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Development Deep Dive: The Full Guide to Code Quality

Woman looking happily at a screen of high quality code.

We all know how important productivity and speed are in software development, but what about code quality? Code quality can often fly under the radar when looking at KPIs for development team productivity. While balancing high performance in development teams against time and cost is important, poor code quality could be a silent killer for your software’s profitability. Today, we’re taking a deep dive into code quality KPIs, seeing what they are, why they matter, and how you can improve your organizational health and ROI by tracking them.

What is code quality in software development?

Code quality can mean different things to different development teams. At Foreworth, for example, we recognize code quality as a combination of features contributing to the functionality, reliability, maintainability, and security of a software product or system.

At the lowest level, clean, well-written, and cognitively simple code that’s free of unnecessary complexity is good quality code. These features make code easier to maintain or upgrade and improve the overall technical health of software applications. Low code quality, on the other hand, is prone to defects that can hamstring deployment and cripple software in production.New call-to-action

How is code quality measured?

Software applications and systems are built on complex interactions of code. Code quality is often an aggregation of several metrics used to measure sources of risk for your software’s overall health. The more issues there are, the lower the code quality. The 2 best indicators of code quality are:

1.    Number of defects:

The number of defects (or “defect detection rate”) is a popular metric used to track code quality. It also provides insights into your team’s QA and testing efforts. Software testing has a strong impact on your software’s long-term health and functionality. It’s important to bear in mind that simply counting the number of defects in testing may not provide a complete picture of your software’s code quality. A well-rounded look at defects in code should also include:

Defect distribution

This is a measure of the number of defects detected over a period of work (e.g., sprint, month, quarter, etc.) based on how severe they are. Defect distribution offers observers a snapshot of testing performance and the severity of issues detected. This can be used to identify trends in developer performance when taken over time.

Defect density

Defect density can be considered a more refined measure of defect distribution. It’s used to measure the density of defects or bugs identified in areas of the codebase. Popular methods for measuring defect density are:

1) number of defects per functional area of code (also called code modules)

2) average number of defects per 1000 lines of code (KLOC)Formula for calculating dfect density. Number of defects per functional area of code and number of defects per one thousand lines of code.

Change Failure Rate

Sometimes bugs that aren’t caught in development can make it to the deployment stage and wreak havoc. Change failure rates are typically measured as the percentage of total deployment attempts that fail after changes or modifications to code.

Measuring the number of deployment attempts that fail due to bugs or other defects in code is a good way to track development and testing performance over time

Defects found in Production (Escaped Defects)

Defects that aren’t caught in development and get through deployment into production environments are often the most dangerous. Bugs in production typically cost up to 6 times more to fix and can incur severe financial losses when they result in critical failures or expose users to cybersecurity threats.

Tracking the number of defects found in production over time provides insight into the quality of testing and the quality of code produced in the development process.

Code Coverage

This QA metric measures how much of your source code has been covered by unit testing. With higher coverage, there is greater certainty that each functioning unit in applications works as expected. Code coverage can be broken down into:

- Line Coverage

- Decision Coverage

- Branch Coverage

- Condition Coverage

Low code coverage correlates strongly with defects in production, and thus code quality. Measuring code coverage consistently is essential for better planning in software development.

These metrics can be calculated using data from testing reports generated by QA teams and developers. However, this is all dependent on the quality and coverage of testing in your software team. If your test coverage or testing effort isn’t consistently high, the accuracy of defect-based code quality metrics can suffer.

2.    Code Complexity:

While defect and bug detection-based metrics depend on the coverage and quality of testing, code complexity does not. It looks solely at the code underlying software applications and uses this to determine the likelihood of code quality issues.

Code complexity covers several different properties that affect the internal operations of software products. It often involves several metrics that correlate to complexity or complicatedness in code, like:

- Number of logical paths per code module (Cyclomatic complexity)

- Volume of code (usually as total Lines of Code)

- Complexity of relationships between files or modules in an application

- Number of changes made over a period of work

- Number of developers contributing to an area of code

Complicated and complex code can both have a severe impact on development efficiency and productivity. What’s more, areas of high complexity in the codebase are strongly correlated with defects and other issues that can impact code quality. Be sure to check out our deep dive on code complexity for a more in-depth look at the most popular methods and metrics used in the industry.

How do you analyze code quality?

A well-rounded approach to tracking and analyzing code quality requires a combination of both defect detection and code complexity metrics.

The former depends heavily on your software team’s ability to cover test cases, document, and report the results of testing. Therefore, it’s necessary to put the right strategies in place. For example, setting up measures to support higher code coverage and transparency in testing and implementing efficient data infrastructures to capture, record, and analyze the data. To do this, you may need to allocate room in your budget for a software data analyst or for expanding your QA and testing efforts.

Code complexity can also be calculated in-house, but it comes with a high relative cost in time and capacity. A cheaper and often simpler solution could be using software intelligence tools or services. These tools analyze source code and other metrics to provide clear and easy-to-access data on your software development.

Foreworth’s platform, for example, tracks code complexity at every level by analyzing source code in an objective and extensive way. It uses this data to provide deep insights into code quality, development health, knowledge distribution and other high impact metrics to support more efficient and productive software development.book a free demo with an expert

Why is code quality important?

