LinearB

With the latest round of G2 Grid Reports, LinearB is proud to have been named a leader in the Software Development Analytics Tools Category for the 9th time! 🔥 Across the Software Development Analytics Tools, DevOps, and Value Stream Management categories, user feedback has helped us receive awards like: – Easiest to do business with – Best support (shoutout to our amazing support team Betsy Rogers and Val Cabral)  – Users most likely to recommend  – Fastest Implementation  – Easiest setup  ✚ more! Thank you to all of our users who have left us reviews, we love hearing your feedback: 💬 “LinearB makes the engineering processes and flows visible by exposing a large number of engineering metrics. It helps me as the VP of R&D have educated discussions with my direct reports and with my CEO. I can identify bottlenecks quickly, measure the team efficiency and developer experience, and improve based on data.” - G2 user If you’re a user who hasn’t left a review yet, we would love to hear from you. Your feedback is our roadmap. Not a user yet? You can try out our Software Engineering Intelligence Platform for free and start your data-driven journey with our DORA metrics dashboard right out of the box. No limitations on contributors, repos, or team size – and no credit card required. #Metrics #Analytics #SoftwareEngineeringIntelligence 

Is your software delivery life cycle Gen AI ready? Maximize your investment in Gen AI in less than 30 days with: ✅ Policy-as-code to de-risk how code is delivered ✅ Orchestration of Gen AI code through your dev pipelines ✅ Visibility into the adoption and impact of Copilot and other Gen AI solutions By applying Gen AI Code Orchestration alongside a robust Software Engineering Intelligence Platform, you can fully realize the efficiency gains from Gen AI to: 𝐈𝐧𝐜𝐫𝐞𝐚𝐬𝐞 𝐦𝐞𝐫𝐠𝐞 𝐟𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲: Optimize your code review process by automatically routing Gen AI pull requests to the most relevant experts in your team. 𝐄𝐧𝐟𝐨𝐫𝐜𝐞 𝐩𝐨𝐥𝐢𝐜𝐲 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐬: Maintain strict security requirements for PR Agents and automatically alert your OpsSec team when action is necessary. 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤 𝐲𝐨𝐮𝐫 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞: Gain visibility into the impact Gen AI adoption is having on your delivery pipelines by using standard DevOps metrics to show the before and after 𝐒𝐞𝐞 𝐡𝐨𝐰 𝐆𝐞𝐧 𝐀𝐈 𝐢𝐬 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐢𝐧𝐠: Compare Gen AI workflows against your organization's baseline with dynamic label tracking. 𝐑𝐞𝐬𝐡𝐚𝐩𝐞 𝐲𝐨𝐮𝐫 𝐢𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭𝐬: Uncover how adoption of AI helps remove inefficiencies and reallocate resources into revenue generating activities. 𝐃𝐞-𝐫𝐢𝐬𝐤 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐚𝐭 𝐬𝐜𝐚𝐥𝐞: Turn best practices into working agreements. Ensure Gen AI code contributions receive the right level of review and attention using team level goals. 𝐒𝐡𝐢𝐩 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐚𝐡𝐞𝐚𝐝 𝐨𝐟 𝐬𝐜𝐡𝐞𝐝𝐮𝐥𝐞: Optimize for delivery metrics that expose the true ROI of your Gen AI code enhancements. Learn more about Gen AI Code Orchestration linked below 👇 #GenAI #AI #SoftwareEngineeringIntelligence

