July 2022 Summaries

Engineering leaders can enhance team output and align with business goals by leveraging data-driven insights to track key metrics, such as business alignment, DORA metrics, outsourced team contributions, customer satisfaction, and developer happiness. This approach allows for proactive management and quantifiable decision-making, leading to measurable improvements in efficiency and morale. While specific actions vary by leadership level, all leaders must use data to prioritize actions, align with business goals, and communicate impacts across the organization. A VP of Engineering, for instance, focuses on broad-impact data-led decisions that affect the organization and peer entities. By shifting from reactive to proactive management, engineering leaders can ensure alignment with business objectives and improve execution. Data-driven decisions also aid in vendor selection and customer satisfaction by providing objective metrics to evaluate performance and address escalations. Additionally, maintaining developer happiness is crucial, as it can be quantified through productivity and human factors, ensuring a motivated workforce.

Harness has launched "codeAbout," a new livestream series aimed at engaging developers and engineers in exploring the Harness ecosystem through interactive and unscripted sessions. Hosted by Kevin Poorman, the series will cover topics such as Continuous Integration (CI), Continuous Delivery (CD), and Chaos Engineering, among others. Scheduled to air most Thursdays at 1 pm Eastern, the sessions will be available on Twitch and YouTube, allowing participants to watch live or on-demand. The format encourages audience interaction and collaboration, featuring real-world problem-solving and guest appearances. The goal is to foster a community-driven learning environment where failure is seen as a part of the learning process, and participants are encouraged to actively engage, provide feedback, and even join as guest speakers.

Trigger testing is a technique used in A/B experiments to focus on user actions that are specifically relevant to an experiment, such as clicking a button or scrolling to a particular part of a webpage, ensuring that the data captured is accurate and contextually relevant. This method helps eliminate the noise in data by including only those who have had the opportunity to be influenced by the experiment, thus providing clearer insights and more reliable results. Platforms like Split, which feature an in-app decision engine, facilitate this process by allowing real-time, local decision-making without network dependency, ensuring privacy and reducing performance penalties. This approach contrasts with platforms that require network calls, which can complicate trigger testing and potentially lead to less precise results and a slower user experience. Split promotes a culture of experimentation by providing tools that enable product development teams to confidently release impactful features and continuously improve through data-driven insights.

Harness Feature Flags has introduced support for the open-source Xamarin SDK, enabling developers to utilize feature flags in their cross-platform mobile applications. This integration with Xamarin, a popular platform for native app development, ensures consistent feature support and performance, simplifying the process of mobile feature development and deployment. Developers familiar with other Harness Feature Flag SDKs can expect similar functionality and patterns with the Xamarin SDK. The announcement provides guidance on accessing and initializing the SDK, as well as creating and deploying feature flags, emphasizing the importance of integrating the feature flag into both the codebase and the Harness project for full functionality.

Harness Feature Flags, a new addition to the Harness Software Delivery Platform, enhances feature delivery by integrating feature flag management into the CI/CD pipeline, focusing on developer experience, management, governance, and reducing deployment risks. Traditionally, software feature releases are fraught with risks due to the simultaneous deployment to all users, leading to potential customer dissatisfaction and complex rollback processes. Current tools often fail to meet the needs for speed and risk minimization, as they rely on outdated methodologies that don't fully embrace feature flags. Harness Feature Flags addresses these challenges by offering a simple UI-based workflow and integrating governance and verification processes that ensure compliance and minimize negative impacts. It allows organizations to create visual feature release pipelines, making feature flag management a seamless part of the software development lifecycle. Despite existing solutions in the market, Harness Feature Flags distinguishes itself by providing a more integrated and efficient approach, allowing teams to achieve higher engineering velocity without compromising existing processes.

Harness Feature Flags has introduced new analytics and business intelligence capabilities, offering teams enhanced visibility into feature flag usage, governance, and compliance, thus optimizing feature release processes. This integration enables the creation of custom dashboards and automated reporting, facilitating strategic decision-making throughout the software delivery lifecycle. The platform provides a comprehensive view of feature flag activities, including usage metrics, creation history, and changes in production, which aids in governance and compliance audits. Users can customize dashboards across various metrics, ensuring timely alerts and reports for unexpected behavior, thereby empowering teams to evaluate and improve their feature flag strategies. Additionally, it supports mixing analytics across the software development lifecycle (SDLC) and integrates seamlessly with other Harness modules, providing insights into continuous integration and delivery processes.

