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Why LambdaTest is a Game-Changer for Cross-Browser Testing

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LambdaTest este o platformă inovatoare care transformă modul în care facem teste de compatibilitate între browsere. Într-o lume în care utilizatorii accesează aplicații web de pe o varietate de dispozitive și browsere, asigurarea că totul funcționează perfect este esențial. Acest articol va explora cum LambdaTest reușește să simplifice acest proces, aducând beneficii semnificative dezvoltatorilor și testerilor. Puncte Cheie Introduction to LambdaTest and Cross-Browser Testing Salutare, pasionați de tehnologie! Astăzi, ne aruncăm într-un subiect esențial pentru orice dezvoltator web modern: testarea cross-browser. Și, mai important, vom explora cum LambdaTest simplifică acest proces complex și îl face mai accesibil ca niciodată. Testarea cross-browser este vitală pentru a ne asigura că aplicațiile web funcționează corect pe toate browserele și dispozitivele. Gândește-te la asta: un site web care arată perfect în Chrome ar putea fi un dezastru total în Firefox sau Safari. Aici intervine testarea cross-browser. LambdaTest este o platformă cloud care oferă o soluție completă pentru testarea cross-browser, permițând dezvoltatorilor să testeze aplicațiile web pe o gamă largă de browsere și sisteme de operare, fără a fi nevoie să configureze infrastructura locală. What is LambdaTest? În esență, [LambdaTest](https://medium.com/front-end-world/25-game-changing-websites-every-developer-must-bookmark-bfbb1399e35f) este o platformă cloud concepută pentru a simplifica testarea cross-browser. Gândește-te la ea ca la un laborator virtual unde poți verifica dacă site-ul tău arată și funcționează corect pe o grămadă de browsere și dispozitive diferite, fără să ai nevoie de o infrastructură complicată. E ca și cum ai avea o armată de calculatoare la dispoziție, gata să testeze orice modificare faci. Prezentare generală a caracteristicilor LambdaTest LambdaTest vine cu o mulțime de funcții interesante. LambdaTest nu e doar un instrument, ci o soluție completă pentru testarea cross-browser. Te ajută să te asiguri că site-ul tău funcționează perfect pentru toți utilizatorii, indiferent de browserul sau dispozitivul pe care îl folosesc. Și asta, în ziua de azi, e esențial. În plus, GitHub Copilot se integrează cu instrumente CI/CD, permițând testarea automată ca parte a conductei de dezvoltare a software-ului. Această integrare asigură că fiecare implementare este testată pe diferite browsere înainte de a fi lansată. The Importance of Cross-Browser Testing În lumea digitală de azi, e vital ca aplicațiile web să funcționeze impecabil pe orice browser. Gândește-te că utilizatorii tăi folosesc Chrome, Firefox, Safari, Edge și multe altele. Fiecare browser interpretează codul diferit, ceea ce poate duce la probleme de afișare, funcționalitate sau performanță. Testarea cross-browser e ca o asigurare că aplicația ta web arată și funcționează corect, indiferent de preferințele utilizatorilor. Provocări în Testarea Cross-Browser Testarea cross-browser nu e întotdeauna simplă. Sunt multe aspecte de luat în considerare: Testarea cross-browser nu e doar despre a face ca site-ul tău să arate bine pe toate browserele. E despre a oferi o experiență consistentă și plăcută pentru toți utilizatorii, indiferent de modul în care accesează site-ul tău. O experiență bună înseamnă clienți mulțumiți, iar clienții mulțumiți revin. Testarea cross-browser este esențială pentru a asigura o experiență de utilizare optimă și pentru a evita pierderi de clienți din cauza problemelor de compatibilitate. Digital.ai a lansat o actualizare Denali pentru platforma DevSecOps, îmbunătățind integrarea modelelor AI personalizate. Ignorarea acestei etape poate duce la frustrare, abandon și, în cele din urmă, la pierderi financiare. Folosind instrumente precum LambdaTest, poți simplifica procesul și te poți asigura că aplicația ta funcționează perfect pentru toți. How LambdaTest Revolutionizes Cross-Browser Testing LambdaTest nu doar […]

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What Is The mt19937 Random Generator In Modern C++?

