AWS Lambda vs. OpenText ALM/Quality Center

Lambda excels at event-driven, short-lived tasks, such as processing files or building simple APIs. However, it's less ideal for long-running, computationally intensive, or applications that rely on carrying the state between jobs. Cold starts and constant load can easily balloon the costs.

Incentivized

For an organisation that has completely adopted SAFe structure including naming terminology, it is less appropriate and apart from that. It can suit any organisation out there, and it can solve all your problems one way or another by customising it. It is a robust and highly scalable solution to support all the business needs. It improves a lot of productivity and visibility.

Incentivized

• No provisioning required - we don't have to pay anything upfront
• Serverless deployment - it gets executed only when request comes and we pay only for the time the request is getting executed
• Integrates well with AWS CloudWatch triggers so it is easy to setup scheduled tasks like cron jobs

Incentivized

• If you have a mix of automation & manual test suites, HPALM is the best tool to manage that. It definitely integrates very well with HP automation tools like HP Unified Functional Testing and HP LoadRunner. Automated Suites can be executed, reports can be maintained automatically. It also classifies which test suites are manual & which are automated & managers can see the progress happening in moving from manual to automated suites. In HPA ALM all the functional test suites, performance test suites, security suites can be defined, managed & tracked in one place.
• It is a wonderful tool for test management. Whether you want to create test cases, or import it, from execution to snapshot capturing, it supports all activities very well. The linking of defects to test runs is excellent. Any changes in mandatory fields or status of the defect triggers an e-mail and sent automatically to the user that the defect is assigned to.
• It also supports devops implementation by interacting with development tool sets such as Jenkins & GIT. It also bring in team collaboration by supporting collaboration tools like Slack and Hubot.
• This tool can integrate to any environment, any source control management tool bringing in changes and creates that trace-ability and links between source control changes to requirements to tests across the sdlc life-cycle.

Incentivized

• Developing test cases for Lambda functions can be difficult. For functions that require some sort of input it can be tough to develop the proper payload and event for a test.
• For the uninitiated, deploying functions with Infrastructure as Code tools can be a challenging undertaking.
• Logging the output of a function feels disjointed from running the function in the console. A tighter integration with operational logging would be appreciated, perhaps being able to view function logs from the Lambda console instead of having to navigate over to CloudWatch.
• Sometimes its difficult to determine the correct permissions needed for Lambda execution from other AWS services.

Incentivized

• The requirements module is not as user friendly as other applications, such as Blue Bird. Managing requirements is usually done in another tool. However, having the requirements in ALM is important to ensure traceability to tests and defects.
• Reporting across multiple ALM repositories is not supported within the tool. Only graphs are included within ALM functionality. Due to size considerations, one or two projects is not a good solution. Alternatively, we have started leveraging the template functionality within ALM and are integrating with a third party reporting tool to work around this issue.
• NET (not Octane) requires a package for deployment to machines without administrative rights. Every time there is a change, a new package must be created, which increases the time to deploy. It also forces us to wait until multiple patches have been provided before updating production.

Incentivized

I like the ease to use and its reliable.

Incentivized

I give it a seven is usability because it's AWS. Their UI's are always clunkier than the competition and their documentation is rather cumbersome. There's SO MUCH to dig through and it's a gamble if you actually end up finding the corresponding info if it will actually help. Like I said before, going to google with a specific problem is likely a better route because AWS is quite ubiquitous and chances are you're not the first to encounter the problem. That being said, using SAM (Serverless application model) and it's SAM Local environment makes running local instances of your Lambdas in dev environments painless and quite fun. Using Nodejs + Lambda + SAM Local + VS Code debugger = AWESOME.

Incentivized

Because it lets me track the test cases with detailed scenarios and is clearly separated in folders. Also the defect filter helps me filter only the ones that have been assigned to a particular area of interest. The availability of reports lets me see the essentials fields which I might be missing the data on and helps me to work on these instead of having to go through everything.

Incentivized

Amazon consistently provides comprehensive and easy-to-parse documentation of all AWS features and services. Most development team members find what they need with a quick internet search of the AWS documentation available online. If you need advanced support, though, you might need to engage an AWS engineer, and that could be an unexpected (or unwelcome) expense.

Incentivized

It is a great tool, however, it got this rating because there is a lot of learning that takes a lot longer than other tools. There are no mobile versions of ALM even with just a project summary view. I believe ALM is well capable of integration with other analytics tools that can help business solutions prediction based on current and past project data. This is Data held in ALM but with no other use apart from human reading and project progress. ALM looks like a steady platform that I believe can handle more dynamic functionality. You could add an internal communication platform that is not a third party. Limit that communication tool to specific project members.

Incentivized

AWS Lambda is good for short running functions, and ideally in response to events within AWS. Google App Engine is a more robust environment which can have complex code running for long periods of time, and across more than one instance of hardware. Google App Engine allows for both front-end and back-end infrastructure, while AWS Lambda is only for small back-end functions

Incentivized

We have other tools in our organization like Atlassian JIRA and Microsoft Team Foundation Server, which are very capable tools but very narrow in their approach and feature set and does not come even close to the some of the core capabilities of HP ALM. HP ALM is the "System of Record" in our organization. It gives visibility for an artifact throughout the delivery chain, which cut downs unnecessary bottlenecks and noise during releases.

Incentivized

• Positive - Only paying for when code is run, unlike virtual machines where you pay always regardless of processing power usage.
• Positive - Scalability and accommodating larger amounts of demand is much cheaper. Instead of scaling up virtual machines and increasing the prices you pay for that, you are just increasing the number of times your lambda function is run.
• Negative - Debugging/troubleshooting, and developing for lambda functions take a bit more time to get used to, and migrating code from virtual machines and normal processes to Lambda functions can take a bit of time.

Incentivized

• ALM/QC has allowed for quick, traceable turnaround on relatively simple tasks
• ALM/QC allows us to achieve our business objective of always being able to refer to a documented ticket for work being done.
• ALM/QC navigation is not the easiest, so this aspect of the product has caused great frustration among new users.

Incentivized