Statista expects the software market to hit $800 billion in revenue by 2027. But even though the industry keeps growing steadily year after year, not all players on the market will thrive.
There are multiple reasons why, but we’re going to focus on software quality.
As with other products on the market, software can be good or, well, not so good.
Your clients will have final say, but you’re accountable for what you sell. And you can monitor software development quality metrics to reduce the odds of delivering low-quality products to your clients.
Those metrics help you figure out if your development team is on track to building better software in a better way. And with actionable insights, you can make more informed decisions to pinpoint where your team members need to improve.
We’ll cover the quality metrics you’ve got to track for software development, explain the factors underlying each metric, and suggest solutions to help you understand and improve those numbers.
We’ll also show you how time tracking can lead to better software quality.
Software development quality metrics are a set of criteria for assessing the quality of software products.
They’re a compass you must rely on while developing software solutions. But most importantly, those metrics quantify aspects of software quality that you could find hard to evaluate for being too abstract.
For instance, how would you assess your team’s source code quality? Would you judge it by the opinion of a code reviewer? There’s always a subjectivity element to a code review performed by a team member — even when they’re senior developers or software architects.
Luckily, quality metrics are objective. They take biased conclusions and uncertainty away from the evaluation of software quality.
And there are different types of quality metrics for software development, with each type addressing distinct dimensions of software quality. For instance, some metrics enable you to make conclusions on how good the source code is and how well-designed the user interface is, whereas others measure the effectiveness of your software testing process.
After all, the software development life cycle includes different stages, roles, and activities, each with its concerns. But all together, stages, roles, activities, and quality metrics drive high-quality software.
First of all, software development quality metrics are pivotal to the functionality of any software product.
Incorporating quality metrics in your software development process helps guarantee your products will work as expected. Just keep the metrics in mind while designing, coding, and testing your software solutions.
For instance, testers rely on quality metrics to check their progress in each software project. Those numbers also assist project managers in knowing if each product meets all its user requirements and is ready for release.
Additionally, because of quality metrics obtained from user feedback, project managers keep a close eye on frequently reported issues. And the entire team takes action to provide software updates and new releases that fix those issues.
Other quality metrics focus on product responsiveness. That’s how quick a software system responds to user interaction, such as a button click or text input.
Finally, some quality metrics measure the usability of your products: how easy it is to work with them, and whether the user experience is positive or falls short of users’ expectations.
Above all, the data you get from quality metrics draws attention to improvement areas. Whether that’s a shift in coding standards or a process enhancement, the numbers help you figure out the course of action. They help you prevent bugs, maintain the software more easily, and make the source code clearer and more efficient.
As a result, you’ll boost the quality of the software, get better customer reviews, and increase customer acquisition and retention levels. At the end of the day, aiming for excellent quality metrics in software engineering brings in loyal, repeat customers.
There are (of course) more metrics you might want to track than the ones we selected for this article, but these are a great starting point.
Apply each one at the appropriate point in the development life cycle, but keep them all in mind throughout projects.
Your goal is to understand the quality of the product so team members can build better software over time.
Total code churn is the number of lines of code added, modified, or deleted from a codebase during a time period.
Although developing and maintaining software is all about changing a codebase multiple times, doing it too often might indicate quality issues. That’s because, as a rule of thumb, the more changes, the higher the probability of introducing bugs into products.
It doesn’t measure quality per se.
But high churn could correlate with other quality metric issues.
Software architects and programmers structure modern systems in modules, with each module depending on others to function. But then, when coding, one mistake in a module might end up in another module’s failure.
Also, too many new lines of code might indicate you’re in the early stages of coding. But they might as well indicate you’re not reusing code, which affects the software’s performance. For instance, not reusing code may take products longer to load the resources they need to work properly.
Last but not least, once you release the software, a high code churn might indicate an error-prone architecture that needs refactoring. For instance, the architecture might be hard to test or scale.
Formula: [lines added] + [lines modified] + [lines deleted] in [time period]
Example: 1,000 lines of code in a month
As you might have guessed, “crash rate” is the frequency at which a software product crashes. And when that happens, end users just can’t operate the product at all.
Crashes are unexpected errors, like a defect testers didn’t pick up on, or a sudden system overload. They occur while the software is running and terminate its execution abruptly. Sometimes, data gets lost in the process.
Critical system companies — such as those that develop airline traffic control systems or stock exchange systems — focus a lot on this metric.
A high crash rate is a red flag for errors in the product (usually severe ones). And the underlying quality issues might range from insufficient quality assurance to poor user validation.
