#untested software

[joncronshawauthor]

Tech Bros Versus Zombies: A Story of Disruption Gone Wrong

Have you ever wondered what might happen if Silicon Valley accidentally triggered a zombie apocalypse? Not the shambling, brain-eating kind – but the perfectly synchronised, engagement-metrics-obsessed, neural-interface-gone-wrong sort. Well, wonder no more. I’m excited to introduce my latest story, Tech Bros Versus Zombies, now available for free on my Patreon…

[daemonhxckergrrl]

we're more like field-commissioned engineers - no formal training so we figure the system out by breaking it and putting it back together. emphasis on the breaking lmao

Linux users are the horsegirls of computer nerds

[gyrofenton]

Fenro is ABUSIVE

I eliminated your first ask but I'm glad you're bitter enough to send a second one. You have had me thinking for weeks, unable to sleep, caught in a spiral of thoughts and I have finally come to a conclusion.

Anon is right. Fenro is ABUSIVE.

Allow me to elaborate:

Gyro's behavior is the result of the traumatic events in Tokyolk, before that he used to be a kind and naive person as it was shown in Boyd's memories. He became sarcastic and distrustful as a way to mask his own insecurities and prevent further damage. Even his inventions that turn evil are a manifestation of his deep-rooted trauma. Imagine the kind of person he would be if he had never worked for Dr. Akita.

Meanwhile Fenton might seem innocent at first glance. Big-hearted, well intentioned, smart but still learning, but overall, he seems like someone who has it together. Except that he is a faker.

Mrs. Beakley is one of the smartest characters in Ducktales and she had her reasons to suspect about Fenton. Are we going to ignore Fenton's crimes against the McDuck family and McDuck Enterprises? Crimes that endangered Gyro and others.

Beakley is a reliable source of information, after all, she belongs to Scrooge's close circle of employees along with Duckworth, Quackfaster, Launchpad and, of course, Gyro. All of them are loyal to Scrooge, something that Fenton is not. Here are the reasons why he is a potential traitor:

Fenton leaked important information about Bulb Tech online, which constitutes a violation of McDuck Enterprises's intellectual property rights and humiliated Gyro in the process.

Fenton stole an untested prototype of the Gizmosuit to put it to the disposition of Waddle. We are talking about theft of private property, endangering the population of Duckburg and compromising Gyro's job since he is the leader of the project, putting him in a similar situation to what happened with the 2BO project.

Fenton also caused a lot of damages to the underwater lab on many times, like in the accident with the Gearloose Monophonic Rail or when he lied about owning his own lab for the sake of a date, where Gyro was kept captured and injured on what should have been a normal day of work for him.

All this also explains why Lil Bulb is in the list of potential traitors, Bulb Tech is no longer a trustful system as Fenton crompromised the integrity of its hardware and software. Evidence of this is how Mark Beaks was able to overwrite his own code in the first prototype of the Gizmosuit, which core was also based on Bulb Tech

And yet, both Gyro and Scrooge, continued to give him a free pass each time without legal consequences.

As someone who also works in a scientific field, I can't understand Fenton's reckless behavior nor justify it as simple mistakes. Beakley is right, he is a traitor who has been sabotaging Gyro's work on purpose. I do not think he is associated with another villains such as F.O.W.L., he works by his own and this is for the pure pleasure of creating chaos. He does not mind hurting Gyro in the process, Fenton is a wolf in sheep's clothing.

In conclusion, Fenro is abusive because Fenton is an abusive person. That is why I am making the compromise to spread this message for the rest of my life the following week.

And now I am inviting more people over to see the truth and hoping for Fenro shippers to wake up.

#Gyro deserves better

Disclaimer: this is obviously a joke, a joke I'm now commited to.

[human-cyborg-relations]

The software risks of recharging via coupling with an untested power source, especially without an interface adapter, should not be ignored! Always practice Safe Plug-ins! ‼️

[meret118]

Untested new software installed by DOGE employees crashing Social Security servers: report

www.rawstory.com/social-security-trump/

Trump Said Cuts Wouldn’t Affect Public Safety. Then He Fired Hundreds of Workers Who Help Fight Wildfires.

https://www.propublica.org/article/trump-doge-cuts-forest-service-firefighting

The EPA has since announced that it will also modify (i.e., soften) the implementation of the Clean Air Act’s Regional Haze Program to reduce the burden “on power plants and other sectors,” and “reconsider” wastewater regulations for coal-burning power plants.

https://newrepublic.com/article/193631/trump-epa-cuts-lead-pipes-water

As the Trump administration weighs the future of the federal agency tasked with responding to disasters, it is ending a key program that has been used by communities across the country to pay for projects designed to help them prepare for natural disasters like flooding and fires.

https://apnews.com/article/fema-grants-cuts-trump-emergency-management-disaster-bc36ea4ca328e1eb4a07641ba1fb770e

