#software testing startups

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Software Trends

Top Trends in Software Testing for 2022:

Keep an eye out for the Top Software Testing Trends to watch in 2022.

#1) DevOps and Agile

DevOps and Agile have been adopted by organisations in response to the desire for speed and requirements that change quickly.

By integrating development and operations tasks, DevOps strategies, procedures, processes, and tools help shorten the time it takes from development to operations. For businesses searching for strategies to speed up the software lifecycle from development to delivery and operation, DevOps has gained widespread acceptance.

The adoption of Agile and DevOps by the teams enables them to produce high-quality software more quickly, sometimes known as "Quality of Speed." In this adoption,over the last five years, and interest in it has increased and will continue to do so in the years to come.

2.Test Automation

Software teams cannot ignore test automation because it is a crucial component of the DevOps process if they want to implement DevOps practises successfully.

They must look for chances to switch from manual testing to automated testing. Since test automation is thought to be a major DevOps bottleneck, at the very least, most regression testing should be automated.

Given the popularity of DevOps and the fact that test automation is underutilized, with less than 20% of testing being automated, there is a lot of room to increase the adoption of test automation in organizations. More advanced methods and tools should emerge to improve how test automation is used in projects.

Selenium, Katalon, and TestComplete, three widely used automation tools, continue to develop new capabilities that make automation much simpler and more efficient as well.

3.Automated API and services testing: 

A current trend in both Web and mobile application design is decoupling the client and server.

APIs and services are applied to numerous applications and parts. Teams must test APIs and services independently of the applications that use them as a result of these changes.

When API and services are used across client applications and components, testing them is more effective and efficient than testing the client. The The demand for API and service test automation is on the rise and may soon outpace the capabilities used by end users on graphical user interfaces.

It is more important than ever to have the proper procedure, tool, and solution for API automation tests. Learning the best API Testing Tools for your testing tasks is therefore worthwhile.

#4) Testing using artificial intelligence:

Although the software research community has long used artificial intelligence and machine learning (AI/ML) approaches to address difficulties in software testing, current advances in AI/ML and the abundance of data now available present new opportunities to use AI/ML in testing.

The use of AI/ML in testing, however, is still in its infancy. Businesses will uncover ways to improve their AI/ML testing procedures.

To produce better test cases, test scripts, test data, and reports, AI/ML algorithms are being created. Making decisions on where, what, and when to conduct tests might be aided by predictive models. The teams are assisted by clever analytics and visualisation in their efforts to find errors, comprehend test coverage, identify high-risk locations, etc.

In the next years, we anticipate seeing increasing uses of AI/ML to tackle issues like quality prediction, test case prioritisation, fault classification, and assignment.

#5 Automated Mobile Testing

As mobile devices become more sophisticated, the practise of developing mobile apps continues to expand.

Mobile test automation needs to be a component of DevOps toolchains in order to effectively enable DevOps. However, the way that mobile test automation is currently used is exceedingly low, in part because there are no methodologies or tools available.

Automated testing of mobile applications is a growing trend. Shortening time to market and using more sophisticated tools and approaches for mobile test automation are driving this trend.

Mobile automation may advance with the combination of cloud-based mobile device labs like Kobiton and test automation solutions like Katalon.

Conclusion:

The schedule, budget, staffing, and facilities needed for testing consume a significant amount of a project's resources. Testing is a crucially important verification method. Testing is comparatively unique because it is inherently destructive, in contrast to the many constructive activities of systems engineering.

[qqueenofhades]

I’m in undergrad but I keep hearing and seeing people talking about using chatgpt for their schoolwork and it makes me want to rip my hair out lol. Like even the “radical” anti-chatgpt ones are like “Oh yea it’s only good for outlines I’d never use it for my actual essay.” You’re using it for OUTLINES????? That’s the easy part!! I can’t wait to get to grad school and hopefully be surrounded by people who actually want to be there 😭😭😭

Not to sound COMPLETELY like a grumpy old codger (although lbr, I am), but I think this whole AI craze is the obvious result of an education system that prizes "teaching for the test" as the most important thing, wherein there are Obvious Correct Answers that if you select them, pass the standardized test and etc etc mean you are now Educated. So if there's a machine that can theoretically pick the correct answers for you by recombining existing data without the hard part of going through and individually assessing and compiling it yourself, Win!