If you’re making your delivery deadlines, you might think that your organization doesn’t need to focus on efficiency, productivity, or even code quality. This couldn’t be further from the truth. If you’re not keeping track of your code quality, you could be losing out more than you know.

To stay competitive in the software industry, it’s common practice for teams to push themselves to the wire, grinding out working software as quickly as possible. While this approach may work in the short-term, it also leads to bigger problems down the line, like:

  • more unplanned downtime
  • increased maintenance costs
  • poor system performance
  • higher risk of security vulnerabilities
  • slower release times
  • lower customer satisfaction and retention
  • increased developer burnout

These issues will ultimately eat into your ROI and jeopardize the profitability of your software efforts. Remember, the immediate gains of hasty development are always offset by the resulting costs of technical debt.

Poor code quality is one of the key sources of technical debt for most development teams. As your code quality declines, more technical debt is accrued. As your tech debt compounds, your team’s productivity and efficiency will also decline.A graph showing difference in performance of teams that prioritize code quality versus those that prioritize development speed.

The more time devs spend figuring out how to work around spaghetti code or putting out fires caused by poor code quality, the less time they spend on actually developing new features or functionalities. Ultimately, this means your team’s ability to continue meeting those critical deployment deadlines will be affected.

The best way to get ahead of your technical debt is to adopt a “measure and manage” strategy for your development efforts. This means you’ll need to:

  1. Start tracking code quality KPIs.
  2. Keep updated on the level of your technical debt.
  3. Make conscious decisions to balance your development efforts with maintenance, refactoring, or rework as needed.

How to ensure code quality

If you’ve been tracking code quality in your development and notice a downward trend, it’s time to dig deeper to get to the root causes. Code quality might decline for several reasons, for example:

  • rapid growth in code complexity
  • inexperienced developer errors
  • developer churn leading to abandoned code
  • project management issues (e.g., tight deadlines, vague requirements, scope creep, etc.)
  • excessive rework due to constantly changing requirements
  • Software development metrics and KPIs can only help to identify where these problems exist. To improve the situation, you’ll need to spend time with your development team to address the issues affecting code quality.

To be more proactive about managing code quality, we recommend the following actions:

1.    Make static code analysis tools part of development

Static analysis tools are fast and inexpensive ways to get a quick snapshot of code in development. They can be used without having to run any code making them fast, and they offer a range of benefits to help improve code quality. With static analysis tools you can:

- Scan your entire code base

- Find weaknesses in your code quickly and easily

- Improve the turnaround time for bug fixes

- Reduce cost and impact of bugs on software

Another advantage of static analysis tools is they can be automated as part of the development process. 

2.    Implement shift left testing

Shift left testing is related to static analysis as it aims to improve the early detection of bugs and issues in the development process. By initiating code quality testing earlier in development, defects can be identified and resolved before they become significant problems. This leads to lower escaped defect rates and improved software reliability. This means less rework and lower technical debt.

3.    Establish and enforce coding standards with style guides

Poor code quality isn’t always about bugs or defects, it can also be about complex or hard to maintain code. Complex code can suffer from code quality issues like:

- Modularity violations

- Circular dependencies

- Duplicated code

- Excessive nesting

Formalizing acceptable coding practices in a style guide can help to curb these unhealthy code features.

Additionally, teams comprised of devs with varying levels of experience, are certain to have a mix of coding and work styles.

One way to ensure that code is easy to maintain for everyone on the team is to implement a shared coding standard and style guide. This document sets out the guidelines for expected code practices to ensure better collaboration and maintainability of code.

4.    Encourage pair coding activities and peer review

Developers on your team may not always be able to communicate their problem-solving efforts effectively. Sometimes, in trying to implement smart fixes or add new features, they create complex code that can be hard to refactor or modify. Pair coding and peer review can help.

Setting up pair coding activities with other devs improves communication and supports knowledge sharing and better collaboration. Pairing more experienced devs with juniors can reduce the number of errors in code, improve code readability, and helps with unifying the coding style used in the team. This leads to higher code quality and better maintainability.

Code Quality is only a piece of the puzzle

Code quality is a huge contributor to software quality. In a complex endeavor like software development, what may seem like a few small bugs or inefficiencies here and there, can have far reaching implications for your development teams and your ROI. As your organization scales up, tracking code quality becomes more and more important. The best way to measure code quality is by combining the QA and testing metrics and code complexity measures we’ve identified above. Even then, code quality is only a piece of the puzzle.

While it certainly plays a big part in mitigating team technical debt there are other key aspects of development that you should keep in mind. Doing it in-house, might be too much to take on for your team, but there are tools that can help. Foreworth, for example, provides a 360º view of your software development efforts from code quality to team productivity. See how we can help you maximize ROI on your development efforts here.


About the author
Juan Pablo González

Working as Foreworth’s Chief Technical Officer, Juan Pablo (JP) manages the company’s technical strategy. With nearly 20 years of experience in software development, he ensures the development process at Foreworth is meeting its keys objectives and technical requirements.

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