different phases of the software development life cycle (e.g., count of build, test, deployment, or on-call participation).
- Communication and collaboration, measured by onboarding time or work integration speed.
- Efficiency measures interruptions or delays that affect the flow state (e.g., the number of handoffs in a process) and the impact of interruptions (e.g., meetings or training).
The SPACE framework recommends choosing a limited set of metrics that span all three levels and capture various aspects of productivity rather than using them all at once.
A recent McKinsey article proposed a new approach: adding ‘opportunities focus’ metrics to those offered by the DORA and SPACE frameworks.
Source: McKinsey
However, technology leaders also vocally criticized the framework. Extreme programming creator Kent Beck and veteran software engineer Gergely Orosz countered that the McKinsey framework is overly focused on software engineering output, not outcomes or impact, and effectively discards half of the software developer lifecycle.
Dan Terhorst-North, an organizational transformation consultant, argued that the framework also effectively discards the supporting work senior engineers often do. “The whole push of the [McKinsey] article is that the best developers should be ‘doing’ rather than thinking or supporting,” he wrote.
Arguably, the best response to the ongoing debate about measuring developer productivity came from Andrew Boyagi, Senior Technical Evangelist at Atlassian:
There’s an unhealthy obsession with measuring developer productivity. Imagine the possibilities if the same amount of time and energy went into helping developers be more productive. Give them what they need, and they will be more productive.
I’m not saying don’t measure, but measuring isn’t going to get you closer to the outcome you’re after. Balance where you put your energy and time.
So, how can you empower people to do their best work? At 8allocate, we practice the following principles.
Measure Outcomes, Not Output
Many software development metrics focus on quantity: the number of code lines, the number of new commits, etc. These metrics are not inherently wrong but don’t always tell the whole story.
A low defect rate looks good on the dashboard, but high-quality code for an unnecessary feature lacks business value. Senior software engineers may spend more time pair-programming with junior staff to help them solve more impactful problems, which boosts the junior worker’s metrics.
Measure outcomes, not output. Quantify the teams’ contribution through dimensions like product quality (e.g., service health, change failure rate, etc.) and business impact (e.g., feature usage, customer retention, user satisfaction, etc).
Measure Teams, Not Individuals
Software is built by teams, not individuals. Agile metrics like sprint burnout rate or velocity can give you a good sense of team performance, allowing you to track progress and forecast the team’s speed in working through the backlog. Both give managers an idea of the team’s pace through iterations and help identify and investigate reasons for friction.
Measuring individual contributions is challenging. Some people may get higher-impact tasks, warranting more story points, while others do important ‘glue work.’
Tanya Reilly, principal engineer at Squarespace and former Googler, pointed out that senior engineers often have to deal with unglamorous non-technical work like “onboarding the junior engineers, updating the roadmap, talking to the users, noticing the things that got dropped, asking questions on design documents, and making sure that everyone’s going roughly in the same direction.”
Focusing on rewarding individual contributions instead of teamwork can make people avoid ‘glue work’ and not develop technical leadership skills. Without strong leadership, no software engineering team can be successful.
Don’t Blindly Optimize Around the Metrics
Metrics provide direction and are important to understand progress, output, and impacts. But as Goodhart’s Law says: “When a measure becomes a target, it ceases to be a good measure.”
By optimizing for one or several metrics, you’ll get unhelpful results. For instance, maximizing successful builds incentivizes developers to focus on relentlessly shipping new features rather than ensuring their high performance in production.
We modify our behavior based on the standards we’re held against. Any metric you try to optimize will compel the person to optimize their score against it, which doesn’t always translate to the best business impacts.
6 Recommended Software Development Productivity Metrics
With the right metrics in place, leaders can identify points of common friction — poor planning, subpar processes, or overly optimistic deadlines — and nip this at the bud. Use the following software development productivity metrics to understand your team’s performance and make smart operational decisions.
1. Cycle Time
What it tells: Time from the first commit to production release. Indicates the speed of shipping a new feature through deployment pipelines.