Developer experience is a key driver of developer productivity; when your team feels supported and empowered, their efficiency and output naturally increase. By making thoughtful improvements to developer experience, you set your team up for sustained success. Key aspects include: 𝐈𝐝𝐥𝐞 𝐨𝐫 𝐖𝐚𝐢𝐭 𝐓𝐢𝐦𝐞: Otherwise known as developer toil, this metrics collection shows the time between the various phases of the pull request process. Idle time is the killer of productivity as it splits focus, creates a high cognitive load, and drives further slowdowns in the PR process. 𝐌𝐞𝐞𝐭𝐢𝐧𝐠 𝐓𝐞𝐚𝐦 𝐆𝐨𝐚𝐥𝐬: When bottlenecks and inefficiencies are discovered, effective teams set data-backed goals against them. They also leverage automated workflows. Regularly attaining these goals indicates both developer effectiveness and a good developer experience. 𝐓𝐚𝐬𝐤 𝐁𝐚𝐥𝐚𝐧𝐜𝐞: Ensuring a healthy mix of tasks, including both bug fixes and engaging new challenges, to keep developers motivated and interested. 𝐒𝐤𝐢𝐥𝐥 𝐄𝐱𝐩𝐚𝐧𝐬𝐢𝐨𝐧: Encouraging developers to explore new parts of the codebase and expand their skill sets, promoting continuous learning and growth. 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐄𝐱𝐩𝐚𝐧𝐬𝐢𝐨𝐧: Continuously expanding knowledge of the codebase and learning new programming languages and technologies. 𝐂𝐨𝐝𝐢𝐧𝐠 𝐯𝐬. 𝐂𝐨𝐝𝐞 𝐑𝐞𝐯𝐢𝐞𝐰: Balancing time spent on writing code with reviewing and providing meaningful feedback on others’ code to maintain high code quality standards. 𝐔𝐧𝐩𝐥𝐚𝐧𝐧𝐞𝐝 𝐚𝐧𝐝 𝐀𝐝𝐝𝐞𝐝 𝐖𝐨𝐫𝐤: Unplanned work is a normal part of any engineering organization. A healthy amount of planning and scoping flexibility is good, but it needs to be addressed when it begins to impact the delivery of roadmap items promised to the business. 𝐖𝐈𝐏: Managing work-in-progress (WIP) to avoid overloading developers, helping them maintain focus and avoid burnout. By making thoughtful improvements to your developer experience, you set your team up for sustained success. Learn how you can improve in the Engineering Leader’s Guide to Developer Productivity linked below 👇 #DeveloperExperience #DeveloperProductivity #DevEx

Developer productivity can make or break whether companies deliver value to their customers – are you tracking the right metrics that truly make a difference? It’s easy to blame the difficulty of measuring developer productivity on skepticism and team pushback. But the reality is that good measurement and scalable improvement remains out of reach for three main reasons: ❌ Data is everywhere–insight is missing ❌ A lack of standardization in metrics and approaches ❌ Change–the universal constant in software engineering Understanding developer productivity is crucial for distinguishing between mere activity and meaningful progress. Productivity in software development goes beyond the simple measure of output–it means creating high-quality software efficiently while aligning with business goals and customer needs. Enhancing developer productivity is how businesses can maintain a competitive advantage–and a structured approach is essential. The basic framework for improving developer productivity is as follows: ✅ Goal setting using quantifiable engineering metrics ✅ Automating productivity improvement and toil reduction ✅ Using data to inform conversations, recurring syncs, and developer ceremonies We just released the Engineering Leader’s Guide to Accelerating Developer Productivity, which is a 40-page guide that highlights how you can adjust your approach to measure what matters and identify the right corrective strategies. Download the guide linked below, and see how other leaders have successfully navigated these challenges with real-world examples. #DeveloperProductivity #SoftwareEngineeringIntelligence #Productivity

