The database DevOps magazine from Redgate Software explores the new world of compliant Database DevOps, and how to protect against data breaches without turning the database into a deployment bottleneck.
Tricentis commissioned Forrester to evaluate current software development and delivery priorities as well as key metrics tracked throughout the software development lifecycle. Download the report to learn:
- What 5 core Continuous Testing practices separate Agile/DevOps leaders from laggards
- How Continuous Testing practice usage & automation varies across leaders and laggards
- Where automation has the greatest impact on DevOps success
- About the dangerous “risk blind spot” in most organizations
- What quality metrics are most valuable at each phase of the delivery process
Download a complimentary copy of the 2017 Gartner Magic Quadrant for Software Test Automation.
There is, inevitably, a cost to introducing database DevOps, but what kind of return can you expect from that investment? Redgate used some pioneering research into the real business benefits of DevOps, and calculated the actual $ cost from some real-world examples, to show the return for every stakeholder involved.
DevOps is becoming the new normal in application development, and DevSecOps is now entering the picture. By balancing the desire to release code faster with the need for the same code to be secure, it addresses increasing demands for data privacy. But what about the database? How can databases be included in both DevOps and DevSecOps? What additional measures should be considered to achieve truly compliant database DevOps? This whitepaper provides a valuable insight.
Software testing might not be as exciting as development, where abstract ideas are magically transformed into attractive interfaces you can showcase to customers and staff. However, testing can have a tremendous impact on the success of your digital transformation strategy. In fact, testing is often the silent killer of these efforts. Why? Because software testing is still dominated by yesterday’s tools and outdated processes — which don’t meet the needs of today’s accelerated development processes. Learn how this disconnect results in:
- Throttled acceleration
- Risk to your brand
- Poorly-allocated resources
Continuous integration and automated deployments become possible with database DevOps, paving the way to a complete continuous delivery process. Importantly, the biggest roadblock isn’t the hardware or software you require, it’s the development practices and strategies that need to change to accommodate it. This whitepaper gives some practical tips to introducing continuous delivery and including the database in your DevOps journey.
This report contains the results of Redgate's latest annual survey SQL Server database professionals, across a range of industries and company sizes. Over 700 organizations were asked whether they had adopted, or were planning to adopt, DevOps practices and how many of them had applied the same principles to their databases. The report looks at how things have developed over the last 12 months, and what key challenges and requirements are driving DevOps adoption in 2018.
For over two decades now, software testing tool vendors have been tempting enterprises with the promise of test automation. However, the fact of the matter is that most companies have never been able to achieve the desired business results from their automation initiatives. Recent studies report that test automation rates average around 20% overall, and from 26-30% for agile adopters. Read this paper to explore the 6 factors contributing to these dismal automation results — and insights into the best path forward.
Although Artificial Intelligence (AI) is nothing new, applying AI techniques to software testing started to become feasible just the past couple years. Inevitably, AI will soon become part of our day-to-day quality engineering process. But before we get caught up in the exuberance of the technology, let’s take a step back and assess how AI can help us achieve our quality objectives. It’s been suggested that AI could be applied to actions such as prioritizing testing and automation, generating and optimizing test cases, enhancing UI testing, reducing tedious analysis tasks, and helping to determine pass/fail outcomes for complex and subjective tests. However, should AI be applied in these cases? And where else could it assist?
The Software Fail Watch is an analysis of software bugs found in a year’s worth of English language news articles. The result is an extraordinary reminder of the role software plays in our daily lives, the necessity of software testing in every industry, and the far-reaching impacts of its failure. The 5th Edition of the Software Fail Watch identified 606 recorded software fails, impacting half of the world’s population (3.7 billion people), $1.7 trillion in assets, and 314 companies. And this is just scratching the surface — there are far more software bugs in the world than we will likely ever know about. Download the report for a detailed analysis of 2017 software fails, including:
- The overall impact on businesses, users, time, and assets
- How the number and type of software fails compare to previous years
- Software fail trends within and across industries — finance, retail, consumer tech, services (e.g., internet, telecom), public services, healthcare, transportation, and entertainment
- The biggest stories, hacks, and glitches that made headlines or slipped under the radar
The need to deliver value continuously has led application organizations to take agile and DevOps to enterprise scale. Application leaders must select the right combination of tools, technologies and practices to enable the ongoing digital business transformation of their organizations. Key findings in this report include:
- Tools and practices commonly associated with Mode 2, such as agile and DevOps, are evolving to enterprise scale as organizations mature their bimodal capabilities.
- As cloud-based services make advanced technologies such as machine learning more accessible, application architectures and tools that leverage such services take prominence while service-oriented approaches fade.
- Because application development technology assets are interdependent with other assets, organizational collaboration is needed to change them effectively.
Most people involved in IT may be able to describe the general framework of DevOps, but lack a concrete understanding of the When, What, Why, and How of DevOps. Download the white paper to find out more about:
- What DevOps really is
- What gave rise to the DevOps movement
- How DevOps improves collaborations, efficiency, and effectiveness
- How DevOps impacts attitudes, values, goals, and practices
Read this paper to learn how AI can take software testing to the next level, including:
- Why AI is now more feasible — and critical — than ever
- What AI really is and how it’s best applied
- How AI can help us test smarter, not harder
- The role of smart testing technologies that aren’t technically “AI” (e.g., self-healing technologies)
The past few years have brought a sea change in the way that applications are architected, developed, and consumed — increasing both the complexity of testing and the business impact of software failures. Read this paper to learn:
- What is Continuous Testing
- Where traditional test automation falls short in modern development and delivery processes
- The 3 main differences between Continuous Testing and test automation
- How testers can address each of the 3 key elements of Continuous Testing