Cycle time gives a good idea of how long different software releases take, but it doesn’t fully reflect the scope of work done. Pushing four smaller commits per day suggests a faster cycle time. But if breaking down work for developers leads to more toil, you’re not really getting better outcomes.
How to use it: Knowing cycle time leads to better Spring planning and project timeline estimation. A sudden drop in cycle time for a reasonably regular commit likely means your team is blocked or experiences another issue worth investigating.
2. Lead Time
What it tells: Time from when a customer places a request (e.g., for a feature) to when it’s delivered in production.
Unlike cycle time, which gets tracked when the team begins development work, lead time includes all the ‘grunt work’ such as creating software requirements specifications and user stories.
Source: Agile Academy
How to use it: Lead time shows the time needed to go from an idea to a completed feature. When compared to the cycle time, it helps managers understand the time spent on preparatory work — customer interviews, data collection, business analysis, stakeholder management, etc.
Long lead time may signify operational inefficiencies, delaying software engineers from starting development work.
3. Sprint Burndown
What it tells: How much work gets done per sprint in story points or hours.
Ideally, all planned work must be completed by the end of the sprint. Full sprint burnout rates signify an A+ software engineering team and a capable project lead. But it can also mean you’re not committing enough work for each Sprint, especially if your team finishes it early.
Source: Microsoft
How to use it: Sprint burnout charts track progress and detect issues. It’s normal for software engineering teams not to burn down all items every time. But if the team often misses the mark, it can indicate planning issues (e.g., the lead plans too much work or changes the scope). A sudden, rather than gradual, drop in the burnout rate means you’re not sufficiently breaking down work.
4. Velocity
What it tells: Average amount of work completed per sprint, measured in story points or hours.
Velocity gives a good outlook on the team’s performance and productivity. But it’s tricky to get right. Inexperienced managers may push for higher velocity, forcing more work, not better. Others may misuse story points to represent time. Neither is optimal.
Each team’s velocity is unique and reflects their work type. Some have lower velocity but produce complex, impactful features, while others have higher velocity by doing simpler, operational work.
How to use it: Velocity helps estimate a team’s speed through the backlog and helps plan work. It also monitors team dynamics. New teams increase velocity as they improve relationships and processes. Established teams have stable velocity, which allows you to test whether a particular improvement to the software development lifecycle made any impact. Bottlenecks and inefficiencies in development processes can cause a sudden drop in velocity.
5. Feedback Loop
What it tells: The time developers need to get work done vs. the time they spend waiting for feedback.
Software engineers can be blocked by technology (e.g., automated test latency or CI pipeline failures) or people (e.g., code reviewer approvals). Fast feedback loops — instant response from systems and people — help developers work efficiently. Slower feedback loops create interruptions, forcing developers to switch tasks to review notes or troubleshoot issues instead of staying in the flow.
How to use it: Ask your teams where they experience the most friction to shorten the feedback loops. A quick survey can help you identify areas for improving peer review or hand-off processes and accelerating development with technology (e.g., by improving the development environment setup).
6. Mean Time to Recovery (MTTR)
What it tells: Average time the team needs to recover a system after an incident or an outage.
Unplanned downtime is costly. The sooner the system is back to normal, the less damage your organization will experience. MTTR helps assess the teams’ performance during critical events — unplanned outage, critical system bug, or another type of failure.
How to use it: Low MTTR indicates issues with your recovery workflows, but it doesn’t point to the exact cause(s), which can be plenty: Alert fatigue, lack of automated playbooks, staff shortages, etc.
To diagnose the root cause, you’ll need to dig deeper into available data to identify areas for improvement.
How to Improve Software Development Productivity
Metrics are great for diagnosing problems, but you also need to administer solutions.
High software development productivity is a sum of investments, made into operational processes, enabling technologies, and cultural practices.
At 8allocate, we’ve been looking into several areas to make our software engineering teams happier and more productive.
