Value Drivers & Objective Metrics
to Improve Your Software Development

Share on facebook
Share on twitter
Share on linkedin
Share on reddit

Run-through

By GITENTIAL TEAM

There is a long list of challenges software development needs to face today. Think about the rapid technology advancement, the increasing customer demand and time limitations. While scheduling and team communication problems also need to be solved.

C-level decision makers from software development companies often focus mostly on three things. The 3 key things which directly relate to the company’s profitability and reputation.

  • How much can we produce?
  • How fast can we produce it?
  • What’s the quality of what we’re producing?

Whether C-level decision makers focus on it or not, you as a DevOps or software engineering manager probably heavily concentrate on your team’s ongoing development and improvement.

What is the key here?
Have a clear understanding of the progress and the bottlenecks of your developer teams. Be able to identify their strengths and areas needing help.

Well, that sounds very general, but how can I do that? You may ask. Of course you need to know what to measure, identify metrics and KPIs, have benchmarks, and analyze. Then repeat. Still very general.. What metrics should I use? What are the most important software development KPIs to measure? How can I measure them? We have some ideas to help you get to the heart of these questions.

The Four Drivers of Software Development

First we have identified 4 main value drivers of successful software development:

  • Speed,
  • Quality,
  • Efficiency,
  • and Collaboration.

Each is associated with core metrics to make it easier to see how you’re team is faring. We’re also adding many more metrics to dig deeper into your team’s performance so you can see it from every angle. We’ll discuss each of these with a quick look at their corresponding metrics.

Software development value driver framework

• Lead time
• Cycle time
• Velocity
• # of active days
• Coding hours

• Churn
• Technology churn
• Test volume
• Bug fixing rate

• Code efficiency
• Code complexity
• Waste
• # of PRs vs. multiple review cycles

• Responsiveness
• Co-authoring
• Review coverage
• Unreviewed PRs

Objective software development KPIs

Software Development Driver #1 - SPEED

The first pillar is about speed, but it’s not only about that. It is speed in the right direction. The goal is to improve your development speed while maintaining and increasing the quality of your team’s work. Improving speed can be achieved by:

PEOPLE – Building a culture of continuous improvement and facilitating skill mastery.
PROCESSES – Identifying productivity issues and key success indicators.
TECHNOLOGY – Innovating with new tools and automating where possible.

The 5 Main Metrics of Speed

Lead time – In Agile, lead time measures the time it takes for an assignment first entering the queue until the time it takes to complete it, often tracked in project management software like Jira. Lead Time includes cycle time.

Cycle time – time spend from code commit till PR merged.

Velocity – As used in coding, velocity measures the Lines of Code produced in a period of time. It is a useful metric, however, it shouldn’t necessarily be used as a KPI for comparing performance across two or more completely different projects.

# of active days – The number of days in which a developer is involved with a project in writing, reviewing, or testing code; or other project tasks.

Coding hours – Estimated time spent by author on writing the inlier lines by a median speed derived from his/her overall commits (median of speeds for all commits).

Software Development Driver #2 - QUALITY

Speed trumps quality on the basis that the project must eventually be released. While it might be possible to squish every bug, you don’t always have the time for it. The key to improving quality is identifying hot spots to focus on what matters. Correlate code quality information against areas of high churn so you can focus your efforts on files with inadequate coverage or maintainability issues.

The 5 Main Metrics of Quality

Churn – Measures the additions, deletions, and modifications to lines of code (LOC) during a period of time. The goal is to steadily decrease code churn as your code evolves and gets closer to release. It’s the inverse of efficiency.

Technology churn – the churn rate per technology enables evaluation of best-suited technologies for a given project timeline and also helps identifying the company’s echnology tknowledge gaps.

Test volume – A measurement of the amount of code that has been tested vs. implemented. A lack of testing can increase code churn and lead to long-term quality issues.

Bug fixing rate – it is defined as the ratio of the fixed bugs to the closed bugs.

Software Development Driver #3 - EFFICIENCY

Efficiency is largely about working smarter, its productivity with precision – getting things right the first time. This begins when drawing up project requirements but continues in defining each tasking. While promoting efficiency, the goal is to spot developer inefficiency. Inefficiency may be the result of wide-ranging root causes. A developer may be rusty or have little experience with a specific programming language or requirements of a software component. Developers may be overworked, have personal issues, or health problems.

Efficiency metrics let software engineering managers keep an eye on the health of their team members – and their projects.

The 5 Main Metrics of Efficiency

Code efficiency – The percentage of productive code free from defects.

Code complexity – Ties into McCabe Cyclomatic Complexity (MCC) as a measurement of the number of paths through a portion of code. This is typically evaluated by a Control-Flow Graph (CFG), “a representation, using graphic notation, of all paths that might be traversed through a program during its execution.” More paths equate to more complexity and a tendency for more defects making the code costlier to maintain.

Waste – The number of hours “lost” due to unproductive work including errors and code churn.

# of PRs vs. multiple review cycles – the time while PRs are closed but not merges. 

 

Software Development Driver #4 - COLLABORATION

Team collaboration metrics provide engineering managers with an overview of how their team is engaging with code reviews. These metrics help them identify knowledge silos, hot spot code, and where there may be obstacles to their next release. A variety of additional metrics exist allowing managers to dig deeper if and when they find issues in these top five collaboration metrics:

The 5 Main Metrics of Collaboration

Responsiveness – is the average time it takes to respond to a comment with either another comment or a code revision.

Co-authoring – dynamics and productivity of co-authorship

Review Coverage – Reflects the portion of pull requests that have been reviewed by someone other than the developer who actually wrote/submitted the code.

Unreviewed PRs – The percentage of pull requests that have been added to the main code branch without any form of review. These PRs deserve extra attention and review whenever they’re discovered.

The Engineering Intelligence Tool Helping You to Measure & Analyze Software Development

Gitential provides software teams the best analytics and recommendations to build better and more sustainable code and products. We analyze the source code and its evolution in git repositories using unique language diagnostic algorithms, and other statistical models.

The Gitential Guide on How to Reduce the Size of Your Git Repository

The Gitential Guide on How to Reduce the Size of Your Git Repository

Unfortunately, there are no easy ways on how to reduce the size of your git repositories. However, there are quite a few ways to keep your repositories down to an efficient and easily manageable size. Many of them are fairly simple and can be set up fairly quickly. But, they probably won’t help you if you are already exceeding your repository cap on Bitbucket, Github, Gitlab, or Azure DevOps. For this scenario, we can help you identify what to target when it becomes necessary to deal with a large repository.

Read More »

Did you like our content?

Spread the word

Share on facebook
Share on twitter
Share on linkedin
Share on reddit

Subscribe to Our Newsletter

Don't miss our latest updates.
All About Software Engineering Best Practices, Productivity Measurement, Performance Analytics, Software Team Management and more.

Did you like our content?

Spread the word

Share on facebook
Share on twitter
Share on linkedin
Share on reddit

Subscribe to Our Newsletter

Don't miss our latest updates. All About Software Engineering Best Practices, Productivity Measurement, Performance Analytics, Software Team Management and more.