Trump administration 'covertly' denied FEMA relief money for blue states: judge

https://www.msn.com/en-us/politics/government/trump-administration-covertly-denied-fema-relief-money-for-blue-states-judge/ar-AA1CkciB

The White House DOGE office's actions led to 216,670 announced job cuts of federal workers and contractors in March, a new report by a global outplacement firm found.

https://www.businessinsider.com/number-workers-fired-doge-trump-musk-challenger-2025-4?op=1

The DOGE Axe Comes for Libraries and Museums

https://www.wired.com/story/institute-museum-library-services-layoffs/

[fieldbears]

Hi! Thank you for doing this AMA! How did you get started in on your career path? I feel like I keep hearing either "know somebody who has faith in your ability to write technical documents" or "write technical documents for fun" both of which I'm not certain how to start.

Great question! In grade school, my friends and I were little nerds who wrote and traded stories with one another. We were giving honest feedback, learning, writing more, rinse and repeat. A couple of summers, we took writing workshop classes. We learned the general style of workshop critiquing. Being able to deliver and receive criticism is a valuable life and career skill.

Fast forward, I went to college, majored in English with an informal focus on modern literature, and took more creative writing classes! Having been in a workshop environment as a kid REALLY helped prepare me for the ones in college. Not everyone in those classes knew how to receive even very gentle criticism. Learning how to re-calibrate for them was its own learning experience. Another skill!

Fast forward again and I'm in the job market. I thankfully had a friend whose company was hiring, and that started my personal ball rolling. I got in through a friend, yes, but my career advances have been because I'm incredible. :)

But don't get it twisted. Most of the stuff in my background was completely unnecessary to getting an entry-level position. They helped me keep the job, and get more jobs, and get promotions, but nobody is expecting all that workshop experience. If you have average or higher people skills, you can teach yourself how to gently deliver the news that someone is functionally illiterate and you need to fix their work.

What you should have: a 4-year degree majoring in English, a relevant STEM field, or Communications, in that order. You should be ready to clean up a "test" document as part of your interview. You should probably pay close attention to the job description you're applying for, because 'technical documentation' is a really broad field and you need to be able to prove you're ready for whatever flavor they're asking for. Are you writing instructions (how to operate a device)? Processes (how to onboard an employee)? How much of your job will be interviews with SMEs (Subject Matter Experts)? Are you writing a new document at all or updating something that's outdated or untested? You can do some light testing of software, right?

If the interviewers are smart, they will ask you your plan when you're asked to make a new document. Have an answer ready. The information I always want first is: who is this for? Let's say it's an internal guide for some software your company uses. Is everyone on the project an engineer who's very familiar with computers? Or, is it a more generic office, where a 64-year old named Eustace will need very clear instructions on where the start button is? That information is a starting point for figuring out what your document should be.

So tl;dr: 1) Have a relevant degree. 2) Know the specifics of the position. 3) Be ready to walk interviewers through the steps you'd start taking to make a requested document.

Hope this helps <3

[ao3cassandraic]

I'm a little confused, what trouble did the Internet Archive get into exactly?

By this I mean, is it going down? Or is it just dealing with politics behind the scenes?

(I occasionally find the Internet Archive to be useful, so I hope it doesn't disappear)

Okay, so, it helps to have context here. First, IA.

IA has its fingers in several pies:

the Wayback Machine (and allied services such as Archive-It) for website preservation

software (including game) preservation

print digitization, which started (I think) as an add-on to software preservation (because manuals matter!) and expanded to pretty much whatever print IA could get its hands on

a lending system for the above digitized collection, known as the "Open Library"

lately, machine-learning tools intended to operate over its digitized-print collections (it's still building this out, I've seen some of the grant applications) -- nothing generative-AI-like yet that I know of, however

A lot of this work is only dubiously and uncertainly within the scope of US copyright. (N.b. IANAL, IANYL, I am certainly not Internet Archive's lawyer, TINLA.) IA takes refuge largely in audacity, and in the centrality of the Wayback Machine to web preservation generally. So they have been known to pull the "if we lose this legal case totally unrelated to web preservation and have to pay gonzo fines, Wayback is in peril!" ripcord.

Is this true? Hell if I know, I don't audit IA's books. I doubt it, though.

What they're in trouble for -- what an appeals court shot them all the way down for yesterday -- is what they did with their Open Library of digitized print books, many of them in-copyright, during COVID lockdown. And to understand all that, we have to untangle some things about US copyright. Ugh, somebody hand me a read-more link.

Why can libraries lend print books, vinyl, cassettes, CDs, and DVDs in the US? Because of a legal doctrine called "the first sale right," which goes like this: if you have a legally-produced physical object containing copyrighted material, you can do whatever the fuck you want with that physical object with zero copyright implications --other than reproduce/copy or perform it (which does have copyright implications, complex ones).