Risk is the chief variable that prevents predictable software delivery and can keep teams from delivering on time regardless of their stability or velocity. ❌ Here are some of the risks you need to watch out for when trying to deliver software predictably: 𝐔𝐧𝐩𝐥𝐚𝐧𝐧𝐞𝐝 𝐖𝐨𝐫𝐤: Unplanned work, such as bug fixes or urgent changes, can derail any engineering team. This type of work typically requires immediate attention, pulling resources away from planned tasks and disrupting your team’s flow state. The key is to strike a healthy balance between appropriately scoping iterations with planned work and leaving a buffer for the team to do a little extra. 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐃𝐞𝐛𝐭: Technical debt refers to additional work required to fix issues that arise from taking shortcuts or making trade-offs. Unchecked technical debt can eventually lead to having a fragile codebase that is difficult to maintain and prone to bugs. This can slow down your delivery processes and increase the likelihood of critical failures. Regularly addressing technical debt via refactoring is necessary for maintaining long-term predictability and overall code quality. 𝐒𝐜𝐨𝐩𝐞 𝐂𝐫𝐞𝐞𝐩: Scope creep is when additional features or changes are added to a project after the project’s start date. Without fully evaluating your delivery timelines and resourcing constraints, scope creep can cause project delays and burnout your team. You need to have clear processes for evaluating and approving changes to ensure any changes align with the project's original goals and deadlines. 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐂𝐨𝐧𝐬𝐭𝐫𝐚𝐢𝐧𝐭𝐬: Limited resources, such as insufficient or misplaced headcount or a lack of tooling, can cause significant risks to software delivery. Without enough headcount or well-distributed teams, velocity and quality will suffer, leading to missed deadlines and increased stress. To guarantee high-priority tasks are given the appropriate level of company resources, high-performing executives use resource allocation metrics in order to prioritize work that aligns with business goals. 𝐏𝐨𝐨𝐫 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞: Developers want to be productive – consistency and clarity in a working environment are conducive to productivity. That means keeping things like WIP, velocity, stability, and added/unplanned work at acceptable levels every single iteration. A core pillar of predictable software delivery is a strong foundation in developer experience. In order to reduce developer frustration while also increasing productivity, you can utilize workflow automations that: - Remove developer toil - Reduce context switching - Offload manual work - Minimize human error - Drive clarity and standardization Using automation to reduce developer toil and streamline work results in a more predictable software delivery pipeline. Learn more in our latest blog on how to deliver software predictably linked below 👇 #SoftwareEngineering #DeveloperExperience

In the IEEE newsletter, Google recently outlined their practices for setting developer goals – but what makes their approach to goal-setting so effective, and how can you apply these principles to your team? At the core of Google’s process is the concept of durable goals. Google defines durable goals as goals that stand the test of time because goals should remain relevant and motivating even as projects evolve, challenges arise, and priorities shift. They should focus on hitting milestones and fostering continuous growth and improvement. A key aspect of durable goals is alignment with long-term business objectives and flexibility to adapt to changing circumstances. To define their list of goals, Google started with a group of subject matter experts who defined a list of goals and priorities across each phase of the software development lifecycle. They then compared this list to historical data from past engineering satisfaction surveys and refined it through a series of sessions with developer teams to refine them further. This led to goals that are durable, relatable, and mappable to observable behaviors in pursuit of improving developer productivity and experience. Learn how you can apply this process for your engineering team in our recent blog 'How Google Sets Durable Goals for Their Software Engineers' linked below 👇 #Goals #Google #SoftwareEngineeringIntelligence

Resource allocation isn’t just a matter of managing costs—it's a strategic driver of business outcomes. By implementing effective resource allocation strategies, engineering leaders can:  – Optimize project prioritization – Enhance team productivity – Ensure alignment with business goals Visibility into how resources are allocated enables better decision-making and the ability to deliver high-value projects on time and within budget, ultimately contributing to your company's success. For more insights on resource allocation, read our recent blog on how you can use team data to manage engineering resources effectively (🔗👇) #ResourceAllocation #SoftwareEngineeringIntellgience #Metrics

