Optimize DevOps through Performance Analytics

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Run-through

Release Notes: New Account Navigation, Upgraded Dashboard Overview + More

How do you feel about your process for onboarding developers? How do they feel about it? You can ask them. You can also examine your first-year developer turnover to get a sense of its effectiveness, too. In our last post, Why Software Developers Leave, we tackled turnover. About 40% of developers leave in their first year. Of these, 43% leave in their first 90 days. Plus, turnover for tech startups always runs high. So, let’s examine steps you can take when onboarding developers to help your them settle in easier and become more productive faster.

Read More »

Onboarding Developers: 9 Tips for a 90 Day Program

How do you feel about your process for onboarding developers? How do they feel about it? You can ask them. You can also examine your first-year developer turnover to get a sense of its effectiveness, too. In our last post, Why Software Developers Leave, we tackled turnover. About 40% of developers leave in their first year. Of these, 43% leave in their first 90 days. Plus, turnover for tech startups always runs high. So, let’s examine steps you can take when onboarding developers to help your them settle in easier and become more productive faster.

Read More »
WhySoftware Developers Leave

Why Software Developers Leave

Understanding why software developers leave positions engineering managers to tackle one of the scariest industry-wide metrics – Turnover. It’s a persistent, recurring, expensive issue. There are several ways your company can avoid and mitigate turnover. This includes being aware of turnover triggers and the reasons that go with them. Developers at risk of leaving show plenty of warning signs that you can casually observe and track with software development performance analytics. So, let’s get you up to speed on what to watch for and things your company can do to keep your team together longer.

Read More »

By GITENTIAL TEAM

Relentless efforts to optimize DevOps recently hit an unmovable object – and won! For the uninitiated, DevOps aims to speed up software development releases. It engages in a perpetual cycle of improving people skills, processes, and technologies.
 
Software released with a major bug today can be fixed tomorrow thanks to DevOps. Even so, The Developer Coefficient by Stripe indicates, that software developer inefficiency may be as high as 31.6%. With this in mind, let’s examine how you can further optimize DevOps with automated performance analytics.
Before going too far, let’s take a moment to recognize a pretty grand achievement by the collective DevOps community. For years, the US Food and Drug Administration maintained a very slow, cumbersome, and costly approval process for medical apps. A bad medical app puts lives at risk. Previously, if a medical app requiring FDA approval failed its review, it could take 7 to 9 months to be reviewed again.
 
Tasked with responding to the broader healthcare pains in the United States, the FDA started to modernize. This led to the 2019 Software Pre-Cert Pilot Program and the Digital Health Innovation ActionPlan. Here, the FDA is aligning its processes around continuous product development. The focus shifts some from the product to how the company developing it handles the entire product life cycle. Now, they may provisionally approve an app with a bug knowing the company will rapidly fix it.

How does DevOps work? The DevOps Activities

There’s no shortage of activities to optimize in DevOps. It begins with working to continuously to improve stakeholder participation across at least seven core activities:

  • Configuration Management
  • Continuous Integration
  • Automated Testing
  • Infrastructure as Code
  • Continuous Delivery
  • Continuous Deployment
  • Non-Stop, Continuous Monitoring
DevOps

That’s a lot to keep track of. But one of the first axioms of DevOps is to, “Automate that which can be automated”

DevOps and Performance Analytics “On-Demand”

Optimize DevOps to drive organizational growth.

DevOps works with a lot of data. One Terabyte of data would take a team of 200 people a full year to read, say nothing about analyzing it. DevOps may not read that much. But, over time, data from reports, server logs, etc., it adds up pretty fast.
  • Automating both data collection and analysis saves enormous amounts of time.
  • Less time reading reports means more time being able to act on them.
  • Each improvement has a cascading effect and builds momentum to make continuous improvement if not easier, certainly faster.
To optimize DevOps requires measuring and quantifying improvements. It’s necessary to know where you started, where you are now, and where you’re going. The most important aspect of the road forward is prioritizing that which can be improved the most with the least effort.

Four Levels of Oversight

Automated development analytics, like what we offer here at Gitential, tracks every interaction with your git repositories.

Gitential is compatible with

  • GitHub,
  • GitHub Enterprise,
  • Bitbucket,
  • Bitbucket Serve,
  • and Azure DevOps.
  • We also rolled out our data integration for GitLabs.

Now you can easily and objectively track the efforts of all of your team members. This has been fairly difficult in large organizations, widely distributed teams and when outsourcing to offshore agencies. It’s extremely difficult to optimize DevOps if you can’t track it.

Gitential provides everyone the ability to see how projects are progressing – and to drill down to individual performance metrics.

Stakeholder Visibility

Chief Technical Officers

A direct line of sight into how every project is progressing
Software Engineer Managers
See how each project workload is progressing and if any are showing signs of being challenged.
Project Managers
Identify who is collaborating with each other to determine optimal team assignments for each project.
Team Leaders
See detailed metrics on team member code volume, utilization, efficiency vs. utilization, code complexity and much more.

Gitential KPIs and Metrics

Gitential tracks a wide range of Key Performance Indicators and development metrics including:

  • Active Days,
  • Code Age,
  • Code Churn,
  • Code Complexity Ration,
  • Code Efficiency,
  • Code Structure,
  • Code Volume,
  • Coding Hours,
  • Collaboration Map,
  • Commit Frequency,
  • Error Counts,
  • Files by Language Lead Time,
  • Number of Contributors,
  • Number of Commits,
  • Test Volume,
  • Utilization,
  • Velocity

DevOps optimizes people, processes, and technologies

As with all organizational activities and operations, DevOps has three main ingredients to work with. Improvements can usually be found in how each pair of legs on the PPT triangle come together.