You can (yes) burn it. You can lend it to a friend, or an enemy, or a random stranger. You can give it away. You can throw it away. You can resell it. You can hang it on your wall or in your window. You can make an art installation with it. And the copyright owner cannot win a copyright-based lawsuit over any of this, even if they hate what you're doing! Even if it competes with them selling new copies (as the resale market absolutely does, and as some jerkfaced copyright owners -- usually corporations, not authors! -- love to complain that libraries do)!

Here's the thing, though, and it's an important thing so I'm gonna big-type it:

The right of first sale does not apply to anything digital ever.

Not ebooks (digitized or born-digital, doesn't matter). Not streaming anything. Not paywalled online news or research.

When libraries offer these to patrons, it's through contracts with publishers or aggregators. Long story short, a lot of these contracts are ridiculously restrictive (not to mention expensive) to the point of cartoonish evil, but it's what we have to work with.

The idea behind Controlled Digital Lending is "if libraries purchased a physical item legally, we should get to lend the item to one person at a time as we always have, and it shouldn't actually matter whether what we lend is the physical item or a digital version of it, as long as only one or the other is out to a patron at a given time."

Which is an untested legal theory! I can't tell you whether it's legal! Nobody can! The case law doesn't exist! Yeah yeah, there's relevant past cases in both directions having to do with accessibility or Google Books or whatever, but a specific precedential ruling on CDL is not a thing that presently exists.

No, not even now. Because what IA did with its Open Library during lockdown, and got slapped down for by the court, is not CDL as defined above. IA didn't hold to one-person-at-a-time-per-book. They tried to make a fair-use argument for what they actually did (that is, not for actual CDL), and the court was not having it.

The thing is, IA's stumblebummed legal fuckup means that actual CDL, as actual libraries (n.b. the IA is not an actual library or an actual archives, I will happily die on this hill, I loathe IA like poison and do not want to admit them to my profession, IA people have dissed me and my work TO MY ACTUAL PHYSICAL FACE and they only love libraries or librarians when trying to hide behind us) were trying to design and implement it, now faces additional legal hurdles. Any court looking at an actual CDL program has to take into account IA getting slapped down. And that's if we can even find a library or library consortium with deep enough pockets and hardcore enough legal representation to even defend such a case.

The thing also is, IA just issued Big Publishing a gilt-edged invitation to use this precedent to sue actual libraries, especially academic libraries, over other things we do. (I'm gonna pass over exactly what in silence because I do not want to give those fuckers ideas, but... there have been past lawsuits, look 'em up.) THANKS, BREWSTER. THANKS EVER SO. Asshole.

For a calmer take than mine, check out Library Futures, which to their credit has not given up all hope for CDL.

This IS the short version of all this nonsense, believe me. I used to teach a whole entire three-credit graduate-level course in the long version. (Which IA would doubtless diss to my face if they knew about it.)

[susidestroyerofworlds]

Software development suggestion: Deploy an untested change with an unstaggered rollout, now.

[werbooz]

Understanding Encryption: How Signal & Telegram Ensure Secure Communication

Signal vs. Telegram: A Comparative Analysis

Signal vs Telegram

Security Features Comparison

Signal:

Encryption: Uses the Signal Protocol for strong E2EE across all communications.

Metadata Protection: User privacy is protected because minimum metadata is collected.

Open Source: Code publicly available for scrutiny, anyone can download and inspect the source code to verify the claims.

Telegram:

Encryption: Telegram uses MTProto for encryption, it also uses E2EE but it is limited to Secret Chats only.

Cloud Storage: Stores regular chat data in the cloud, which can be a potential security risk.

Customization: Offers more features and customization options but at the potential cost of security.

Usability and Performance Comparison

Signal:

User Interface: Simple and intuitive, focused on secure communication.

Performance: Privacy is prioritized over performance, the main focus is on minimizing the data collection.

Cross-Platform Support: It is also available on multiple platforms. Like Android, iOS, and desktop.

Telegram:

User Interface: Numerous customization options for its audience, thus making it feature rich for its intended audience.

Performance: Generally fast and responsive, but security features may be less robust.

Cross-Platform Support: It is also available on multiple platforms, with seamless synchronization across devices because all the data is stored on Telegram cloud.

Privacy Policies and Data Handling

Signal:

Privacy Policy: Signal’s privacy policy is straightforward, it focuses on minimal data collection and strong user privacy. Because it's an independent non-profit company.

Data Handling: Signal does not store any message data on its servers and most of the data remains on the user's own device thus user privacy is prioritized over anything.

Telegram:

Privacy Policy: Telegram stores messages on its servers, which raises concerns about privacy, because  theoretically the data can be accessed by the service provider.

