May 24, 2022

Software Development Metrics 101

Table of Contents

Generally speaking, metrics are simply a standard of measurement. In software development, metrics are specifically used to measure the productivity and performance of development teams as they work, as well as the performance of the applications they create, and this includes listing the defects. 

When managing the software development lifecycle (SDLC), particularly in agile software development, software metrics that demonstrate performance levels, performance over time, outcomes/results, and results over time—are relied on for organization, planning, control, and improvement. This data provides vital information that can streamline the software development process and improve security for software and infrastructure. 

Good metrics enable you to understand and improve the progress and performance of DevOps and DevSecOps teams and they help you to more accurately measure user satisfaction once the product is in production.

What to Measure in Software Development 

An awful lot can be measured in the SDLC, but software development metrics and metrics software normally focus on development team progress and productivity, software defects, software performance, user experience (UX), and security. Metrics are extensively used to track patterns and results over time, assess individual team progress, and compare progress and performance between teams.

Metrics are also used to gauge quality and “happiness”—such as customer satisfaction and developer satisfaction. Holistic and high-quality developer productivity metrics offer IT managers keener insight into every aspect of their operation.

In addition to the metrics mentioned above, there are a number of other metrics that can be used to measure the success of a software development project. These include:

  • Time to market: This metric measures the time it takes to go from idea to launch. A shorter time to market can give a company a competitive advantage.
  • Cost of development: This metric measures the total cost of developing the software. A lower cost of development can help to improve the profitability of a project.
  • Customer satisfaction: This metric measures how happy customers are with the software. High customer satisfaction can lead to repeat business and positive word-of-mouth.
  • Developer satisfaction: This metric measures how happy developers are with the development process. High developer satisfaction can lead to higher productivity and better quality software.

The specific metrics that are most important will vary depending on the specific project and the goals of the organization. However, by tracking a variety of metrics, software development teams can gain valuable insights into the success of their projects and make improvements where needed.

Metrics of Successful Software Development

There are metrics and there are metrics. Knowing how to measure progress and define parameters for success is the essence of strategic planning. But when metrics are not chosen well nor viewed in context they can do more harm than good.

An efficient DevOps program does not rely only on metrics and monitoring, it relies on effective and relevant metrics and monitoring and assesses them in context. Numbers alone rarely tell the whole story. KPIs and other software development and performance metrics are not as straightforward as they may seem. For example, speed to delivery is only a useful metric if it is put in context. Does it tell you that there were fewer errors and delays this time than there were previously? Maybe the velocity was the same, but the product was better or more complex in one scenario. Successful metrics are all about context. 

Good decisions require reliable data. How you prioritize, measure, assess, and compare your data will determine its usefulness. Ensure that common agile software development metrics such as KPIs, burndown charts, sprint velocity, sprint quality metrics, lead times, and cycle times are constantly monitored and aim to improve them in every sprint. Proceed realistically by focusing on improving a few key metrics each quarter. With a practical and thoughtful approach, you can define relevant outcome-based metrics that will inspire and guide your teams to reach your KPI targets.

In agile software development, metrics are also used to engage the customer early in the SDLC. Meaningful metrics allow for continuous transparency throughout your project lifecycle, throughout your IT organization, and between your organization and your customers. When customers see the many successes along the way—when they see milestones being met, rather than hearing only about the occasional setbacks, it bolsters their confidence in the project. The accurate data you share increases their trust. And when the data is automatically delivered to your screen—when it is “in your face,”—it is simply not possible to ignore, let alone sweep pertinent information under the rug. Even if you don’t recognize the urgency or risk the numbers pose, someone else probably will. 

The techniques you can use to achieve transparency are simple but effective.

  • Focus on outcomes, not processes.
  • Standardize a minimum set of activities, but encourage local variation and innovation
  • Use automation to mine work products for metrics generation and display.

No project or team should be exempt without a particularly good reason. Cultivate universal transparency by requiring clear and consistent communication of salient information.

Metrics are entirely useful when they are outcome-based. But IT management tends to create numerous metrics that take your eyes off the prize. For instance, the earned value management (EVM) technique is often a distraction since it usually relies on untestable assessments of partial completeness, such as, “percent done.” You know your metrics are serving their purpose, increasing quality, and are outcome-based when they are understood and appreciated by your team members, customers, and senior stakeholders.

KPIs in Software Development 

Metrics are closely tied to key performance indicators (KPIs). Although these terms are often used interchangeably, they are not exactly the same. KPIs are always metrics—or groups of metrics that almost always measure productivity, performance, or results—but not all metrics are KPIs.

The easiest way to distinguish them is to think of metrics as the data you receive from anything you measure and think of KPIs as key measurements aligned to specific goals. Your KPIs are a smaller group—they are your most important (or mission-critical) measurements—the salient data that reflect overall performance over time, alignment to strategy, and achievement of targets. 