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Random numbers are one of the most important parts of today’s modern programming technologies. They are used in mathematics, physics, in many engineering fields, and in programming such as generating random data for testing, random maps in levels, random trees on a planet – the list is endless. Since C++11, mt19937 (std::mt19937) is implemented as a random number generator. In this post, we explain what mt19937 is and how we can use it. What is a random number and a random number generator in C++? A random number is a number that is randomly chosen in a given range. It is impossible to predict future values based on past or present values and they are uniformly distributed over a defined interval or set. Mersenne prime is a prime number used in mathematics that is a number of the form Mn = 2n − 1 where the n is an integer. The Mersenne Twister is a pseudorandom number generator where the period length is chosen to be a Mersenne Prime. It was developed by Makoto Matsumoto in 1997. Since C++11, the Mersenne Twister mathematical number generator is implemented as a random generator number, it is defined in the header as a std::mersenne_twister_engine that is a random number engine based on Mersenne Twister algorithm. What is the std::mt19937 random number generator in modern C++? In C we use rand(), srand() and in C++ we use std::rand(), std::srand(). While they are added to  to make modern C++ compatible, there are more useful and modern random number generators. These are std::mt19937 and std::mt19937_64. The std::mt1993 is a 32-bit Mersenne Twister by Matsumoto and Nishimura in 1998, and std::mt19937_64 is a 64-bit Mersenne Twister by Matsumoto and Nishimura in 2000. The std::mt19937 is a random number generator defined in the header in C++17 standard and beyond, producing 32-bit pseudo-random numbers by using the Mersenne Twister algorithm with a state size of 19937 bits. This is why it is called mt19937 and there is a 64-bit version called mt19937_64. Both are defined as an instantiation of the mersenne_twister_engine. Now let’s see their definitions. Since C++11, mt19937 is defined as below,   typedef mersenne_twister_engine mt19937;   Since C++11, mt19937_64 is defined as below,   typedef mersenne_twister_engine mt19937_64;   How can we use the random number generator std::mt19937 in modern C++? Simply we can generate modern random number as shown below.   std::mt19937 rnd( std::time(nullptr) );   we can use it like so:   unsigned int r = rnd();   if you want to generate a number in a range you can use modulus operator %.This is how can we generate random number between zero to n, i.e. 0 to 100.   unsigned int r = rnd()%100;   This is how can we generate random number between two numbers, i.e. 50 to 150,   unsigned int r = 50 + rnd()%100;   Is there a simple example to use std::mt19937 in modern C++? Here is a simple example to use std::mt19937.   #include #include #include   int main() { std::mt19937 rnd( std::time(nullptr) );   std::cout

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How To Create A Real App That Runs In The Cloud

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Hello developers. Our previous sessions in our Winter Webinars series which showed you how to create a real Android app step by step“, how to create a real iOS app (even if you do not have a mac), how to create a real windows app, create a real Mac app, are extremely popular. It’s great to get positive feedback on the webinar content too. In a previous session I covered how to create apps which work on Linux using RAD Studio 12 and Delphi. Today I went on to cover the various ways we can use RAD Studio with Delphi (or C++ Builder) to create apps which run in the cloud. Each session builds a little on the things we learned in prior webinars and adds to that knowledge. Over the next few weeks, we’ll link things up to the cloud, the web, each other, and even a robot arm. Stick around; we’re going to see that RAD Studio can do pretty much anything you can dream of – and do it without needing to be a super hardcore software developer too. If you want to register, go to: https://lp.embarcadero.com/webinar-registration In this article you can catch the replay of the presentation content and the slides. If you watch on YouTube, please hit the “like” and “subscribe” buttons to make sure you get notifications of all the videos in the Winter Webinar series. Hitting “like” and “subscribe” on YouTube will not add you to any mailing lists from Embarcadero – the only effect is for YouTube to send you a notification the next time we upload a new webinar or start a live broadcast. Where can I see the replay of the “How To Create A Real App That Runs In The Cloud” webinar? Here’s the full replay of the video.   All the video replays are also uploaded to our YouTube channel. You can also find them in the “Learn” section of the RAD Studio IDE Welcome page. The plan is, as time goes on, for me to fill that “Learn” tab with a whole series of videos which take you through every aspect of creating cross-platform and desktop apps with RAD Studio on Windows, macOS, Linux, iOS, and Android. Where can I get the slides for the “How To Create A Real App That Runs In The Cloud” step by step guide? Here are all the slides for “How To Create A Real App That Runs In The Cloud”. Reduce development time and get to market faster with RAD Studio, Delphi, or C++Builder. Design. Code. Compile. Deploy. Start Free Trial   Upgrade Today    Free Delphi Community Edition   Free C++Builder Community Edition