Data Handling: While Telegram offers secure end to end encrypted options like Secret Chats, its regular chats are still stored on its servers, potentially making them accessible to Telegram or third parties.

Designing a Solution for Secure Communication

Key Components of a Secure Communication System

Designing a secure communication system involves several key components:

Strong Encryption: The system should employ adequate encryption standards (e.g. AES, RSA ) when data is being transmitted or when stored.

End-to-End Encryption: E2EE guarantees that attackers cannot read any of the communication, meaning that the intended recipients are the only ones who have access to it.

Authentication: It is necessary to identify the users using secure means such as Two Factor Authentication (2FA) to restrict unauthorized access.

Key Management: The system should incorporate safe procedures for creating, storing and sharing encryption keys.

Data Integrity: Some standard mechanisms must be followed in order to ensure that the data is not altered during its transmission; For instance : digital signatures or hashing.

User Education: To ensure the best performance and security of the system, users should be informed about security and the appropriate use of the system such practices.

Best Practices for Implementing Encryption

To implement encryption effectively, consider the following best practices:

Use Proven Algorithms: Do not implement proprietary solutions that are untested, because these algorithms are the ones which haven't gone through a number of testing phases by the cryptographic community. On the other hand, use well-established algorithms that are already known and tested for use – such as AES and RSA.

Keep Software Updated: Software and encryption guidelines must be frequently updated because these technologies get out of date quickly and are usually found with newly discovered vulnerabilities.

Implement Perfect Forward Secrecy (PFS): PFS ensures that if one of the encryption keys is compromised then the past communications must remain secure, After every session a new key must be generated.

Data must be Encrypted at All Stages: Ensure that the user data is encrypted every-time, during transit as well as at rest – To protect user data from interception and unauthorized access.

Use Strong Passwords and 2FA: Encourage users to use strong & unique passwords that can not be guessed so easily. Also, motivate users to enable the two-factor authentication option to protect their accounts and have an extra layer of security.

User Experience and Security Trade-offs

While security is important, but it's also important to take care of the user experience when designing a secure communication system. If your security measures are overly complex then users might face difficulties in adopting the system or they might make mistakes in desperation which might compromise security.

To balance security and usability, developers should:

Balancing Security And Usability

Facilitate Key Management: Introduce automated key generation and exchange mechanisms in order to lessen user's overhead

Help Users: Ensure that simple and effective directions are provided in relation to using security aspects.

Provide Control: Let the users say to what degree they want to secure themselves e.g., if they want to make use of E2EE or not.

Track and Change: Always stay alert and hands-on in the system monitoring for security breaches as well as for users, and where there is an issue, do something about it and change

Challenges and Limitations of Encryption Potential Weaknesses in Encryption

Encryption is without a doubt one of the most effective ways of safeguarding that communications are secured. However, it too has its drawbacks and weaknesses that it is prone to:

Key Management: Managing and ensuring the safety of the encryption keys is one of the most painful heads in encryption that one has to bear. When keys get lost or fall into unsafe hands, the encrypted information is also at risk.

Vulnerabilities in Algorithms: As far as encryption is concerned the advanced encryption methods are safe and developed well, but it is not given that vulnerabilities will not pop up over the years. Such vulnerabilities are meant for exploitation by attackers especially where the algorithm in question is not updated as frequently as it should be.

Human Error: The strongest encryption can be undermined by human error. People sometimes use weak usernames and passwords, where they are not supposed to, and or even share their credentials with other persons without considering the consequences.

Backdoors: In some cases, businesses are pressured by Governments or law officials into adding back doors to the encryption software. These backdoors can be exploited by malicious actors if discovered.

Conclusion

Although technology has made it possible to keep in touch with others with minimal effort regardless of their geographical location, the importance of encryption services still persists as it allows us to protect ourselves and our information from external invaders. The development of apps like Signal and Telegram has essentially transformed the aspect of messaging and provided their clients with the best security features covering the use of multiple types of encryption and other means to enhance user privacy. Still, to design a secure communication system, it's not only designing the hardware or software with anti-eavesdropping features, but it factors in the design of systems that relate to the management of keys, communication of the target users, and the trade-off between security and usability. 

However, technology will evolve, followed by the issues and the solutions in secure communications. However by keeping up with pace and looking for better ways to protect privacy we can provide people the privacy that they are searching for. 

Find Out More

[mtheland-lockedmartian]

Hi! I think "Tarnian Slip" is such an amazing fic and was wondering how did you come up with language system for such a fic? It been a while but i remember being wowed away by the detail in said fic concerning the different dialects/all the nuances. was it all planned following taking into consideration with the different dialects or written in a spur of the moment kinda thing? purely just a question out of curiosity, but any insights would be enjoyed!

