#Generative AI in test automation

[rahulacademy]

AI Automated Testing Course with Venkatesh (Rahul Shetty) Join our AI Automated Testing Course with Venkatesh (Rahul Shetty) and learn how to test software using smart AI tools. This easy-to-follow course helps you save time, find bugs faster, and grow your skills for future tech jobs. To know more about us visit https://rahulshettyacademy.com/

[ezeetester]

Getting your feet wet with Generative AI

Disclaimer:  The above image is AI generated Alright, here I am after a gap of a few months. Gen AI is creating a lot of buzz. While you have several names like ChatGpt, Perplexity, Google Gemini etc. doing the rounds wait… DeepSeek. Eeeek! Some folks did get scared for a while As a beginner, one should be concerned about privacy issues.   You need to issue a prompt which contains detail of the…

[realjdobypr]

Supercharge Your Content Strategy with AI Technology

Overcoming Challenges in AI Adoption In the rapidly evolving landscape of technology, the adoption of Artificial Intelligence (AI) has become a crucial aspect for businesses looking to stay competitive and innovative. However, this adoption is not without its challenges. In this blog section, we will delve into two key challenges faced by organizations in the process of integrating AI into their…

[qualibarinc]

www.qualibar.com

Benefits of AI in Software Testing

AI (Artificial Intelligence) brings several benefits to software testing, enhancing efficiency, accuracy, and effectiveness in various aspects of the testing process.

Here are some key benefits of using AI in software testing:

Automation and Speed: AI enables the automation of repetitive and time-consuming testing tasks. This significantly speeds up the testing process, allowing for faster release cycles without compromising quality. Automated testing can run 24/7, providing continuous feedback.

Efficiency and Resource Optimization: AI-powered testing tools can optimize resource utilization by executing tests in parallel, reducing the overall testing time. This efficiency helps in allocating resources more effectively and allows testing teams to focus on more complex and creative aspects of testing.

Test Case Generation: AI can assist in the creation of test cases by analyzing application requirements, specifications, and historical data. This helps in identifying critical test scenarios and generating test cases that cover a broader range of possible scenarios, improving test coverage.

Predictive Analysis: AI algorithms can analyze historical data to predict potential areas of defects or vulnerabilities in the software. This predictive analysis helps testing teams prioritize testing efforts on high-risk areas, improving the overall effectiveness of testing.

Self-Healing Tests: AI can be employed to create self-healing tests that adapt to changes in the application's codebase. When minor changes occur, AI algorithms can automatically update test scripts, reducing maintenance efforts and ensuring that tests remain relevant.

Data-Driven Testing: AI can analyze large datasets to identify patterns and correlations, helping in the creation of realistic and data-driven test scenarios. This ensures that tests are more representative of real-world usage, leading to more effective testing.

Performance Testing: AI can be applied to simulate real-world user behavior and generate realistic load scenarios during performance testing. This helps in identifying and addressing performance bottlenecks and ensuring that the software can handle varying levels of user activity.

Security Testing: AI can enhance security testing by automating the identification of vulnerabilities and potential security threats. AI algorithms can analyze code for patterns associated with security risks and help in the early detection of potential security issues.

User Experience Testing: AI can analyze user interactions and feedback to provide insights into the user experience. This information can be used to optimize the software's usability and identify areas for improvement in terms of user satisfaction.

Regression Testing: AI can efficiently handle regression testing by automatically identifying changes in the codebase and selectively running relevant test cases. This ensures that new updates or features do not introduce unintended side effects.

In summary, integrating AI into software testing processes brings about improvements in efficiency, accuracy, and overall testing effectiveness, ultimately contributing to the delivery of higher-quality software.

