OverOps Platform helps DevOps find misbehaving code with ML
Thursday, August 2, 2018
Machine learning and bug detection capabilities will soon come with the release of OverOps to provide AIOps tools to DevOps teams.
Machine learning meets bug detection with the announcement that OverOps made about the release of their new platform. DevOps teams will soon be armed with net new machine data to effectively evaluate the reliability of software they promote and implement a culture of accountability within their organizations, says OverOps with the release fo their platform. At its core, OverOps captures data from applications and services to provide code-aware insights to developers so they can detect and troubleshoot issues more effectively. Building on this foundation, OverOps Platform introduces new features such as software quality dashboards and an API that open this data up to fuel AIOps use cases.
“The industry has retooled almost the entire software supply chain, yet organizations still rely on manual and shallow methods to investigate and measure errors found within code,” said Stephen Elliot, Program Vice President, Management Software and DevOps at IDC. “There is a need to rethink the way development and DevOps teams gather insight about code-level issues. By having more granular visibility into the quality of applications and services across all environments - including production - organizations can proactively prevent outages that could otherwise lead to brand degradation and loss of revenue.”
For decades, development and operations teams have relied on noisy, shallow log files to detect and troubleshoot errors in software. OverOps improves this process by capturing net new machine data about every error and exception at the moment they occur, automating root cause analysis. Unlike existing tools, OverOps’ data includes structured details such as the value of all variables across the execution stack, the frequency, and failure rate of each error, the classification of new and reintroduced errors, the associated release numbers for each event, and more.
This comprehensive data not only helps developers find and fix issues more quickly, but with the introduction of four key new features - the OverOps API, Software Health Dashboards, a Machine Learning Engine and OverOps Extensions - OverOps Platform now also enables a number of AIOps-related use cases for DevOps and Site Reliability Engineers (SRE), including:
Continuous Reliability Using the RESTful API and Log File Linkage:
Organizations rely on the limited information found in log files to gauge how safe it is to promote code. This manual process often results in bad code making it to production and downtime that leads to lost revenue and brand damage. The RESTful API included in OverOps Platform now allows DevOps teams to investigate the overall quality of an application and determine when it is safe to promote code within a fast-paced continuous integration/continuous delivery (CI/CD) workflow. OverOps allows an organization to gain insight into new and reintroduced errors by type and for every release. OverOps precedes the creation of a log file entry and augments them with links to the platform so developers are enabled with rich information about each error and can quickly remediate issues, completing the circle and providing a valuable feedback loop from operations to development. Additionally, OverOps offers visibility into the uncaught and swallowed exceptions that are completely unavailable in log files.
Create a Culture of Accountability with Software Health Dashboards:
Many organizations have built natural walls between internal groups that encourage finger-pointing and blame when software fails and systems go awry. Without visibility into how and why things break, it is difficult to combat this. With the Software Health Dashboards that are introduced in OverOps Platform, development and operations teams can gain real-time insight into the overall quality and health of their applications and services. Powered by Grafana, the dashboards also help you understand types of errors, the team responsible for them and even the release or build they are associated with. This level of granularity into where, when, why and who is responsible for issues helps promote a culture of accountability across the software development lifecycle and ensures alignment and a shared goal for delivering reliable software.
Detect Anomalies with OverOps’ Machine Learning Engine:
Organizations have become accustomed to sifting through thousands of log file entries to find where code breaks, but when this escalates to millions and billions of log entries, determining the signal in the noise is near impossible. OverOps Platform solves this challenge by applying machine learning and anomaly detection techniques to its unique data set to detect elusive errors and help identify critical issues, new issues or reintroduced issues amongst billions of events. Existing AIOps solutions take a similar, machine learning-based approach, but are limited to the shallow information found in logs. With OverOps, the data beneath the algorithms enables you to analyze actual throughput in real-time, allowing for more exact analysis and helping teams focus on what's actually important.
All three of these DevOps use cases are dependent on the deep integration capabilities in OverOps Platform. With its API and support for metrics, OverOps expands the value of its unique data into critical DevOps tools such as Splunk, Elastic, Dynatrace and AppDynamics, among others. Further complementing this interoperability, OverOps Extensions provides an AWS Lambda-based framework (and on-premises code as an option) for organizations to create their own custom functions and workflows based on the valuable OverOps data.
Read more: https://www.overops.com/
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