... but of course, that's not the way it works at all, because AI is shown to create misleading, nonsensical, or flat-out dangerously incorrect information in every field it's applied to, and the errors are spotted as soon as an actual human subject expert takes the time to read it closely. Not to go completely KIDS THESE DAYS ARE JUST LAZY AND DONT WANT TO WORK, since finding a clever way to cheat on your schoolwork is one of those human instincts likewise old as time and has evolved according to tools, technology, and educational philosophy just like everything else, but I think there's an especial fear of Being Wrong that drives the recourse to AI (and this is likewise a result of an educational system that only prioritizes passing standardized tests as the sole measure of competence). It's hard to sort through competing sources and form a judgment and write it up in a comprehensive way, and if you do it wrong, you might get a Bad Grade! (The irony being, of course, that AI will *not* get you a good grade and will be marked even lower if your teachers catch it, which they will, whether by recognizing that it's nonsense or running it through a software platform like Turnitin, which is adding AI detection tools to its usual plagiarism checkers.)

We obviously see this mindset on social media, where Being Wrong can get you dogpiled and/or excluded from your peer groups, so it's even more important in the minds of anxious undergrads that they aren't Wrong. But yeah, AI produces nonsense, it is an open waste of your tuition dollars that are supposed to help you develop these independent college-level analytical and critical thinking skills that are very different from just checking exam boxes, and relying on it is not going to help anyone build those skills in the long term (and is frankly a big reason that we're in this mess with an entire generation being raised with zero critical thinking skills at the exact moment it's more crucial than ever that they have them). I am mildly hopeful that the AI craze will go bust just like crypto as soon as the main platforms either run out of startup funding or get sued into oblivion for plagiarism, but frankly, not soon enough, there will be some replacement for it, and that doesn't mean we will stop having to deal with fake news and fake information generated by a machine and/or people who can't be arsed to actually learn the skills and abilities they are paying good money to acquire. Which doesn't make sense to me, but hey.

So: Yes. This. I feel you and you have my deepest sympathies. Now if you'll excuse me, I have to sit on the porch in my quilt-draped rocking chair and shout at kids to get off my lawn.

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[gender-trash]

like the grand innovation of ros is that actually now 1) you don't have to rewrite your MESSAGING FRAMEWORK from scratch every time and you get a bunch of debug tools for free and 2) theres a bunch of prototype grade components you can probably plug together to get a demo if you know what you're doing

it's great but it's not like. whatever's going on in webservices world. and the webservices people and vcs who are used to it HATE this!!! i interned for a really crappy startup once whose entire concept was "hey, why is robotics not like webservices? clearly we need to reimplement ros but worse and dockerized lol what's hard realtime" and it so clearly had only survived as long as it had because it was The Baby of this VC guy who tried to sell me on javascript like it was an abusive boyfriend i should give ~another chance~, and right after i left they hired a new ceo and pivoted to having an actual product. theres ALLLL these guys coming from webservices land who want to pretend really hard that a malfunctioning robot is exactly as disposable as a malfunctioning docker container, and that gluing together preexisting robotics components (largely research-grade code developed on a completely different robot from whatever your product is) is exactly as easy as gluing together APIs, and it ends so badly for them every time. it's adorable.