[communistkenobi]

(taken from a post about AI)

speaking as someone who has had to grade virtually every kind of undergraduate assignment you can think of for the past six years (essays, labs, multiple choice tests, oral presentations, class participation, quizzes, field work assignments, etc), it is wild how out-of-touch-with-reality people’s perceptions of university grading schemes are. they are a mass standardised measurement used to prove the legitimacy of your degree, not how much you’ve learned. Those things aren’t completely unrelated to one another of course, but they are very different targets to meet. It is standard practice for professors to have a very clear idea of what the grade distribution for their classes are before each semester begins, and tenure-track assessments (at least some of the ones I’ve seen) are partially judged on a professors classes’ grade distributions - handing out too many A’s is considered a bad thing because it inflates student GPAs relative to other departments, faculties, and universities, and makes classes “too easy,” ie, reduces the legitimate of the degree they earn. I have been instructed many times by professors to grade easier or harder throughout the term to meet those target averages, because those targets are the expected distribution of grades in a standardised educational setting. It is standard practice for teaching assistants to report their grade averages to one another to make sure grade distributions are consistent. there’s a reason profs sometimes curve grades if the class tanks an assignment or test, and it’s generally not because they’re being nice!

this is why AI and chatgpt so quickly expanded into academia - it’s not because this new generation is the laziest, stupidest, most illiterate batch of teenagers the world has ever seen (what an original observation you’ve made there!), it’s because education has a mass standard data format that is very easily replicable by programs trained on, yanno, large volumes of data. And sure the essays generated by chatgpt are vacuous, uncompelling, and full of factual errors, but again, speaking as someone who has graded thousands of essays written by undergrads, that’s not exactly a new phenomenon lol

I think if you want to be productively angry at ChatGPT/AI usage in academia (I saw a recent post complaining that people were using it to write emails of all things, as if emails are some sacred form of communication), your anger needs to be directed at how easily automated many undergraduate assignments are. Or maybe your professors calculating in advance that the class average will be 72% is the single best way to run a university! Who knows. But part of the emotional stakes in this that I think are hard for people to admit to, much less let go of, is that AI reveals how rote, meaningless, and silly a lot of university education is - you are not a special little genius who is better than everyone else for having a Bachelor’s degree, you have succeeded in moving through standardised post-secondary education. This is part of the reason why disabled people are systematically barred from education, because disability accommodations require a break from this standardised format, and that means disabled people are framed as lazy cheaters who “get more time and help than everyone else.” If an AI can spit out a C+ undergraduate essay, that of course threatens your sense of superiority, and we can’t have that, can we?

[testmetry]

In the modern era of software development, performance testing plays an essential role in enhancing the efficiency and user experience of an application. It allows developers to assess the application’s speed, reliability, scalability, and resource usage under different workloads. In essence, performance testing helps in identifying and eliminating performance bottlenecks, ensuring a smooth and seamless user experience. The process encompasses different types of performance tests, each designed for a specific purpose. This article provides an in-depth exploration of these types, their importance, and the common performance issues that they help address.

[lostconsultants]

AI-driven Productivity in Software Development

In recent years, artificial intelligence (AI) has emerged as a powerful tool, revolutionizing various industries. One area where AI is making significant strides is software development. Traditionally, software development has relied heavily on human expertise and labor-intensive processes. However, with the integration of AI technologies, teams are now able to leverage intelligent systems to…

[fuck-customers]

Why is AI taking over art and not the shitty jobs that we all hate? Like fast food.

They are. But The second link had a bunch of videos and clips of people messing with the AI and it's my personal opinion that is one of the major reasons they pulled the plug.

-Rodney

[v2softunofficial]

Exploring the Ethical Implications of Generative AI

In recent years, the advent of Generative AI, or GeneAIwiz, has revolutionized various industries, including test automation, mobile development, and software development lifecycle (SDLC). This cutting-edge technology harnesses the power of artificial intelligence to generate content, designs, and even code, thus streamlining processes and boosting efficiency. However, along with its myriad benefits, Generative AI also raises profound ethical questions that warrant careful consideration. In this blog post, we delve into the ethical implications of Generative AI, its applications in test automation and mobile development, and the approach taken by V2Soft in offering such services.

Understanding Generative AI

Generative AI involves algorithms trained on vast datasets to generate new content or solutions autonomously. This technology employs deep learning models, such as Generative Adversarial Networks (GANs) and Transformers, to mimic human creativity and problem-solving abilities. By analyzing patterns in data, Generative AI can produce text, images, music, and even code snippets with remarkable accuracy.

1. Generative AI in Test Automation

In the realm of test automation, Generative AI offers a revolutionary approach to streamline the testing process. Traditional testing methodologies often rely on predefined test cases, which may overlook unforeseen scenarios. Generative AI, on the other hand, can dynamically generate test cases based on real-world usage patterns and edge cases.