If you choose your metrics well and put them in proper context they will always have value, but not all of them will be the critical measurements you need in order to determine if you are reaching your goals.


A good KPI target might be to increase new customers by 20% in the following year. To achieve this target, your performance indicator will be the number of new customers onboarding each month. You will create metrics to capture this data. This KPI clearly defines a desired outcome—an increase in revenue.


A common metric would be the measurement of your website traffic on a daily, weekly, and monthly basis. Such a metric offers insight that enables you to evaluate your strategies to improve outcomes. Metrics such as these are indeed valuable, but they are not necessarily KPIs that relate to specific outcomes. (Although it could be if tying these numbers to specific targets becomes a priority and you have a plan to achieve it.)


Creating a realistic list of doable KPIs helps you reach your priority milestones and accomplish your main objectives. The following four categories will help you define your KPIs:

  • Objective or Target — The goal or outcome you want to achieve. This KPI must align to your measurement KPI, be it a percentage, a raw number, or a qualitative value, such as customer satisfaction. They must be evaluated in the same way. Once your objective is defined, you can map it to appropriate indicators, and, lastly, define your metrics to measure desired outcomes. 
  • Measurement — Be specific about what you want to measure. Performance measures can be structured into four attributes: activities, outcomes, project progress, and targets. Targets represent numbers aligned to dates. Do not bloat your metrics. Use a very limited number of metrics per outcome.
  • Data Source — Define the KPI data source. State where your data is coming from and what calculations are being used so that the KPI is easily understandable to all concerned.
  • Assign an Owner and Frequency — KPIs should have owners and tracking frequency should be assigned. To ensure success, someone should be accountable for pulling the data on a regular schedule, as defined. Depending on the KPI, assessing a metric monthly is often sufficient.

If your KPIs are defined and measured correctly, they will allow you to gauge the efficacy of your strategy; they will put the whole team’s focus on high priority outcomes; they will provide a common language and understanding for measuring performance; as well as accurate data; and they will be verifiable. When goals are achieved, your KPIs have moved from outputs to outcomes.

With buy-in from senior management, KPIs will often focus on efficiency and improvement, technical leadership, customer feedback, innovation, and cost control. There is definitely some overlap in the categories. For instance, efficiency and improvement will affect customer feedback as well as cost control.

DORA Metrics

DORA is the DevOps Research and Assessment team. This Google research group evaluated DevOps practices identifying four key metrics that indicate the performance level of software development teams. DORA metrics are now used by DevOps teams to determine if they are Elite, High, Medium, or Low performing.

DORA found that Elite teams are much more likely to meet or exceed their performance goals. The four metrics DORA uses to make assessments are deployment frequency (DF), lead time for changes (LT), mean time to recovery (MTTR), and change failure rate (CFR). Learn more about DORA metrics here.


IT organizations rely on mathematical averages and means to calculate the occurrence of system failures and defects. To create better software, engineers must understand how, why, and where applications and systems fail. The defects and security metrics listed in the next section below assess bugs, flaws, and vulnerabilities.


Equally important as technical metrics in determining success and productivity is the level of staff-member and team happiness and satisfaction. Unsatisfied and frustrated team members will impact productivity, collaboration, transparency, and, ultimately, software performance and customer satisfaction. And replacing talented team members is both costly and challenging.


Productivity metrics usually measure the performance of people and teams. There are numerous ways to assess efficiency and measure work completed. These metrics are invaluable for managers engaged in continual improvement and development. Use the metrics you need to evaluate project status, developer efficiency, additional development time needed, and more.


Software performance metrics are various measures of the behavior of an application or a software system. These metrics gauge functional quantitative attributes, such as how the software performs, as opposed to what it performs.

Many organizations are challenged by performance-based metrics. Here are some rules to make them simpler. Each metric should link to your strategy, which should always align with organizational business objectives; and each metric should measure performance, not process.


Because each user interacts with software in their own unique way, there is always a challenge when it comes to assessing UX. No single metric can communicate the whole spectrum of UX indicators, but we list a few useful ones in the next section, below.

23 Common Software Development Metrics

IT organizations utilize a broad range of useful metrics to gain insight into their progress, software quality, and user satisfaction. Each organization prioritizes goals and requirements and then selects metrics that best assess progress and outcomes relating to the targets. Their selection usually includes some or many of the metrics listed below.

But remember, the key word here is context. Many of these metrics, when reviewed alone, or out of context, can provide little or no benefit and can even be misleading.


  • Actual security incidents — Number of times a hacker breaches the software. Tracks the frequency of occurrence, the severity, and duration of attack.
  • Code coverage — Proportion of source code covered by automated testing.
  • Defect density — Number and frequency of defects per KLOC.
  • Defect detection percentage — Number of defects identified prior to software releases compared to the number post-release. Related to the DORA metric—change failure percentage or change failure rate (CFR), which measures how frequently code is shipping out with issues (i.e., the percentage of deployments causing a failure in production).
  • Mean time between failures — Calculation of how often a program fails.
  • Mean time to detect — An average that indicates the time it takes to notice an issue or bug.
  • Mean time to repair (MTTR) — Also referred to as mean time to recovery and mean time to resolution. Average time it takes to repair an issue or failure.
  • Security vulnerabilities — Identifies security risks in an application. The fewer vulnerabilities detected, the more secure the software.
  • Technical debt — Long-term consequences and costs of not addressing a development issue when it appears. 