relatedly my dad has this theory, which i think has a lot of predictive power, about how a company Makes It Big doing one thing (SAAS, or online ads, or B2B software, or like. making computer chips) and then that product cycle cadence/approach is baked into the company culture so hard that they completely flub it when they try to make something that necessitates a different approach. intel fucking sucks at making software because they inevitably drop support after 18 months because when you make a chip you design it and you send it to the fab and then it's out of your hands and there's no real way to "fix bugs" (you just maintain an errata sheet and add more tests so you can catch the bugs in the next chip you design). webservices companies who do continuous deployment or bust are often really really bad at coping with the sales and maintenance cycles of Big Businesses that a lot of b2b software is for. and software people in full generality have a ridiculously hard time with the concept that you can't exactly continuously deploy improved suspension for your robot chassis, because you have to actually crack the robot open on a workbench and swap it out.

in microservices world one of the baked-in cultural attitudes is "cattle not pets" -- your herd of docker containers is like a herd of cattle; when one of them malfunctions you take it out back and shoot it and spin up a new one, you don't waste energy on failure recovery. when you bring this approach to robotics land it inevitably fails! you have spent 4 or 5 figures minimum on a robot, that thing's a fucking pet! and also, when it malfunctions, it's flailing around potentially doing damage out here in the real world, which is... generally considered to be bad. there have to be layers and layers and layers of safety systems and fallbacks and failure recovery logic and everything needs to be designed to fail into a state that won't maim anyone, or it inevitably will fail, and maim someone. even just "if something goes wrong stop moving immediately" has some complexity to it.

webservices people are also big on "eventual consistency" and almost nothing in robotics is physically safe to do on an eventual consistency basis or else your robot will eventually (but consistently!) destroy itself and/or anything around it. the closer you get to low level control the more important it is to have hard timing guarantees and that's generally sort of antithetical to the philosophy that hardware should be as abstracted away as possible

[galwednesday]

This week's deep dive rec is Wall Street Journal reporter John Carreyrou's investigation into infamously fraudulent biotech start-up Theranos and its founder and CEO, Elizabeth Holmes, which conveniently comes in both book and podcast format. Podcast summary:

She was once the world's youngest self-made female billionaire. Now Elizabeth Holmes, founder of the blood-testing startup Theranos, stands accused of leading a massive fraud, and lying to investors, doctors, and patients about the capabilities of her technology. If convicted, she faces up to 20 years in prison. But Elizabeth may be able to sway a jury with her charisma, highly unusual defense strategy and the fact that key evidence has gone missing. John Carreyrou broke the Theranos scandal. Now he’ll take you into the courtroom as he examines Silicon Valley’s fake it-til-you-make it culture, and the case against Holmes.

Episodes 8 and 9 are particularly interesting looks into how Theranos secured funding and partnerships using faked demos, and how this strategy fell apart when potential clients pressed for more technical details. Episode 8 summary:

Elizabeth Holmes wowed investors, board members and journalists with live, in-person demonstrations that made it seem like her blood-testing machine worked. But most of these demos were faked. Behind the scenes, the blood samples were tested either manually or on third-party lab equipment. It's an astoundingly bold deception that was enabled by a software application Sunny Balwani wrote.

Episode 9 excerpt:

NARRATOR: The DOD contingent pressed for more information on how the black box that looked like a big desktop computer tower even worked. Holmes and Edlund refused to answer. That was a trade secret, they repeated. Frustrated, one member of the DOD delegation blurted out, "I'm starting to believe the device is just a box of Palo Alto air." Sensing that they were fast losing credibility, Holmes and Edlund made a small concession. They agreed to pass around the white rectangular cartridge containing the blood sample that slotted into the front of the device. Wagar asked what was inside the cartridge beside the blood sample. WAGAR: And they're like, we're not going to tell you. And so when I got to me, I reached into my pocket and pulled my Swiss army knife out and I started to try and cut it apart because you know, I'm curious and that really wigged them out. Um, I think they kind of jumped over the table to take it back from me. And I laughed at them and I said, you know, you realize that if you actually let this thing out into the wild, the first cartridge, people are going to tear it apart to see how it works. You know, you can't nondisclosure the entire Department of Defense.

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