Feature flags are a strategic tool in software development that enable targeted rollouts and release progressions, allowing companies to mitigate deployment risks, enhance continuous integration and continuous delivery (CI/CD) processes, and gather user feedback without immediate engineering involvement. By allowing changes to be stress-tested on selected user segments before wider deployment, feature flags help organizations learn from customer interactions and de-risk modifications effectively. Unlike canary deployments, which focus on system anomalies and performance metrics, feature flags emphasize testing user experience and functionality. This approach facilitates incremental change introductions with minimal project management overhead and allows for flexibility in enabling or disabling features based on user feedback or specific conditions. Harness, for instance, uses feature flags to manage staged rollouts, turning features on for specific customer segments or internal teams to gather insights and improve feature development. Overall, feature flags provide a versatile method for refining software releases, enabling organizations to be agile and responsive to customer needs and system demands.

Feature flags enhance team efficiency and product development by allowing safer, more controlled releases and testing in production, but teams often grapple with the decision to build their own solution or purchase a commercial tool like Harness Feature Flags. While building an in-house tool might initially seem cost-effective and tailored to specific needs, it can lead to increased complexity, maintenance challenges, and resource demands as usage scales. Commercial tools, on the other hand, offer scalability, security, and best practices that improve long-term productivity and reduce maintenance overhead. They also provide a more reliable and user-friendly experience, benefiting from industry-wide knowledge and innovations in workflows. Ultimately, the choice between building and buying a feature flagging tool depends on the specific needs and context of an organization, but commercial options often present a more sustainable and comprehensive solution.

Split has redesigned its S3 Inbound Integration data pipeline to enhance scalability and reduce costs in response to increasing customer demands. The new pipeline leverages feature flagging for A/B testing, enabling controlled rollouts and seamless production testing without affecting the customer experience. Compared to the existing setup, the new architecture employs Spark streaming, facilitating parallel data processing and significantly lowering resource consumption by 80%. It also improves status report quality with enriched data, allowing customers to self-correct errors. Split's feature flag strategy not only helps in fine-tuning data ingestion but also provides a quick rollback to the previous pipeline if necessary. With successful deployment over a quarter, the new pipeline processes data faster and more efficiently, demonstrating enhanced performance and reliability in real-time environments.

Harness CI's integration with Jira enhances the CI/CD workflow by automating task tracking and approvals, thus minimizing manual efforts and boosting collaboration among development, operations, and testing teams. This integration allows developers to utilize a standardized process for workflow efficiency while building, testing, and deploying artifacts in any programming language. Harness Test Intelligence, a feature employing machine learning, optimizes the testing phase by running only relevant tests, accelerating feedback loops, and improving code quality. The integration also supports Jira approvals, facilitating smoother CI/CD operations through easy-to-manage approval stages and ticketing processes. By incorporating these features, organizations can streamline their DevOps journey, reduce operational burdens, and improve software delivery efficiency.

Implementing an Engineering Knowledge Graph (EKG) with Harness Software Engineering Insights enables organizations to improve engineering productivity by automating workflows, enhancing decision-making, and accurately mapping the relationships between teams, tools, and assets. As organizations and products grow, understanding the tools and assets owned by different teams becomes challenging, leading to reliance on gut-based decisions and manual processes that often reach breaking points. A mature EKG helps address these challenges by providing comprehensive metric collection, identifying the right owners for tasks, and enabling end-to-end automation of DevOps processes. The process of building and maintaining an EKG is complex due to the varied segmentation models in different DevOps tools, but Harness Software Engineering Insights aids in creating and maintaining an EKG by integrating with dev tools like JIRA and Git, thereby correlating information to build a cohesive system. This allows for better insights into project trends, bottlenecks, inter-team dependencies, and the customization of automated workflows, ultimately maximizing engineering productivity and ensuring effective communication and actionability across teams.