Software metrics quantify the software development process. Project managers track metrics to manage workloads, schedule deliverables, and tag performance. Tracking software development metrics also helps to improve efficiency and measure the ROI of the project.

Developers and project managers use different metrics. Here are the most popular ones.

Lines of Code (LOC)

Lines of code are one of the earliest and most basic software development metrics in circulation. It counts the number of lines that ends with a return, to find out the length or size of the software. Some developers consider each logical argument a line of code, to avoid “dead code” or comments.

Reduce the line of code and increase the number of iterations to improve user experience.

Agile Software Development Metrics

Developers using agile or lean approaches often track lead-time, cycle time, team velocity, and open/close rates. These show the health of the development.

Lead-Time

Software development is a time-consuming task. Developers need time to design the concept, code, aggregate resources, test and release the software. Lead-time is the time taken to develop the ready-to-use software, from the idea stage. In today’s fast-paced world, the focus is on reducing lead-time as much as possible. With technology and business needs changing by the day, a long lead-time could make the software obsolete or irrelevant by the time it is ready to use.

Agile development approaches, which spread the development across cross-functional teams, cut lead-time.

Cycle Time

Software always changes to factor in bugs and changing requirements. Cycle time is the time taken to change the software system and deliver such change into production.

Developers strive for short cycle time, to ensure their software remains relevant. The continuous delivery approach reduces cycle times to minutes or even seconds. In comparison, the cycle time of traditional software development approaches extends to months.

Team Velocity

Team velocity or sprint is the “units” of software the team completes in a typical iteration.

Sprint works best as an internal metric, to plan iterations. It fails as a benchmark of satisfactory progress, or as a yardstick of success.

Open/Close Rates

Open and close rates show the number of production issues reported and closed within a specific time.

The specific numbers in the open and close rates are irrelevant in the wider scheme of things. Project managers rather look at the general trend to understand if any issues plague the development. The open rate remaining within manageable limits, and close rates matching open rates is routine.

A high open rate and a low close rate across a few iterations may mean a higher priority to new features and a lower priority to production issues. This means the development team focusing on reducing technical debt, or the person who knew how to fix issue having quit.

Production Metrics

Time-to-market and hyper-efficiency are the lodestones of most business processes today. Time is money in today’s fluid and competitive business environment. Production metrics measure the work done. It allows project managers to quantify the efficiency of software development teams.

Active Days

“Active days” measures the time a developer spends in coding, excluding the time spent in planning and administrative tasks. This metric highlights interruptions and the hidden costs of disruptions. A developer spending less time to code and more time for non-coding activities means something wrong in the ecosystem.

Assignment Scope

Assignment scope is a productivity metric. It highlights the quantum of code a programmer maintains and/or supports in a year. It comes handy to plan the developers required to execute a project. Business managers and HR use this metric to compare teams.

Code Churn

Code churn is the number of lines of code a programmer or a team adds, modifies, or deletes in a specified time. High code churn means something wrong with the clarity of the project. It may also mean the developer indulging in trial-and-error methods. A software developer with a low churn may be highly efficient.

Impact Metrics

The three common impact metrics are Mean time between failures (MTBF), mean time to recover/repair (MTTR) and Application crash rate (ACR).

Perfect software exists only in theory. It is improbable for any program not to fail, ever. Developers try to save data when the program crashes and ensure instant recovery.

MTBF and MTTR quantify how well the software recovers and preserves data. Application crash rate is the ratio of the number of times an application fails to the number of times used. A high crash rate means an unstable program.

Changes in one place trigger inadvertent changes in other places. These ratios unearth such changes and help developers quantify the impact of each change.

Security Metrics

Many developers and business managers overlook security until too late and pay the price.

Security manifests as tight coding, comprehensive testing, and overall high quality of development.

Endpoint Incidents

Tracking the endpoints with virus infections over time makes explicit vulnerabilities. Project and business managers increase security measures, such as encryption, as countermeasures.

MTTR (mean time to repair)

Mean-time to repair is the time between the discovery of a security breach and deploying effective remedy.

A small MTTR means the developers understanding security issues better. They find effective fixes quickly. A high MTTR increases the chances of breaches, as it means users working with vulnerable software. Smart project managers invest to build competencies to reduce MTTR.

Modern operations-monitoring software gathers detailed metrics on individual programs and transactions. Plugging a source-code scanner into the build pipeline generates reams of objective metrics. These enforce coding styles, flag anti-patterns, show outliers and unearth insightful trends. But such metrics are useless unless the project managers have set up proper ranges for alerts and triggers.

Software development metrics aid planning and show a general direction of progress. A note of caution though. Many teams, engrossed in improving software development metrics get distracted from usability and customer satisfaction. When measuring the work becomes more important than doing the work, analysis-paralysis occurs.