Software development efficiency: key metrics, missteps, and better practices

Software in today’s development landscape

Software development efficiency is the ability to deliver high-quality software quickly while maintaining reliability, security, and long-term maintainability. As software becomes the foundation of modern products and services, improving engineering efficiency has become a priority for organizations across every industry.

Today, success depends not only on building software, but on delivering it efficiently at scale. The methods will change and the technologies will evolve, but most of the work will still be done by humans with the growing support of AI. Everything is becoming code, including data processing and infrastructure.

Business demands more velocity, faster delivery, and the highest quality standards along with resiliency to ensure the competitiveness of its products and services in this digital era. Yet, despite so many lessons learned, and so many of them were very painful, managers tend to repeat the same mistakes over and over again.

This topic is very serious, but I’d like to turn it into an ironic quick guide for inefficiency first, then share some key findings on how to save ‘efficiency’ before it’s too late.

Quick guide for killing productivity and efficiency in software engineering

Apply industrial era rules to software development

Software development is often treated as a production process of code and configuration used to deliver user-facing functionality supported by APIs and data stores. This perspective led to importing industrial-era efficiency models into software engineering without accounting for its fundamental differences.

In those times, worker efficiency was measured by the number of items produced per hour or day. Quality standards eventually followed, so only non-defective items counted. It was a sensible approach for factories and coal mines. Naturally, someone decided it should work just as well for software development.

Let’s apply the same logic to software development. If code is the product, surely the obvious conclusion is to measure how much of it each developer produces. Problem solved—or so it would seem.

That thinking gave rise to measuring lines of code written per developer per day. As surprising as it sounds, this actually happened.

It didn’t take long for developers to adapt. Meaningless lines of code quickly became an easy way to satisfy a metric that had little to do with engineering quality

Some even measured how many key presses the developer did per day, for how long they were staring at the screen, etc. All of which could be easily faked as well with a little help from tools.

Once metrics become a contest between management and developers, everyone loses. Dashboards may look healthy, but quality, delivery speed, and customer outcomes rarely do.

Time vs. Estimation game, how to turn a win-win into a lose-lose

After the initial industrialization phase, waterfall methodologies for project management were all the rage. Project managers were using an infinite loop to manage projects. First, they asked developers how long the given functionality would take to implement, then they controlled the delivery time vs. estimation.

Developers adapted to this system as well. They started to overestimate the amount of work required in order to always be on the safe side, and to avoid unnecessarily and (sic!) unproductive discussions on why the given task was delayed. The “overestimate and code in peace” approach became the norm and is so until this day.

Project managers were satisfied because every task appeared to be on schedule. Meanwhile, anyone looking at the bigger picture could often see the same work being completed two, four, or even ten times faster under different conditions. The catch, of course, is that software projects rarely come with parallel universes for comparison, so hidden inefficiencies remain comfortably invisible.

The same antipattern can be applied to agile methodologies using story points, for instance. The times are different but estimated vs. delivered often results in a natural response of … overestimation.

And overestimation is a great tool to achieve great levels of inefficiency.

More pressure on the development team

The obvious solution, then, is to apply even more pressure. If developers are overestimating their work, simply cut every estimate in half and the problem solves itself… at least on paper.

Unfortunately, that assumes the original estimates were inflated. If they were realistic to begin with, the only practical option left is to cut corners in order to meet the new deadlines.

The result is a rapid accumulation of technical debt, accompanied by growing system instability, weaker security, a poorer customer experience, and increasing regulatory risk.

Consistently pushing developers to deliver faster eventually creates what software engineers call a “ball of mud” — an unmanageable system that becomes increasingly difficult to maintain. Frustration grows, experienced developers leave, and those who remain inherit a codebase full of hidden traps and unexpected dependencies that slow development even further.

Distractions kill productivity

Another very effective way to kill productivity is to provide as many distractions as possible. There’s nothing better than another meeting, another call, or another break in the flow.

A particularly dependable strategy is to place every developer in a large, open workspace designed for constant interaction. Those trying to focus will likely respond with noise-canceling headphones, flexible work arrangements, or by looking for an employer that values uninterrupted time for deep work.

Others may adapt more comfortably to the environment, enjoying the steady stream of conversations, impromptu discussions, and social interactions. If career progression is based primarily on tenure rather than impact, the arrangement can appear to work surprisingly well—at least until delivery deadlines and business outcomes tell a different story.

To summarize, imagine a company trying to solve the problem of too many meetings by scheduling a series of meetings to discuss how to reduce meetings.

In programming, that’s called recursion.

Same project – different goals

Another reliable way to undermine software development efficiency is to let individual objectives drift away from the actual goals of the project.

A developer discovers an interesting new technology and naturally wants to try it. The intention may be perfectly reasonable, especially if there was a promise that experimentation would happen in a separate innovation stream. When that opportunity never quite materializes, the production project often becomes the next best alternative.

The result is a subtle shift in priorities. The project is gradually steered toward a problem that happens to justify the new technology, followed by another tool to support it, and perhaps another after that. Before long, a significant share of the team’s effort is spent maintaining an increasingly sophisticated solution to a problem that did not exist until it was designed into the project. Our guide to inefficiency has found yet another dependable strategy.