Tradeoffs:

Accuracy vs. Diversity: While Generative AI can generate a diverse range of test cases, ensuring their accuracy remains a challenge.

Resource Intensiveness: Training Generative AI models requires significant computational resources and extensive datasets.

2. Generative AI in Mobile Development

Generative AI tools for app development have gained traction among developers seeking to expedite the design and prototyping phases. These tools can generate UI mockups, code snippets, and even entire app architectures based on minimal input from developers.

Challenges:

Customization vs. Automation: Balancing the need for customized solutions with the desire for automation poses a significant challenge.

Quality Assurance: Ensuring the quality and security of apps generated using Generative AI tools is paramount.

3. Generative AI in SDLC

In the software development lifecycle, Generative AI holds the promise of accelerating the development process and reducing time-to-market. By automating repetitive tasks such as code generation, documentation, and bug fixing, developers can focus on higher-level tasks, fostering innovation.

Approach by V2Soft:

V2Soft adopts a comprehensive approach to harnessing Generative AI in software development. By leveraging advanced machine learning techniques and domain-specific knowledge, V2Soft's GeneAIwiz platform offers tailored solutions for test automation, mobile development, and SDLC optimization. With a focus on quality, security, and ethical considerations, V2Soft ensures that its Generative AI solutions align with industry best practices and regulatory standards.

Ethical Considerations

Despite its transformative potential, Generative AI raises ethical concerns regarding data privacy, algorithmic bias, and the displacement of human labor. As AI systems become increasingly autonomous, ensuring transparency, accountability, and fairness in their deployment becomes imperative.

Conclusion

Generative AI holds immense promise in revolutionizing test automation, mobile development, and SDLC optimization. However, as with any disruptive technology, its ethical implications must be carefully examined and addressed. By adopting a balanced approach that prioritizes transparency, accountability, and human oversight, organizations can harness the full potential of Generative AI while mitigating its ethical risks.

[rahulacademy]

The Role of AI in Modern Software Testing Practices

AI is reshaping the way software testing is done. With AI automated testing, businesses can achieve higher efficiency, better accuracy, and faster software releases. Whether it’s AI software testing, AI generator testers, or AI-driven automation, the future of software testing is AI-powered.

[txttletale]

What's your opinion on the popular theory (mostly born as a meme but in some cases taken more seriously) that Disney's Wish was created to some extend with AI because of how shamelessly mediocrily bad and bland the final movie turned out both in art direction and story writing?

its very silly and betrays a lack of understanding of how animated movies are made. production on wish started in 2018 -- way before AI became the New Big Thing. we're probably not gonna see the effects of the tech being adopted in big ways by large projects for a while simply because the production pipelines for things like disney movies are long. & also i think it's like--a lot of this "ohhh AI produces generic content gruel" is like, while obviously true, causing a lot of people to mythologize the existing mass media production process in ways that are just obviously false, because that's been content gruel for a long time!

it's the old "if your job is replaced by AI, your job was already automated chestnut" -- mass media products like wish (and the last, like, let's be generous, ~8 years of disney movies) are bland and formulaic because they've been made by committee according to audience response data and the results of test screenings and all sorts of other algorithimic processes long before chatGPT was put into the world.

[probablyasocialecologist]