  • Employee turnover, or employee churn, is just one indicator of many that you may have a morale problem brewing. On the other hand, lower turnover signifies higher job satisfaction within the organization.
  • Software developer happiness can be tracked and measured and no doubt these metrics will continue to gain value as leaders map their progress and success to the retention of talented workers.


  • Agile velocity — Examines previous sprints and calculates the number of user stories or story points completed over time. Estimates how productive the team will be within a single sprint.
  • Amount of code/lines of code — Also called thousands of lines of code (KLOC)—determines the size of an application.
  • Lead time — Lead time is duration—how long something takes from start to finish.
  • Number of software releases — Tracks the frequency of software releases— by month, hour, or in any specified period.
  • Sprint goal success rate — Calculates percentage of items the team completed from the backlog.
  • Work in progress (WIP) — Work in progress that is no longer in the backlog.


  • Aspects of software performance — Performance testing might assess scalability, speed, and stability, as well as:
  • Reliability, availability, and serviceability (RAS) — Software's ability to perform, as in meeting its specifications; and how easily it can be repaired or maintained. 
  • Response time — Time it takes a system to respond to a demand or request.
  • Throughput — Amount of data units a system processes in a specific time frame.


  • Net Promoter Score (NPS) — Measures customer willingness to recommend application to other users. Displays on a scale from 0 to 10. Detractors are customers who score 0 to 6; passives score 7 and 8, and promoters score 9 and 10.
  • Usability metrics — How well the software enables customers to achieve their goals. Often broken down to smaller components, such as discoverability, efficiency, memorability, learnability, satisfaction, accessibility, and digital accessibility.
  • UX metrics — Often qualitative. May include emotional or physical responses of users. An example could be how their eyes move across a UI and/or other signs that indicate that the user does or does not trust the software.

Benefits of High-Quality Software Metrics

Using good software metrics is vital to the software development process for many reasons, including the fact that they serve as an indispensable tool for project planning and for improving the software development process, which, in turn, improves software quality—or at the very least, results in far fewer mistakes and delays. Relevant performance and productivity metrics in the proper context identify development team errors (and any other problems that cause roadblocks) by providing accurate timely data on an ongoing basis. Being able to pinpoint a problem makes it easy to apply the correct solution right away. And it’s much easier to fix a problem quickly and effectively when you know when, where, and why it occurred. This drastically speeds up the cycle, reducing delays.

Accurate metrics increase transparency. With shared knowledge, teams and individual engineers are far more likely to own their mistakes and are less likely to point the finger or put the blame elsewhere. This promotes an environment where individuals, as well as development teams and operations teams work better together and continually improve.

Rarely can human beings be completely objective. Decisions can be influenced by emotional bias, past experience, and/or incorrect assumptions, all of which cause you to draw erroneous conclusions or fall short in the critical thinking department. Often you just lack enough data to see the entire picture. Sometimes you don’t have the time to collect and assess your metrics from various tools and sources. Augmenting human decision-making with unbiased automated data delivered to you when you need it ensures that you focus on what matters and removes the guesswork.

Communication of accurate, salient information establishes credibility with stakeholders—not only external customers and internal customers, but senior management, peer groups within the organization, governance groups, and, if separate from development, quality assurance and security teams. It can also reinforce a fundamentally new relationship between the ITO and the rest of the organization. With cycle times reduced and a rich set of performance metrics available, a tighter interaction between technology and business allows the ITO to deliver results to the business and to external customers at a faster pace.

A web-based dashboard, available to all, 24/7, that delivers and summarizes project data automatically, promotes a collective understanding and consistent language to all levels of the organization, from development teams to executives. And communicating the same metrics and information to all teams within the organization streamlines decision-making and drives alignment.

Better Insight with Better Data

Remember that performance metrics are only one piece of the data puzzle. Metrics are also used to gauge quality and morale. This can be the software quality, the quality of the user’s experience, and the quality of your company culture or the zeitgeist of your DevSecOps teams. Happy developers make better products and better products make happy customers. Each area you measure impacts the one next to it and each team affects the other teams they work with. Therefore, data should be evaluated in context.

Using the best tools can help teams establish best practices regarding data during all stages of the software development lifecycle. This is why Harness Software Engineering Insights delivers holistic data garnered and correlated from 40+ DevOps tools and applications ensuring that no aspect of your development experience is ever overlooked or neglected.

Interested in learning more about how Harness Software Engineering Insights can help improve your engineering outputs? Request a demo

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