Hey! I wrote it somewhat spur of the moment based on languages I've learned and how they become combined in my head, as well as by talking to some friends of mine interested in linguistics. I think the original inspiration - Orion Pax talking like a gladiator - had been thrown around in conversation or posts somewhere, and I ran with that for the romance aspect. I've found that people really like that fic because of the linguistic aspects, and would love to write something like that again, but at the moment I have 3 fics I'm writing and so many backlogged ideas

Recently another post has been floating around actually, maybe @bad-tf-fic-ideas ? About language learning in transformers. It's a fascinating concept - how much about a language can be translated by downloaded software and how much must be learned. When I wrote tarnian slip I used human cultures as my backdrop. How do we integrate new language into our speech, how does that develop in subcultures like vehicons, with roots in so many languages and thrust into human areas, etc. If I were to write another, I might imagine it like AI -large language learning models. Perhaps language packs can be downloaded easily for most cybertronians, but rely on use and trial an error to sort our the kinks. They work by sequencing the word and assigning it a number, then calculate the probability of the next word. A language pack might listen by taking in a new word, assigning it a number for storage, and calculating a % likelihood that it is being used to mean "a" "b" or "c".

Imagine that Optimus Prime comes to earth, lands in Texas, and downloads an English language pack. Ratchet slaps together a starter pack but it's thoroughly untested and they have no real translators, so the pack is thrown into a language learning model and downloaded into Optimus Prime's processor. OP has a conversation where agent fowler says "wow, the decepticons are mean". The language model is trained on government reports provided by agent fowler. It calculates "mean" has a 76% likelihood of signifying "the average of a set of numbers". Optimus Prime gets very confused until he goes in an looks at the possible synonyms.

I'm getting very off topic, but I think that would be a fun way to look at language packs and language systems in a future fic

Tldr: I thought mostly about the vast array of wildly different immigrant communities in my area and built the tarnian slip systems about them. The fic is about Megatron and Optimus Prime, but actually it's not, it's about the vehicons

[cromacampusinstitute]

Test coverage metrics measure how much of your code is tested by automated tests. They help identify untested areas, such as functions, branches, or lines. To improve coverage, write additional tests for missed paths, use tools like coverage reports, and regularly review test effectiveness during development cycles.

[imarkinfotech089]

Real-Life Examples of Ad Hoc Testing in Software Testing Projetc

Understanding ad hoc testing in software testing becomes much clearer through real-world examples. Consider a situation where a development team was preparing a major release for an e-commerce website. After completing all scripted tests, a tester decided to perform some ad hoc testing on the checkout process. Surprisingly, they discovered a critical bug—users were unable to apply discount codes under certain conditions. This defect had slipped past automated and manual test cases because it only occurred when selecting a specific sequence of products. In another case, a startup building a mobile app performed ad hoc testing during a demo rehearsal. A team member noticed the app crashed when switching rapidly between screens—a behavior users were likely to replicate but wasn’t covered in the test plan. These examples show that ad hoc testing in software testing can reveal bugs that traditional approaches may miss. Its spontaneous, experience-driven nature allows testers to think like users and explore untested paths. These case studies highlight why ad hoc testing remains relevant, especially in agile and fast-paced environments. Although it shouldn't be the only testing strategy, incorporating ad hoc testing into your QA workflow can lead to valuable discoveries and help deliver more reliable software.

[w2gsolutions1]

Study Finds AI Code Mutations Help Developers Catch Bugs Faster

The software development landscape is evolving rapidly, and a recent study finds AI code mutations are revolutionizing how developers identify and fix bugs. This groundbreaking research highlights the potential of artificial intelligence to enhance code quality, streamline debugging processes, and boost productivity. By introducing controlled changes to code, AI-driven mutation testing is proving to be a game-changer for developers striving to deliver robust applications in less time.

What Are AI Code Mutations?

AI code mutations involve using artificial intelligence to intentionally alter software code in small, controlled ways to test its resilience and uncover hidden bugs. Unlike traditional testing methods, which often rely on predefined test cases, mutation testing powered by AI generates dynamic variations of code to simulate potential errors. This approach allows developers to evaluate how their software responds to unexpected changes, revealing vulnerabilities that might otherwise go unnoticed.

The study finds AI code mutations offer a proactive way to stress-test applications. By mimicking real-world scenarios where bugs could emerge, AI helps developers catch issues early in the development cycle. This not only improves software reliability but also reduces the time spent on manual debugging, a process that can be tedious and error-prone.

Why Bug Detection Matters in Software Development

Bugs are the bane of every developer’s existence. Even a minor coding error can lead to significant issues, from application crashes to security vulnerabilities. Traditional debugging methods, while effective to an extent, often fail to catch complex or subtle bugs that only manifest under specific conditions. This is where AI-driven mutation testing shines, offering a more comprehensive approach to quality assurance.