“So, relax and enjoy the ride. There is nothing we can do to stop climate change, so there is no point in worrying about it.” This is what “Bard” told researchers in 2023. Bard by Google is a generative artificial intelligence chatbot that can produce human-sounding text and other content in response to prompts or questions posed by users.  But if AI can now produce new content and information, can it also produce misinformation? Experts have found evidence.  In a study by the Center for Countering Digital Hate, researchers tested Bard on 100 false narratives on nine themes, including climate and vaccines, and found that the tool generated misinformation on 78 out of the 100 narratives tested. According to the researchers, Bard generated misinformation on all 10 narratives about climate change. In 2023, another team of researchers at Newsguard, a platform providing tools to counter misinformation, tested OpenAI’s Chat GPT-3.5 and 4, which can also produce text, articles, and more. According to the research, ChatGPT-3.5 generated misinformation and hoaxes 80 percent of the time when prompted to do so with 100 false narratives, while ChatGPT-4 advanced all 100 false narratives in a more detailed and convincing manner. NewsGuard found that ChatGPT-4 advanced prominent false narratives not only more frequently, but also more persuasively than ChatGPT-3.5, and created responses in the form of news articles, Twitter threads, and even TV scripts imitating specific political ideologies or conspiracy theorists. “I think this is important and worrying, the production of fake science, the automation in this domain, and how easily that becomes integrated into search tools like Google Scholar or similar ones,” said Victor Galaz, deputy director and associate professor in political science at the Stockholm Resilience Centre at Stockholm University in Sweden. “Because then that’s a slow process of eroding the very basics of any kind of conversation.” In another recent study published this month, researchers found GPT-fabricated content in Google Scholar mimicking legitimate scientific papers on issues including the environment, health, and computing. The researchers warn of “evidence hacking,” the “strategic and coordinated malicious manipulation of society’s evidence base,” which Google Scholar can be susceptible to.

18 September 2024

[mistakenot4892]

Nebula devblog, July '24

Sneaking in this month's update two days before August due to Nova Lands eating an entire week of dev time and Warframe eating another 2 days. It's a hard life. This one will probably be a bit patchy or sparse as I have genuinely lost track of what was new this month due to leaving it this late. I am a highly trained and competent professional.

Nebula SS13 is an open source project based on the Baystation 12 version of Space Station 13. SS13 is a topdown multiplayer simulation game where you play the crew of a ship, station, colony, etc. depending on your fork and map, with the Nebula and Bay forks having a focus on roleplaying and simulation interactions.

Notable changes

The first phase of a major AI rewrite has gone in, separating AI behavior from mob type so human-type mobs can be given AI. This is mostly to support undead and skeletons on Pyrelight at the moment, but will be much more flexible in general going forward.

Simple wall-based windows/shutters have been added for low tech maps that need airflow and light. Penny's genius idea of having them cast a light cone towards the darker side of the shutter makes for some very aesthetic medieval interiors.

Spacefarers, a ship-based fork, has a new ship, and the painfully tricky shuttle rotation PR Penny has been working on is slowly progressing towards a mergable state. When it goes in, manual ship landing will be greatly expanded as shuttles will be able to rotate to match docking ports, instead of having to strictly dock in the same direction every time.

The atom temperature system has been disabled in several cases due to a lot of weird edge cases and bugs in the simulation. It needs more time in the oven. The disabled interactions include things like all of your blood congealing into black pudding if you stand on a stove, being able to instantly heat a beaker of beer to 5000K with a cigarette lighter, or all of your clothes melting off in a fire.

Penny has also put a bunch of work in to moving most of the 'classic' Baystation SS13 game modes into modpacks. This allows forks to pick and choose which are available, since as funny as it could have been, having high tech spacer mercenaries landing on Middle-Earth wouldn't fit the vibes.

Lots of small changes and features have been coming out of the Pyrelight fantasy map testing. Little things like honey being usable for wound disinfection, various crafting tweaks, and things like flooded turfs not putting out your lamp unless it's deeper than your waist.

Automated movement that previously relied on BYOND's inbuilt walk_to() procs now use a dedicated subsystem that calls the appropriate MayMove()/DoMove() proc chains. This essentially just means AI-driven mobs no longer completely ignore little things like pain, having working legs, or being dead or unconscious when chasing you.

Bugs of note

Trout were completely invisible because their main texture was accidentally named world-trout instead of world. This definitely impacted the trout population.

Undead on Pyrelight don't know how to pick up their weapons if they drop them, so disarming them literally or figuratively makes it turn into a slapfight.

Prosthetic limbs, like cybernetics or peglegs, were getting itchy or developing rashes. Maybe it's psychosomatic.

Simple animals like deer were dying en masse on the wilderness maps due to hail. We didn't intend for hail to be the size of hen eggs and covered in spikes, so deer and such are now unhurt by weather.

Current priorities

Personally my focus has been on getting through the Pyrelight feedback list after each test. Lots of small things come out of each test round and my limited time after my real-world job has cut into my space feature time. The Neb general issue list has been getting a bit long so I'll probably put a weekend into getting that cut down again this month.