People not doing anything at all

This may be the ultimate level of inefficiency. Empty commits, no commits at all, endless formatting changes, or, in more extreme cases, treating one employer as little more than a desk and a laptop while working for another company. It sounds far-fetched, yet it happens more often than many organizations would like to admit.

I once audited a project for an external client where nearly a thousand person-days had been reported, but the outcome was just three web forms. Unfortunately, two of them were unusable because of major defects.

This is often the result of a disconnect between project management and the reality of day-to-day software development.

There’s an old saying that “code does not lie.” Yet many managers never look closely enough to verify whether meaningful progress is actually being made.

The modern version of this problem often appears when DevOps teams evolve into isolated silos. Once visibility is lost, what happens inside those silos can be enough to make even the most experienced CTOs and CIOs start dreaming about a NoOps future.

Quick guide to saving software engineering efficiency, probably

What follows is a practical counterweight to the inefficiency patterns described above, focusing on what actually improves efficiency in software engineering in real environments.

The reason is that the best practices have to be applied in the right context, with the right people, and at the right time. And, that’s something that cannot be simply described in a cookbook-style guide, ironic or not. You may think it’s not fair and I cannot disagree.

“Best effort” approach

Let people do their work and focus on meaningful milestones, such as the next sprint or release. Avoid micromanagement. Instead, help the team connect with the project’s goals, and provide the tools, support, and autonomy they need to succeed.

Take an active interest in the product itself. Use it, explore it, and understand how it works from the user’s perspective. Teams are far more likely to engage with leaders who are genuinely invested in the outcome than those who simply oversee the process.

In most cases, this approach produces better results in both quality and delivery. Admittedly, it also requires managers to become comfortable with having less day-to-day control, which can leave some wondering what to do with the extra time. The usual answer is another meeting, although that may not be the most productive one.

The saying “you cannot optimize what you cannot measure” has its place, but digital product development is driven just as much by focus and motivation as it is by metrics. Teams perform best when they’re solving the right problems at the right time and understand why their work matters.

Management’s role is to create that environment. Align teams around clear goals, demonstrate genuine interest in the outcomes, and remove obstacles instead of adding them. That’s how many of the best digital products and customer experiences are built.

Let them focus on what matters

Removing distractions and unnecessary meetings, while letting people work from home or from a well-designed office, wherever they feel they can be more efficient, is the way to be proficient. And people are not the same, as one environment may work better than the other for different people.

All the activities related to analysis, design, architecture, implementation, testing, and deployment require focused attention. It’s often called “flow”, but if it’s broken by noise and interruptions then returning to this focused state will take a lot of time. This is one of the most common complaints that is very often ignored by managers without a technical background who don’t understand this type of job, and who are lacking empathy at the same time.

Goal alignment and personal motivation

Are my individual goals aligned with the business goals of the organization? How does this feature or technological change relate to the business success of the company? Does it all make any sense? What is my impact? What can I be proud of?

If there’s any doubt about any of those questions, there’s probably a focus problem and a motivation problem.

Knowing what we’re doing, why we are doing it, what is the impact, and what is the positive outcome for end-users, clients, and employees are the critical parts of selling the digital solution to … the internal team first.

Value streams

The state-of-the-art methodologies promote measuring progress with value streams. This is about tracking all the activities to business benefits for the organization.

If this particular feature and the code associated with it are not bringing any value to the customer experience, engagement, and efficiency for sales, it probably should not have been even done in the first place.

Does it sound like LEAN? Yes, but in a new form.

Removing waste is the key and the best first step.

The problem with this approach is that often one technical team activity relates to many goals with minimal impact. And… it’s easy not to notice any impact at all, but thousands of minimal impacts lost in this way may impair the entire digital solution.

So, the rule of thumb is to avoid tasks that are not leading to any goals at all and cannot be mapped to any, thus giving a chance to the others. Features can be tested using modern strategies including feature toggles.

Next steps

Software engineering efficiency depends on applying the right practices in the right context, supported by teams that understand both delivery goals and real-world constraints. It is not about following a fixed set of rules, but about adapting proven approaches to the situation at hand.

Despite using over two thousand words, this topic still only covers part of the reality. I admit that honestly.

Real-world projects and digital programs will always require a dedicated and focused approach that is based not only on the best-known practices but, first of all, in the right context with the right people.

The truth is not in the middle, not at all.

The best approach today is the right combination of LEAN value-based streams, letting people do their jobs at work (focus), discarding the false prophets of agile, and selling the digital solution to your own team first (motivation, work that makes an impact). Take care of these and the customers will follow, attracted by the creative, secure, and well-performing digital experiences.

Get in touch to see how applying LEAN value‑based streams and empowering your team can drive secure, high‑impact digital solutions for your customers.

Rate this article!

Average 0.0 out of 5

Jacek Chmiel

Director of Avenga Labs

Deeply rooted in the software industry, with a trajectory spanning from developer to CTO and CEO, Jacek Chmiel has navigated every tier of the tech stack and every major technology shift.

As a strategic consultant at Avenga, he directs technology strategy for global partners, converting decades of leadership into innovative data products. His work focuses on the frontier of decentralized intelligence: federated learning, agentic AI-driven management, and privacy-first data architectures.