The importance of early bug detection cannot be overstated. Catching issues during the development phase is far more cost-effective than addressing them after deployment. According to industry estimates, fixing a bug post-release can cost up to 30 times more than resolving it during the coding stage. By leveraging AI code mutations, developers can identify and address potential problems before they escalate, saving both time and resources.

How AI Code Mutations Work

AI code mutation tools analyze a program’s source code and introduce small, deliberate changes—known as mutations—to create multiple variants of the original code. These mutations might include altering a mathematical operator, modifying a conditional statement, or swapping variable values. The goal is to simulate potential errors and observe how the software behaves under these altered conditions.

Once the mutated code is generated, it is subjected to the existing test suite. If the tests fail to detect the introduced changes, it indicates gaps in the test coverage. Developers can then refine their tests or fix the underlying code to address these weaknesses. The study finds AI code mutations significantly improve test suite effectiveness, enabling developers to achieve higher code quality with fewer blind spots.

Benefits of AI Code Mutations for Developers

The adoption of AI code mutations offers several tangible benefits for developers and organizations alike. Here are some of the key advantages:

Faster Bug Detection

AI-driven mutation testing accelerates the bug detection process by automating the creation and evaluation of code variants. This reduces the reliance on manual testing, which can be time-consuming and inconsistent. Developers can quickly identify weak points in their code and address them before moving to the next development phase.

Improved Test Coverage

Traditional testing methods often focus on expected use cases, leaving edge cases untested. AI code mutations introduce a wider range of scenarios, ensuring that test suites are more comprehensive. This leads to more robust applications that can handle unexpected inputs or conditions.

Enhanced Code Quality

By identifying and fixing bugs early, AI code mutations contribute to higher-quality software. This is particularly valuable in industries like finance, healthcare, and e-commerce, where reliability and security are paramount. The study finds AI code mutations help developers produce cleaner, more maintainable codebases.

Reduced Debugging Time

Debugging is often cited as one of the most time-intensive aspects of software development. AI-powered tools streamline this process by pinpointing issues with greater accuracy. Developers can focus on writing new features rather than spending hours—or even days—tracking down elusive bugs.

Cost Savings

Fixing bugs early in the development cycle is significantly cheaper than addressing them post-release. By catching issues before they reach production, AI code mutations help organizations save on maintenance costs and avoid potential reputational damage caused by software failures.

Real-World Applications of AI Code Mutations

The study finds AI code mutations are already making an impact across various industries. In web development, for example, AI-driven tools are being used to test complex JavaScript applications, ensuring they perform reliably under diverse conditions. In mobile app development, mutation testing helps developers catch bugs that could cause crashes on different devices or operating systems.

In the realm of cybersecurity, AI code mutations are proving invaluable for identifying vulnerabilities in software. By simulating potential exploits, these tools help developers strengthen their applications against attacks. This is particularly critical for industries handling sensitive user data, such as banking and healthcare.

Challenges and Limitations

While AI code mutations offer significant benefits, they are not without challenges. One potential drawback is the computational resources required to generate and test multiple code variants. For large-scale projects, this can lead to increased processing times and costs. Additionally, not all mutations are equally valuable—some may introduce trivial changes that do not meaningfully improve test coverage.

Another challenge is the learning curve associated with adopting AI-driven tools. Developers unfamiliar with mutation testing may require training to effectively integrate these tools into their workflows. However, as AI technology continues to advance, these challenges are likely to diminish, making mutation testing more accessible to teams of all sizes.

The Future of AI in Software Development

The study finds AI code mutations are just the beginning of AI’s transformative impact on software development. As machine learning algorithms become more sophisticated, we can expect even greater advancements in automated testing, code optimization, and bug detection. Future iterations of AI tools may integrate seamlessly with existing development environments, providing real-time feedback and suggestions to developers as they code.

Moreover, the rise of AI-driven development tools is likely to democratize access to high-quality software testing. Small startups and independent developers, who may lack the resources for extensive manual testing, can leverage AI to compete with larger organizations. This leveling of the playing field could spur innovation and lead to the creation of more reliable, user-friendly applications.

How Developers Can Get Started with AI Code Mutations

For developers eager to explore AI code mutations, the first step is to choose a mutation testing tool that aligns with their programming language and development environment. Popular tools include MutPy for Python, Pitest for Java, and Stryker for JavaScript. These tools offer robust features for generating and analyzing code mutations, with active communities providing support and updates.

Next, developers should integrate mutation testing into their existing workflows. This may involve updating test suites to account for mutated code or configuring CI/CD pipelines to run mutation tests automatically. Starting with small projects can help teams gain familiarity with the process before scaling up to larger codebases.

Finally, developers should stay informed about advancements in AI and mutation testing. The study finds AI code mutations are an evolving field, with new tools and techniques emerging regularly. By keeping up with the latest research and best practices, developers can maximize the benefits of AI-driven testing.