  • Efficiency lagging indicates there’s an issue between people and processes. It warrants examining what can be simplified, aligned better, and tracked more closely.
  • A lack of productivity is tied to processes and technology. It advocates examining what tasks can be automated.
  • If signs of continued improvement are hard to find, it boils down to how people interact with technology. This warrants training and testing innovative technologies.

Software development performance analytics is designed to track the efficiency, productivity, quality and level of collaboration of developers. To a large extent, it specializes in finding areas for improvement between the people and technologies/processes.

Root Cause Analysis and Ishikawa Diagrams

Referenced by a variety of names and processes, the “fishbone diagram” is a trusty old friend of engineers. It’s useful for finding the root cause/s behind problems and relationships between them. It, too, fits into the People, Processes, and Technologies Framework – though you can make it more complex.

There are a lot of possible reasons why members of your team are holding back your productivity, monitoring and analyzing is essential to discover them.

Optimize DevOps with prioritized improvements

What activities or areas of neglect are contributing to the total inefficiency and by how much?

When everything’s a priority, nothing’s a priority. Without objective metrics to guide decisions, we’re relying upon guesswork. The guesswork of an experienced engineer is a pretty good place to start, but DevOps is concerned with a continuous cycle of improvement. Going back to the 30% developer inefficiency cited by Stripe, so there’s still plenty of room for improvement.

The Fishbone Diagram let’s us start defining problems according to the three PPT buckets. The performance metrics from Gitential’s reports can help define how much of a problem each is.  Leastwise, the “People” goal of DevOps is largely defined – how to improve developer efficiency by 30%? Note that DevOps is also concerned about improving interactive processes with non-coding teams like Sales and Operations, too.

Evaluating people vs. technologies and processes

Your engineers may make some initial assessments if you’re just beginning to roll out a DevOps effort. On the people’s side of software development - there can be many reasons for low efficiency.
  • New team members will need to come up to your coding standards.
  • Some may do great with certain programming languages, but not so hot with others.
  • Burnout can be an issue extending from long hours.
  • Some may feel that their concerns are not being properly addressed (like paying down technical debt).
  • They may just be unsure of themselves and rewrite the same code frequently.
  • Is everyone conducting tests? How frequently are they failing?
So, training is always an issue, and a continuous one if you have any amount of turnover. Daily code reviews are one of the best mechanisms to bring new developers up to speed, or improve their skills in different languages. Code reviews can reinforce quality standards, processes, and reiterate the need for regular testing. All of these are factors that can be tracked in Gitential reports.

Improving team cross-functionality may be another function to optimize by DevOps. Knowing who everyone is collaborating with can help you mix things up on coding reviews. By the same token, knowing the strengths of all of your team members helps you to configure the best team for any given project.

Analytics for employee retention

Employee morale is often treated as a “soft issue” – not something that can be readily quantified. Except it can be quantified – and qualified as quite expensive!  Gitential can be used to accurately predict whether an employee is thinking about leaving the company. It’s not a 100% science, per se, but there are several key metrics and fluctuations that provide warning signs. This provides you the opportunity to talk with the team member, see what’s wrong, and try to work out a mutually amenable solution. Finding, hiring, and onboarding comes at significant expense.

Continuous Team Improvement

Software development performance analytics might be intimidating to some team members.  The goal is to use performance metrics as a tool to help all team members become better coders and engineers. The better you are at your job, the more likely it is that you’ll enjoy it and learn additional skills. If a team member starts showing signs of boredom, it may be time to give them greater challenges and responsibilities. You give them an opportunity to objectively see how they do.

About Gitential

Gitential helps you to improve the performance of your software development team based on their Git activities with actionable insights. Our advanced software development analytics provides developers with automated performance tracking of key metrics and easy to understand graphics. These can be called up “on demand” to boost your team’s collaboration, productivity, efficiency, and quality. If you have any questions, please let us know at gitential@gitential.com. We welcome you to sign up for a free trial, no credit card is needed!

Release Notes: New Account Navigation, Upgraded Dashboard Overview + More

How do you feel about your process for onboarding developers? How do they feel about it? You can ask them. You can also examine your first-year developer turnover to get a sense of its effectiveness, too. In our last post, Why Software Developers Leave, we tackled turnover. About 40% of developers leave in their first year. Of these, 43% leave in their first 90 days. Plus, turnover for tech startups always runs high. So, let’s examine steps you can take when onboarding developers to help your them settle in easier and become more productive faster.

Read More »

Onboarding Developers: 9 Tips for a 90 Day Program

How do you feel about your process for onboarding developers? How do they feel about it? You can ask them. You can also examine your first-year developer turnover to get a sense of its effectiveness, too. In our last post, Why Software Developers Leave, we tackled turnover. About 40% of developers leave in their first year. Of these, 43% leave in their first 90 days. Plus, turnover for tech startups always runs high. So, let’s examine steps you can take when onboarding developers to help your them settle in easier and become more productive faster.

Read More »
WhySoftware Developers Leave

Why Software Developers Leave

Understanding why software developers leave positions engineering managers to tackle one of the scariest industry-wide metrics – Turnover. It’s a persistent, recurring, expensive issue. There are several ways your company can avoid and mitigate turnover. This includes being aware of turnover triggers and the reasons that go with them. Developers at risk of leaving show plenty of warning signs that you can casually observe and track with software development performance analytics. So, let’s get you up to speed on what to watch for and things your company can do to keep your team together longer.

Read More »

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Did you like our content?

Spread the word

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