In other words, the crash rate doesn’t indicate exactly what you must do to lower the number. Instead, you must investigate the exact root cause for the crashes.
But, if code is being introduced that leads to crashes in production, that means it’s slipping past whatever QA or unit testing process you have in place.
That’s a problem.
Most issues with QA stem from a few problems:
If it’s the last problem, you’ll need to do a deeper dive on the overall QA workflow. There may be miscommunication or the QA guidelines may be outdated and not reflect the current state of the software.
If it’s a matter of resources or planning, you may want to revisit how your team scopes and commits to work.
How are you currently scoping and planning your user stories or product requirements?
How do you estimate work timelines and effort?
Don’t wing it.
Whenever possible, integrating historical data like time tracking records from analogous projects, user stories, or work items, can help your team better understand what they’re biting off — and that they can chew it properly.
This means your project won’t be crunched when it comes to QA.
Rewinding one step further, you may want to consider which team (or who) is responsible for introducing the code that is causing the crash rate. Is it a repeated issue?
Although the problem should be identified in QA, it may also be stopped by understanding how and why the problem was introduced during the development process.
Formula: [number of crashes] / [unit of time]
Example: One system crash per month
This quality metric evaluates the amount of time a system is up and running. It compares the system’s uptime against its downtime over a time period. And considers that a system that’s available is as fast as expected and doesn’t crash unless unanticipated conditions happen.
If you prefer, system availability is the probability of a software product not to become unavailable when users need it.
A high availability means more uptime than downtime, which means users rarely lose access to the system. In that case, the released product contains no errors or only a few of them, requiring few corrections to its codebase.
System availability is an essential quality metric for software development companies that build products that must work around the clock.
Formula: uptime / (uptime + downtime) * 100, with uptime and downtime in hours
Example: A software product that’s up and running nine hours out of 10 has a system availability of 90%.
Defect density is the number of defects in a software product compared to its size. This means it’s a relative number.
Defects are errors found by testers before the product’s release. They represent unsatisfied user requirements. And if testers don’t detect them early enough, defects originate failures in the hands of final users.
This metric measures the code’s quality and gives you input for estimating software correction efforts. A high-quality code requires few corrections and is easier to maintain, scale, and evolve.
Don’t forget to offer your team members the opportunity to learn from defects they coded or didn’t detect during testing. That’s how they continuously improve their code and test practices, ending up getting better at their jobs.
This metric is often expressed as the number of defects per 1,000 lines of code.
Formula: [number of defects] / ([total lines of code] / 1,000)
Example: Ten defects in a total of 20,000 lines of code equals a defect density of 0.5 per 1,000 lines of code.
This quality metric is the average time it takes for your team to detect errors in a software product.
A low MTTD indicates that, usually, your team is quick at finding errors. And the faster they find them, the faster they fix those errors. As a result, the system isn’t down for maintenance for too long, minimizing the impact of defects or failures on end users.
On the other hand, a high MTTD means your team must spend more time on quality assurance activities. It also means you need to allocate more resources to the product maintenance stage.
Although defect discovery and ticket management processes influence a product’s MTTD, the quality of the code has the same impact. Here’s why: A well-structured code is easier to navigate and debug, which is step number one to fixing an error.
Ask your team members to collect data on all errors, such as the day and time when they occurred and when someone reported them. Additionally, you can have the support of a time tracking system for your team members to log the time they took to fix the errors.
Formula: [total time to detect all errors during a time period] / [total number of errors], with [time to detect an error] = [day and time when it was detected] – [day and time when it occurred]
Example: In August 2023, an app produced 20 errors with a total detection time of 1,000 minutes, which equals an MTTD of 50 minutes.
This is the average time between two system failures. Those are errors found after the product’s release, which originate, for instance, from an undetected defect.
Failures can be as severe as a crash, but they can also be the software not doing what end users expect it to do.
Of course, the higher the MTBF, the better. It means the product is more reliable, which is essential in industries such as healthcare and aeronautics.
Addressing a problematic MTBF rate can be a bit more complex.
The first question to ask if you’ve seen an uptick (decrease) in MTBF:
Are the failures caused by one problem or multiple issues?
It’s possible that a single failure point was introduced in a recent release, which caused a spike in failure frequency and a decline in MTBF. Or perhaps there was nothing new introduced, but a change in user behavior exposed a previous problem that is now creating more instances of failure.
If it’s either of these two issues, it’s hopefully a contained scenario that can be addressed by way of a normal testing and bug fix workflow.