The study finds AI code mutations are transforming the way developers approach bug detection and software quality. By automating the creation of code variants and exposing weaknesses in test suites, AI is helping developers catch bugs faster and build more reliable applications. While challenges remain, the benefits of improved test coverage, reduced debugging time, and cost savings make AI code mutations a compelling tool for modern software development.

[markwateneymemorialcrater]

Got to love how they just push out an untested product for the public to test and troubleshoot.

That’s the mentality of software engineers trying to apply that thinking to real world physical vehicles.

If your video game hasn’t been tested before launch, the bug results in the computer crashing. Annoying. But no one gets hurt.

If your electric vehicle on public roads has a bug where I don’t know, say the accelerator cover slips and locks itself in the “go” position (source) or a power inverter causes the wheels to loses torque and stop spinning (source) or as pointed out above, your steel body panels can come off because of an adhesive failure. Your vehicle might crash or cause someone else to crash and die.

hey... don't cry.... tesla recall for most cybertrucks, okay? >46,000 vehicles affected <3

[gold-for-xp]

Iconic Squads part 3: Solar Unity

As a setting for Beam Saber, the factions of Defined Space will each be comprised of multiple squads. The Iconic Squads aren't necessarily the strongest or most influential of their factions, but they're the most representative of their faction's identity. This next squad is the representative of the Solar Unity

3rd Irregular Reconnaissance Unit “DEAD_DROP”

Tier: IIW

NPCs:  Acting Squad Leader Idris Ivarrson (he/him, calculating, melancholy, candid), Lance Corporal Adelaide Albright (She/her, cooperative, enthusiastic, ferocious), Software Sabotage Specialist Z8 Zephyr (he/him, android, insightful, cavalier, patient), Diplomatic Attache Sakina of Babylon (she/her, sophisticated, enigmatic, charming) 

Notable Assets: Variable-Orbit Communications Satellite, Customized Military-Grade Shells, Mobile Base all-terrain transport vehicle, Non-combatant support cohort

The Solar Unity’s irregular units are hold-overs from the asymmetrical tactics employed by anti-corporation revolutionaries during the Martian Unification conflict. They are the Solar Unity’s special forces, who are usually deployed behind enemy lines, on deep-space missions, or any other scenario which the Unity military cannot bring traditional forces to bear. DEAD_DROP in particular is an orbital deployment squadron, which deploys via unpowered capsules that breach the atmosphere from a stealth satellite. They are expected to complete their mission with no backup or support from Unity forces, and only contact headquarters once their objectives have been completed. 

DEAD_DROP’s most recent mission has been no easy task. The squad was sent to the planet of Andalus, on the border of Beltway Dominion territory, to gather data and run interference against the Dominion military’s activities on the planet following the recent movement of the rogue colony ship Babylon into the system. Their deployment to the surface met unexpected resistance in the form of a Dominion interception which resulted in massive casualties, including the death of the squad's commanding officers. Idris Ivarrson, a career soldier, and Adelaide Albright, an untested rookie, are now the acting heads of DEAD_DROP. In order to pursue their objectives, they have recruited multiple unlikely allies on Andalus, including Z8 Zephyr, an android who fled ALE before his warranty had expired, and Sakina of Babylon, a political dissident against Fleet-Prince Andres. Though their original mission was simply information gathering and sabotage, the members of DEAD_DROP have also come to the aid of the people of Andalus on multiple occasions, and will take action beyond the scope of their assigned duties if they believe it will protect the planet and its people.

Goals: Gather information on Dominion activities on Andalus (8-step clock), Upgrade the Mobile Base defenses (6-step clock)

[aisoftwaretesting]

Fuzz Testing: An In-Depth Guide

Introduction

In the world of software development, vulnerabilities and bugs are inevitable. As systems grow more complex and interact with a wider array of data sources and users, ensuring their reliability and security becomes more challenging. One powerful technique that has emerged as a standard for identifying unknown vulnerabilities is Fuzz Testing, also known simply as fuzzing.

Fuzz testing involves bombarding software with massive volumes of random, unexpected, or invalid input data in order to detect crashes, memory leaks, or other abnormal behavior. It’s a unique and often automated method of discovering flaws that traditional testing techniques might miss. By leveraging fuzz testing early and throughout development, developers can harden applications against unexpected input and malicious attacks.

What is Fuzz Testing?

Fuzz Testing is a software testing technique where invalid, random, or unexpected data is input into a program to uncover bugs, security vulnerabilities, and crashes. The idea is simple: feed the software malformed or random data and observe its behavior. If the program crashes, leaks memory, or behaves unpredictably, it likely has a vulnerability.