What metrics should you monitor to track the adoption, benefits, and risks for your engineering team's Gen AI initiative? ✅ Adoption As a baseline, tracking the number of Pull Requests (PRs) Opened that have Gen AI code is a great place to start measuring adoption. By studying this metric across various segments of your engineering org – and across different timelines – you can answer more advanced questions about adoption, like: – Which devs, teams or groups are adopting Gen AI the fastest? – Is adoption still growing, has it plateaued or is it tapering off after initial excitement? – Which parts of the codebase (e.g. repositories, services etc.) are seeing the highest adoption? 📈 Benefits Is Gen AI actually helping your team the way you expected it to? In theory, a Gen AI tool that’s writing chunks of code for your team should be reducing coding time and speeding up simple tasks. There are several metrics you can track here: Coding Time – The time it takes from first commit until a PR is issued. This can help you understand whether Gen AI is actually helping to reduce the amount of time it's taking developers to code. Ideally, you’ll see a decrease in this metric as Gen AI adoption grows. Merge Frequency – How often developers are able to get their code merged to the codebase. Merge Frequency captures not just coding time, but also the code review dynamic. This can help you understand whether Gen AI is helping your developers move faster – a higher merge frequency indicates faster cycles. ❌ Risks While Gen AI allows for much faster code creation, are your review and delivery pipelines ready to handle this? It is essential that you can gauge how your team's processes are being impacted so you can get ahead of any risks before they affect delivery. Some core metrics to track here include: PR Size – Gen AI makes it easy to generate code. An inflation in average PR size is an early indicator of inefficiencies, as larger PRs are harder and slower to review, and far more risky to merge and deploy. Track PR Size to ensure your teams’ Gen AI PRs aren’t bloating. Generated code is, in many cases, more difficult to review. It’s harder for the PR author to reason about it and defend it - since they didn’t actually write the code themself. Use the Review Depth and PRs Merged Without Review metrics to ensure Gen AI PRs get proper review, and the Time to Approve and Review Time metrics to check for increased bottlenecks in your review pipeline. Finally, use Rework Rate to understand if there is an increase in code churn, and Escaped Bugs to track overall quality trends. Learn more in our latest blog on how to measure Gen AI code linked below 👇 #AI #GenAI #SoftwareEngineeringIntelligence

Improving engineering team performance and code quality starts with understanding the distinction between lagging indicators that show past performance, like DORA Metrics, and leading indicators that predict future performance. When applied effectively, engineering teams can enhance their efficiency and quality through the focused improvement of leading indicators like: – Pull Request (PR) Size: Smaller PRs are linked to reduced cycle times and improved code quality. Encouraging smaller PRs can streamline testing and review processes and drive overall productivity. – PR Pickup Time: Quick initiation of code reviews minimizes inefficiencies and enhances developer productivity by maintaining a flow state. Aiming for a pickup time of one hour or less can significantly boost efficiency. – PR Review Time: Shortening review periods reduces bottlenecks, improving both productivity and developer experience. Targeting review times of thirty minutes or less is recommended. – PRs Merged Without Review: A high number of pull requests without review often correlates with a higher change failure rate, and robust code reviews are crucial for maintaining code quality. Improve PRs merged without review by setting team working agreements related to code reviews. Learn more about how you can leverage leading indicators in our latest blog linked below 👇 #CodeQuality #SoftwareEngineeringIntelligence #Metrics

Qualitative and quantitative engineering metrics are essential to improving operational efficiency and aligning resources to company goals, but you can’t set realistic targets if you don’t have a baseline for what “good” means for your team. Software Engineering Benchmarks provide a standardized and objective way to evaluate performance. By measuring high-impact metrics against industry peers, you can identify your team’s strengths, weaknesses, and workflow bottlenecks.  These are the reasons we built the Software Engineering Benchmarks Report. LinearB data scientists compiled data from more than 2,000 teams, 100k contributors, and 3.7 million PRs. Our recent blog breaks down the Software Engineering Benchmarks Report, the different categories of metrics, and insights related to these metrics. Head to the comments to read the full blog 👇 #Metrics #EngineeringMetrics #SoftwareEngineeringIntelligece