Introduce Developer Portals
Developer portals offer a standard catalog of applications, tools, and APIs for developers to kick-start work. This ‘out-of-the-box’ experience reduces the cognitive load and tool fragmentation. Instead of figuring out which open-source framework to use or test case configurations, developers can start new work with minimal friction. To improve developer experience, 59% of companies plan to introduce application deployment automation, 52% — to add easy access to trusted open-source software, and 46% — to introduce application templates, a Forrester study found.
Some software development teams include Golden Paths in their development portals. Popularized by Spotify, Golden Paths offers tutorials and recommended tools for standard engineering tasks (e.g., building a backend service or creating a data pipeline). By introducing Golden Paths, the Spotify team reduced the time needed to build a simple website from 14 days to less than five minutes.
Establish CI/CD Pipelines
One key DevOps principle is to “Automate as much of the software development lifecycle as possible.” In practice, this means the adoption of continuous integration (CI) and continuous delivery (CD) pipelines — a semi- or fully-automated path that allows for new code to be accepted for release. CI/CD guardrails and checkpoints codify quality requirements, focusing attention on creating quality code.
Smart automation reduces developer time spent on testing and debugging. Software supply chain automation removes constant monitoring, enabling continuous deployments and notifications for irregularities.
Experiment with Coding Copilots
AI developer tools are a hot software engineering trend this year. A GitHub survey found that 87% of developers say AI coding tools help save mental effort on repetitive tasks, and 41% believe they can help prevent burnout.
Another study found that developers complete tasks 55.8% faster when using coding copilots. Early Copilot adopters said developers’ productivity increased by about 10% after a small team trial.
Our software engineers tested several testing copilots. Many noted that AI coding tools helped them maintain a deeper flow state and avoid context switching. We observed a 2X increase in productivity among software engineering teams using AI tools and are working on further integrating AI-powered automation into our workflows.
Final Thoughts
High software development productivity requires changes in workflows, toolkits, and management practices. Discuss barriers with your team. Examine the standard operating procedures and tooling they use to identify areas of improvement. Brainstorm which combination of software engineering metrics could give you a good sense of the teams’ pace, delivery capabilities, and satisfaction with work. Use all of this data to guide your subsequent investments in developer experience and workflow optimization.
Need further guidance in these areas? 8allocate offers strategic advisory in process engineering. With guidance from our senior leaders, you can implement better workflows and improve developer experience. Contact us to learn how you can set up your teams for greater success.
Frequently Asked Questions
Quick Guide to Common Questions
What is software development productivity, and why does it matter?
Software development productivity measures how efficiently engineering teams deliver high-quality software while minimizing bottlenecks. It helps organizations optimize workflows, improve planning, and reduce unnecessary overhead in development processes.
What are the key principles for measuring software development productivity?
Effective measurement should focus on:
- Outcomes, not just output – Prioritizing impact over raw numbers (e.g., lines of code).
- Teams, not individuals – Software is built collaboratively, so team performance matters more than individual contribution.
- Balanced metric use – Avoiding over-optimization around a single KPI that may distort behavior.
What are the most valuable productivity metrics for development teams?
The best metrics for evaluating team productivity include:
- Cycle Time – Measures how quickly code moves from development to production.
- Lead Time – Tracks how long it takes from a feature request to delivery.
- Sprint Burndown – Visualizes progress within an agile sprint.
- Velocity – Assesses the team’s capacity to complete work over time.
- Feedback Loop – Identifies delays in reviews, testing, or deployment.
- Mean Time to Recovery (MTTR) – Measures how quickly teams resolve incidents.
How can organizations improve software development productivity?
Boosting productivity requires:
- Developer portals – Centralized platforms to reduce friction and standardize tool use.
- CI/CD pipelines – Automated workflows that streamline testing and deployment.
- AI-powered coding copilots – Tools that reduce manual coding effort and improve efficiency.
How can organizations prevent productivity loss due to interruptions and inefficiencies?
- Minimize unnecessary meetings to preserve focus time.
- Streamline knowledge sharing with better documentation and developer platforms.
- Automate routine processes to reduce manual overhead.
- Use shorter feedback loops to accelerate decision-making.