Fuzz testing is particularly effective in uncovering:

Buffer overflows

Input validation errors

Memory corruption issues

Logic errors

Security vulnerabilities such as injection flaws or crashes exploitable by attackers

Unlike traditional testing methods that rely on predefined inputs and expected outputs, fuzz testing thrives in unpredictability. It doesn’t aim to verify correct behavior — it seeks to break the system by pushing it beyond normal use cases.

History of Fuzz Testing

Fuzz testing originated in the late 1980s. The term “fuzz” was coined by Professor Barton Miller and his colleagues at the University of Wisconsin in 1989. During a thunderstorm, Miller was remotely logged into a Unix system when the connection degraded and began sending random characters to his shell. The experience inspired him to write a program that would send random input to various Unix utilities.

His experiment exposed that many standard Unix programs would crash or hang when fed with random input. This was a startling revelation at the time, showing that widely used software was far less robust than expected. The simplicity and effectiveness of the technique led to increased interest, and fuzz testing has since evolved into a critical component of modern software testing and cybersecurity.

Types of Fuzz Testing

Fuzz testing has matured into several distinct types, each tailored to specific needs and target systems:

1. Mutation-Based Fuzzing

In this approach, existing valid inputs are altered (or “mutated”) to produce invalid or unexpected data. The idea is that small changes to known good data can reveal how the software handles anomalies.

Example: Modifying values in a configuration file or flipping bits in a network packet.

2. Generation-Based Fuzzing

Rather than altering existing inputs, generation-based fuzzers create inputs from scratch based on models or grammars. This method requires knowledge of the input format and is more targeted than mutation-based fuzzing.

Example: Creating structured XML or JSON files from a schema to test how a parser handles different combinations.

3. Protocol-Based Fuzzing

This type is specific to communication protocols. It focuses on sending malformed packets or requests according to network protocols like HTTP, FTP, or TCP to test a system’s robustness against malformed traffic.

4. Coverage-Guided Fuzzing

Coverage-guided fuzzers monitor which parts of the code are executed by the input and use this feedback to generate new inputs that explore previously untested areas of the codebase. This type is very effective for high-security and critical systems.

5. Black Box, Grey Box, and White Box Fuzzing

Black Box: No knowledge of the internal structure of the system; input is fed blindly.

Grey Box: Limited insight into the system’s structure; may use instrumentation for guidance.

White Box: Full knowledge of source code or internal logic; often combined with symbolic execution for deep analysis.

How Does Fuzzing in Testing Work?

The fuzzing process generally follows these steps:

Input Selection or Generation: Fuzzers either mutate existing input data or generate new inputs from defined templates.

Execution: The fuzzed inputs are provided to the software under test.

Monitoring: The system is monitored for anomalies such as crashes, hangs, memory leaks, or exceptions.

Logging: If a failure is detected, the exact input and system state are logged for developers to analyze.

Iteration: The fuzzer continues producing and executing new test cases, often in an automated and repetitive fashion.

This loop continues, often for hours or days, until a comprehensive sample space of unexpected inputs has been tested.

Applications of Fuzz Testing

Fuzz testing is employed across a wide array of software and systems, including:

Operating Systems: To discover kernel vulnerabilities and system call failures.

Web Applications: To test how backends handle malformed HTTP requests or corrupted form data.

APIs: To validate how APIs respond to invalid or unexpected payloads.

Parsers and Compilers: To test how structured inputs like XML, JSON, or source code are handled.

Network Protocols: To identify how software handles unexpected network packets.

Embedded Systems and IoT: To validate robustness in resource-constrained environments.

Fuzz testing is especially vital in security-sensitive domains where any unchecked input could be a potential attack vector.

Fuzz Testing Tools

One of the notable fuzz testing tools in the market is Genqe. It stands out by offering intelligent fuzz testing capabilities that combine mutation, generation, and coverage-based strategies into a cohesive and user-friendly platform.

Genqe enables developers and QA engineers to:

Perform both black box and grey box fuzzing

Generate structured inputs based on schemas or templates

Track code coverage dynamically to optimize test paths

Analyze results with built-in crash diagnostics

Run parallel tests for large-scale fuzzing campaigns

By simplifying the setup and integrating with modern CI/CD pipelines, Genqe supports secure development practices and helps teams identify bugs early in the software development lifecycle.

Conclusion

Fuzz testing has proven itself to be a valuable and essential method in the realm of software testing and security. By introducing unpredictability into the input space, it helps expose flaws that might never be uncovered by traditional test cases. From operating systems to web applications and APIs, fuzz testing reveals how software behaves under unexpected conditions — and often uncovers vulnerabilities that attackers could exploit.

While fuzz testing isn’t a silver bullet, its strength lies in its ability to complement other testing techniques. With modern advancements in automation and intelligent fuzzing engines like Genqe, it’s easier than ever to integrate fuzz testing into the development lifecycle. As software systems continue to grow in complexity, the role of fuzz testing will only become more central to creating robust, secure, and trustworthy applications.