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23 software development metrics to track today

High-performance, engaging, secure apps don't happen by accident. Measure these KPIs to improve the software development process and software quality.

Software development metrics can reveal how an application is performing and how effective the development team is in its work.

IT organizations rely on a range of these KPIs to fully understand software engineers' progress, as well as software quality, such as performance and user satisfaction. The gamut of possible measurements spans four key categories:

  1. developer productivity
  2. software performance
  3. defects and security
  4. user experience (UX)

While an IT organization doesn't need to tabulate every software metric, it should prioritize and track the ones that matter most to its requirements and goals. Scan these 23 software development metrics, and build a set of KPIs for software quality.

Developer productivity metrics

Many ways exist to discuss or evaluate team efficiency and completed work. Productivity metrics enable development managers to run projects better. Tabulate a mix of these software metrics to gauge how far along a project is, levels of developer productivity, the amount of additional dev time necessary and more.

  1. Lead time. Lead time is how long something takes from start to finish. In software development, for example, a project's lead time starts with the proposal and ends with delivery.
  2. Amount of code. Development teams can look at this software metric, also called thousands of lines of code (KLOC), to determine the size of an application. If this KPI is high, it might indicate that developers were productive in their programming efforts. Although, this metric is not useful when a development team tries to compare two projects written in different programming languages. Also, keep in mind that more code doesn't always make for efficient or effective code, which can make for more refactoring work later.
  3. Work in progress (WIP). In a software engineering context, WIP is development work that the team has begun to work on and that's no longer in the backlog. A team can express WIP in a burn down chart. A common tool for Agile and Scrum sprints, these charts display how much work the team has finished and the amount of work left to do.
  4. Agile velocity. To calculate velocity, an Agile software development team looks at previous sprints and counts the number of user stories or story points completed over time. Agile velocity is an estimate of how productive the team will be within a single sprint.
  5. Sprint goal success rate. This software metric calculates the percentage of items the development team completed in the sprint backlog. A team might not finish 100% of the work during any given sprint. However, the team's progress might still meet its definition of done -- the threshold a project must meet for an organization to consider it finished. If the iteration meets the definition of done, it is a success.
  6. Number of software releases. Agile and DevOps teams prioritize frequent, continual software releases. With this KPI, teams can track how frequently they release software, whether monthly, weekly, daily, hourly or any other time frame -- and whether that cadence ultimately delivers enough business value.

Software performance metrics

Software performance refers to quantitative measures of a software system's behavior. Performance metrics gauge nonfunctional attributes -- i.e., how an application performs, not what it performs.

  1. Aspects of software performance. Performance testing might assess the following characteristics of an application:

Other important expressions of software performance metrics include the following.

  1. Throughput. Throughput is the number of units of data a system processes in a certain amount of time.
  2. Response time. Response time measures how much time it takes for a system to respond to an inquiry or demand.
  3. Reliability, availability and serviceability (RAS). RAS refers to software's ability to persistently meet its specifications; how long it functions relative to the amount expected; and how easily it can be repaired or maintained.

Defect metrics

Development teams must understand how applications fail in order to build them better. These software development metrics assess defects and vulnerabilities.

  1. Defect density. At the code level, developers can tabulate the number of defects per KLOC to assess the frequency of defects.
  2. Code coverage. This is the proportion of source code that automated tests cover. The software metric enables testers to pinpoint what areas of the code they have yet to properly test.
  3. Defect detection percentage. This metric is a ratio of the amount of defects found prior to software releases compared to the number found post-release. To calculate the percentage, take the number of defects found pre-release (x) and the amount users encountered after release (y), and then calculate x/(x + y). A high percentage is preferable, as that means a larger proportion of the defects was found before customers used the software.
  4. Technical debt. Technical debt is a metaphor that reflects the long-term effort, as well as temporal and financial costs, of developers not addressing a development problem when it first arises.

15. Morale as a metric

Treat employee or team happiness as another useful indicator of team productivity and success. It just might be as important as any technical metric or software quality KPI.

Stressed or unsatisfied team members can erode work productivity and, ultimately, software performance. Keep stock of numbers like team member turnover, also called employee churn; a lower number likely means employees are satisfied within the organization.

16. Security vulnerabilities. Vulnerability scans identify security weaknesses in an application. The lower the number of vulnerabilities found, the more secure the software will be.

17. Actual security incidents. This KPI counts the number of times a hacker exploits a vulnerability in the software. Track how often these breaches occur, the severity of the attack -- for example, what data was stolen -- and the amount of time the incident lasted.

IT organizations use various averages to calculate the occurrence of software failures or defects.

  1. Mean time to detect. Mean time to detect is an average that indicates how long it takes for a team to notice an issue or bug.
  2. Mean time between failures. This metric is a calculation of how common it is for a program to fail.
  3. Mean time to repair. Mean time to repair is the average that represents how quickly a team addresses failures.

Usability and UX metrics

Users experience and interact with software in different ways. Just as it's difficult to classify people's emotions, it's also challenging to assess their reaction to software. While no single software metric can communicate the entirety of UX, there are a few helpful ones.

  1. UX metrics. Measurements of UX are often qualitative and might include users' emotional or bodily responses, such as how much they trust the software and how their eyes move across a UI.
  2. Usability metrics. Usability measures how well the software enables customers to achieve their goals. Usability can be broken down into smaller components, such as the following:
  1. Net Promoter Score (NPS). This software metric reflects customers' willingness to recommend an application to others. Net Promoter Score is presented as a number range from 0-10. Customers with a score from 0 to 6 are Detractors; 7 and 8 scores are Passives; and 9 and 10 are Promoters.

Next Steps

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