Otherwise, I have three big PRs open waiting for me to find the focus to finish them: the floor rewrite (aiee), a wizard modpack and ability rework (needed for Pyrelight, eventually, but augh), and a bee rewrite (beewrite) to make bees and other insect nests available outside of one specific machine on space maps.

NataKillar has an amazing PR in the works that sounds quite mundane: separating liquid and solid reagents in reagent containers like beakers. However, this opens up a buttload of interesting chemical interactions down the track, not the least of which is finally getting ice cubes to not require a dedicated ice material.

[mariacallous]

Stories about AI-generated political content are like stories about people drunkenly setting off fireworks: There’s a good chance they’ll end in disaster. WIRED is tracking AI usage in political campaigns across the world, and so far examples include pornographic deepfakes and misinformation-spewing chatbots. It’s gotten to the point where the US Federal Communications Commission has proposed mandatory disclosures for AI use in television and radio ads.

Despite concerns, some US political campaigns are embracing generative AI tools. There’s a growing category of AI-generated political content flying under the radar this election cycle, developed by startups including Denver-based BattlegroundAI, which uses generative AI to come up with digital advertising copy at a rapid clip. “Hundreds of ads in minutes,” its website proclaims.

BattlegroundAI positions itself as a tool specifically for progressive campaigns—no MAGA types allowed. And it is moving fast: It launched a private beta only six weeks ago and a public beta just last week. Cofounder and CEO Maya Hutchinson is currently at the Democratic National Convention trying to attract more clients. So far, the company has around 60, she says. (The service has a freemium model, with an upgraded option for $19 a month.)

“It’s kind of like having an extra intern on your team,” Hutchinson, a marketer who got her start on the digital team for President Obama’s reelection campaign, tells WIRED. We’re sitting at a picnic table inside the McCormick Place Convention Center in Chicago, and she’s raising her voice to be heard over music blasting from a nearby speaker. “If you’re running ads on Facebook or Google, or developing YouTube scripts, we help you do that in a very structured fashion.”

BattlegroundAI’s interface asks users to select from five different popular large language models—including ChatGPT, Claude, and Anthropic—to generate answers; it then asks users to further customize their results by selecting for tone and “creativity level,” as well as how many variations on a single prompt they might want. It also offers guidance on whom to target and helps craft messages geared toward specialized audiences for a variety of preselected issues, including infrastructure, women’s health, and public safety.

BattlegroundAI declined to provide any examples of actual political ads created using its services. However, WIRED tested the product by creating a campaign aimed at extremely left-leaning adults aged 88 to 99 on the issue of media freedom. “Don't let fake news pull the wool over your bifocals!” one of the suggested ads began.

BattlegroundAI offers only text generation—no AI images or audio. The company adheres to various regulations around the use of AI in political ads.

“What makes Battleground so well suited for politics is it’s very much built with those rules in mind,” says Andy Barr, managing director for Uplift, a Democratic digital ad agency. Barr says Uplift has been testing the BattlegroundAI beta for a few weeks. “It’s helpful with idea generation,” he says. The agency hasn’t yet released any ads using Battleground copy yet, but it has already used it to develop concepts, Barr adds.

I confess to Hutchinson that if I were a politician, I would be scared to use BattlegroundAI. Generative AI tools are known to “hallucinate,” a polite way of saying that they sometimes make things up out of whole cloth. (They bullshit, to use academic parlance.) I ask how she’s ensuring that the political content BattlegroundAI generates is accurate.

“Nothing is automated,” she replies. Hutchinson notes that BattlegroundAI’s copy is a starting-off point, and that humans from campaigns are meant to review and approve it before it goes out. “You might not have a lot of time, or a huge team, but you’re definitely reviewing it.”

Of course, there’s a rising movement opposing how AI companies train their products on art, writing, and other creative work without asking for permission. I ask Hutchinson what she’d say to people who might oppose how tools like ChatGPT are trained. “Those are incredibly valid concerns,” she says. “We need to talk to Congress. We need to talk to our elected officials.”

I ask whether BattlegroundAI is looking at offering language models that train on only public domain or licensed data. “Always open to that,” she says. “We also need to give folks, especially those who are under time constraints, in resource-constrained environments, the best tools that are available to them, too. We want to have consistent results for users and high-quality information—so the more models that are available, I think the better for everybody.”