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Diffblue Integrates Generative AI-Based Testing Platform With GitLab

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Diffblue this week generally made available an integration between its automated unit testing platform for Java and the DevSecOps platform from GitLab. The Diffblue Cover platform makes use of generative artificial intelligence (AI) to automatically create unit tests based on reinforcement learning technologies that don’t rely on LLMs—avoiding their drawbacks of sometimes introducing hallucinations and also requiring human review. Diffblue CEO Mathew Lodge said the integration with the continuous integration capabilities found in the premium and ultimate editions of the GitLab platform would, for example, streamline regression testing in a way that should ultimately improve both application quality and security. Diffblue Cover, for example, can update tests as much as 250 times faster than a human developer can write them manually without developer review. That approach also serves to reduce the level of friction many DevSecOps teams encounter when bottlenecks involving testing processes emerge, noted Lodge. The overall goal is to make it simpler for developers to test as much of their own code as possible before integrating it into a build, he added. Otherwise, developers will get fed up because testing is continuously breaking the build, noted Lodge. Ultimately, instead of having to write unit tests, developers become supervisors of a platform that automatically generates them on their behalf, said Lodge. The job of a developer doesn’t go away, but it does fundamentally change, he added. To achieve that goal, developers need to be able to access a platform that writes the tests and can then also execute them automatically. If it takes too long to create the test, chances are high that most developers won’t run it. On average, writing and evaluating a single unit test can take a developer 10 minutes. Over the course of any project, thousands of tests need to be written, so the amount of time testing takes away from coding is often much greater than most IT leaders fully appreciate. Automating those tests should improve developer productivity as more time is available to focus on writing code rather than testing. That doesn’t necessarily eliminate the need for a dedicated testing team, but it does mean that more tests will be run without slowing down the overall pace of application development. Developers naturally want to be able to test code at the very instant they create it. AI platforms can make that happen by, for example, employing reinforcement learning to write unit regression tests. Most developers are not going to resist assuming more responsibility for testing if the tools to automate that task are more accessible. Instead of merely shifting responsibility for testing left toward developers, DevOps teams need to find ways to streamline the process. Otherwise, testing just becomes one in a series of tasks that are being shifted to developers in ways that many of them are coming to resent. It may take some time before AI is fully integrated into software engineering but it’s clear with each passing day more previously manual tasks are being automated. Among the lowest hanging fruit for applying AI to software engineering are clearly testing processes that, if truth be told, few indeed enjoy conducting.

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SmartBear Acquires Reflect to Gain Generative AI-Based Testing Tool