Perhaps the more sinister scenario is that your MTBF is getting worse because the development team is introducing a higher number of issues that are then causing a higher number of failures.
Gulp.
This means there’s perhaps a more systemic problem to address.
Why is the team introducing more issues than before?
As we discussed when unpacking problems with Crash Rate, if there’s an uptick in the number of issues or bugs being introduced in live code, that usually stems from a breakdown in the team’s workflow.
In many cases, it’s a product of poor planning (user stories not properly scoped, work gets rushed, QA only has 2 hours to test).
This is the time to address the process itself. Look into how your team is creating work estimates and what safeguards you can implement to prevent over-scoping that leads to cascading problems with QA and software quality.
Formula: [total execution time] / [total number of failures]
Example: A software that’s been executing for 3,000 hours and failed 15 times has an MTBF of 200 hours, which means it fails on average once every 200 hours.
This quality metric is the average time it takes your team to resolve an error on a software product after someone discovers it.
Generally, you calculate this as the number of hours or minutes that elapse between the issue being detected and when it’s resolved. But you generally only count the minutes or hours that fall within normal work hours. (i.e., You wouldn’t count time elapsed on nights or weekends.)
Generally, a low MTTR means your team repairs errors quickly.
But, of course, it depends on the severity of the error and the expertise of your developers.
Minor errors tend to take much less time to resolve than critical ones. And senior programmers as well as software architects tend to take less time to resolve an error.
To address issues with MTTR, be sure your team is dedicating enough time to structuring your codebase according to the industry’s best practices. Keep perfecting your internal coding guidelines and documentation.
Also consider implementing new processes or systems to more quickly diagnose problems so they can be resolved more quickly.
Formula: [total time from discovery to resolution] / [total number of repairs]
Example: If 2880 total minutes elapsed between the discovery and resolution of 96 errors in a product over the last three months, then that product’s MTTR is 30 minutes for the quarter.
Customer satisfaction is a number that represents the way customers experience your software product. And you get to it by collecting and analyzing customer satisfaction survey data.
Usually, one question in those surveys is about the overall satisfaction of customers with your product. They must give an answer on a five-point scale, from “extremely satisfied” to “extremely unsatisfied.”
This quality metric gives you the users’ perception of your product’s overall quality. The higher the CSAT, the better that perception.
Formula: [total number of satisfied customers] / [total number of customer survey answers] * 100, with satisfied customers being those who rated their satisfaction “extremely satisfied” and “satisfied”
Example: If 53 of 100 customers rated their satisfaction “extremely satisfied” and “satisfied,” then your product’s CSAT is 53% or just 53.
This is the average time it takes for your team to repair cybersecurity vulnerabilities in your software product.
The root cause of those vulnerabilities is usually low-quality code. And a low number means end users are less exposed to potential data breaches, financial losses, and sudden interruptions of service.
Similar to the MTTR, the mean time to remediate a vulnerability depends on how well your team implements coding best practices. But in this case, the best practices they must always monitor, even after the product’s release, are cybersecurity ones.
Formula: [total time between discovery and remediation of vulnerabilities] / [total number of vulnerabilities] in [time period]
Example: If a development team spent 20 hours repairing two vulnerabilities in a product last quarter, then the product’s mean time to remediate a vulnerability is 10 hours a quarter.
This quality metric evaluates the amount of your product’s source code for which there’s a unit test.
Those are tests run by programmers in the development environment they used to build the product. And the goal is to find bugs in the codebase as early as possible and prevent system failures down the line.
For that, programmers must cover every line of code with a unit test. For instance, unit tests must cover all the conditions in the product’s use cases.
And if your product’s code coverage is high and all the unit tests passed, the code’s quality is considered high. That translates to high-quality software.
Formula: [number of lines of code tested] / [total number of lines of code in the codebase] * 100
Example: If a codebase has 10,000 lines of code and unit tests cover 9,500 of those lines, then your product has a code coverage of 95%.
No one said that prioritizing software quality while meeting deadlines is easy. But often, poor time planning is the reason behind quality issues. It forces development teams to rush product releases and cut quality assurance short.
Time tracking can lead to better quality metrics. It improves time and budget estimates and enables you to plan projects with more precision. That’s what you need to allocate resources optimally and manage clients’ money responsibly.
A historical record of time logged by team members in a time tracking app enables you to put accurate timelines together.
It also gives you time data that sheds light on things like:
The insights from logged time have a tremendous influence on the overall quality of software products. And your software development quality metrics will tell that story over time.
To paraphrase an old saying, “the best time to start monitoring and improving software development quality metrics is years ago. The second best time is now.”
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