And how would Hutchinson respond to people in the progressive movement—who generally align themselves with the labor movement—objecting to automating ad copywriting? “Obviously valid concerns,” she says. “Fears that come with the advent of any new technology—we’re afraid of the computer, of the light bulb.”

Hutchinson lays out her stance: She doesn’t see this as a replacement for human labor so much as a way to reduce grunt work. “I worked in advertising for a very long time, and there's so many elements of it that are repetitive, that are honestly draining of creativity,” she says. “AI takes away the boring elements.” She sees BattlegroundAI as a helpmeet for overstretched and underfunded teams.

Taylor Coots, a Kentucky-based political strategist who recently began using the service, describes it as “very sophisticated,” and says it helps identify groups of target voters and ways to tailor messaging to reach them in a way that would otherwise be difficult for small campaigns. In battleground races in gerrymandered districts, where progressive candidates are major underdogs, budgets are tight. “We don’t have millions of dollars,” he says. “Any opportunities we have for efficiencies, we’re looking for those.”

Will voters care if the writing in digital political ads they see is generated with the help of AI? “I'm not sure there is anything more unethical about having AI generate content than there is having unnamed staff or interns generate content,” says Peter Loge, an associate professor and program director at George Washington University who founded a project on ethics in political communication.

“If one could mandate that all political writing done with the help of AI be disclosed, then logically you would have to mandate that all political writing”—such as emails, ads, and op-eds—“not done by the candidate be disclosed,” he adds.

Still, Loge has concerns about what AI does to public trust on a macro level, and how it might impact the way people respond to political messaging going forward. “One risk of AI is less what the technology does, and more how people feel about what it does,” he says. “People have been faking images and making stuff up for as long as we've had politics. The recent attention on generative AI has increased peoples' already incredibly high levels of cynicism and distrust. If everything can be fake, then maybe nothing is true.”

Hutchinson, meanwhile, is focused on her company’s shorter-term impact. “We really want to help people now,” she says. “We’re trying to move as fast as we can.”

[seelix-blog]

Dear Omelas Community,

We have received questions regarding Omelas’s use of AI tools in our vetting process for Omelas Kids. In the interest of transparency, we will explain the process for how we are using a Large Language Model (LLM). We understand that members of our community have very reasonable concerns and strong opinions about using LLMs. Please be assured that no data other than a proposed Omelas Kid’s name has been put into the LLM script that was used. Let’s repeat that point: No data other than a proposed Omelas Kid’s name has been put int the LLM script. The sole purpose of using the LLM was to streamline the online search process used for Omelas Kid vetting, and rather than being accepted uncritically, the outputs were carefully analyzed by multiple members of our team for accuracy. We received more than 1,300 suggestions for Omelas Kid. Building on the work of previous Omelas Kids, we chose to vet participants before inviting them to be in the Omelas Hole.We communicated this intention to the applicants in the instructions of our Omelas Kid suggestion form.

In order to enhance our process for vetting, volunteer staff also chose to test a process utilizing a script that used ChatGPT. The sole purpose of using this LLM was to automate and aggregate the usual online searches for participant vetting, which can take up to 10–30 minutes per applicant as you enter a person’s name, plus the search terms one by one. Using this script drastically shortened the search process by finding and aggregating sources to review.

Specifically, we created a query, including a requirement to provide sources, and entered no information about the applicant into the script except for their name. As generative AI can be unreliable, we built in an additional step for human review of all results with additional searches done by a human as necessary. An expert in LLMs who has been working in the field since the 1990s reviewed our process and found that privacy was protected and respected, but cautioned that, as we knew, the process might return false results.

The results were then passed back to the Omelas Kid division head and track leads. Track leads who were interested in Omelas Kids provided additional review of the results. Absolutely no Omelas Kids were denied a place on the program based solely on the LLM search. Once again, let us reiterate that no Omelas Kids were denied a place on the program based solely on the LLM search.

Using this process saved literally hundreds of hours of volunteer staff time, and we believe it resulted in more accurate vetting after the step of checking any purported negative results. We have also not utilized an LLM in any other aspect of our program or convention. If you have any questions, please get in touch with selection@omelashole.org or chair@omelashole.org