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SmartBear this week revealed it has acquired Reflect, a provider of a no-code testing platform for web applications that leverages generative artificial intelligence to create and execute tests. Madhup Mishra, senior vice president of product marketing at SmartBear, said the platform Reflect created will initially be incorporated into the company’s existing Test Hub platform before Reflect’s generative AI capabilities are added to other platforms. Reflect provides access to a natural language interface to create tests using multiple large language models (LLMs) that it is designed to invoke. It can also understand the intent of a test to understand what elements to test regardless of whether, for example, a button has been moved from one part of a user interface to another, said Mishra. Test step definitions, once approved, can also be automatically executed using scripts generated by the platform. SmartBear has no plans to build its own LLMs, said Mishra. Rather, the company is focusing its efforts on providing the tools and prompt engineering techniques needed to effectively operationalize them, he added. Reflect is the tenth acquisition SmartBear has made as part of an effort to provide lightweight hubs to address testing, the building of application programming interfaces (APIs) and analysis of application performance and user experience. Last year, the company acquired Stoplight to gain API governance capabilities. Rather than building a single integrated platform, the company is focused on providing access to lightweight hubs that are simpler to invoke, deploy and maintain, said Mishra. The overall goal is to meet IT teams where they are versus requiring them to adopt any entirely new monolithic platform that requires organizations to rip and replace every tool they already have in place, he said. There is little doubt at this point that generative AI will have a profound impact on application testing in a way that should ultimately improve the quality of the applications. As the time required to create tests drops, more tests will be run. Today, it’s all too common for tests not to be conducted as thoroughly as they should be simply because either a developer lacked the expertise to create one or, with a delivery deadline looming, they simply ran out of time. Naturally, the rise of generative AI will also change how testing processes are managed. It’s not clear how far left generative AI will push responsibility for testing applications, but as more tests are created and run, they will need to be integrated into DevOps workflows. Of course, testing is only one element of a DevOps workflow that is about to be transformed by generative AI. DevOps teams should already be identifying manual tasks that can be automated using generative AI as part of an effort to further automate workflows that, despite commitments to automation, still require too much time to execute and manage. Once identified, DevOps teams can then get a head start on redefining roles and responsibilities as generative AI is increasingly operationalized across those workflows.

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How To Use void_t Alias Template in C++ 17?

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In C++ 17, there is a very useful alias template for metaprogramming that can be used to simplify use of SFINAE. The void_t is a meta-function that is used to map any types ( or types) to type void. In this post, we explain what is void_t, how you can use it in different examples. What is alias template void_t in C++ 17? The void_t is an alias template which is introduced with C++17, it is defined in header and it is a metafunction that is used to map any types (or a sequence of any types) to type void. The main purpose of void_t is to make easy writing of type traits. It is used to solve SFINAE (Substitution Failure Is Not An Error) prior to concepts of C++20 standard. SFINAE rule says that If the deduced type or explicitly specified type for the template parameter fails, the specialization is discarded from the overload set instead of causing an error in compilation. Since C++17, the std::void_t is defined as below.   template using void_t = void;   Now, let’s see how we can use it in template definitions. How to use alias template void_t to check a typename in a template? We can use void_t to if a class has a certain typename using at compile time, here we check if it has ‘type’ typename.   template struct hastype : std::false_type {};   template struct hastype : std::true_type {};   here is a full example about this, here we check ‘typeB‘ typename if it has or not. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28   #include #include   struct stA { typedef int typeA; };   struct stB { typedef int typeB; }; template struct hastype : std::false_type {};   template struct hastype : std::true_type {};   int main() { std::cout

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How To Create A Real Linux App Step By Step Guide

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Hello developers. Our previous sessions in our Winter Webinars series which showed you how to create a real Android app step by step“, how to create a real iOS app (even if you do not have a mac), how to create a real windows app, and how to create a real Mac app, are extremely popular. Have you spotted the theme yet? ???? During the previous sessions I showed how to use RAD Studio 12 to create multi-platform apps to target Android, iOS and Windows devices – and in today’s session I covered how to create apps which work on Linux using RAD Studio 12 and Delphi. The session today was completely live, even though I prefer to pre-record some demos to make myself look slicker and more professional! I covered the key parts of setting up your Linux machine, or your WSL instance, and then RAD Studio so you can easily compile Linux apps. Each session builds a little on the things we learned in prior webinars and adds to that knowledge. Over the next few weeks, we’ll link things up to the cloud, the web, each other, and even a robot arm. Stick around; we’re going to see that RAD Studio can do pretty much anything you can dream of – and do it without needing to be a super hardcore software developer too. If you want to register, go to: https://lp.embarcadero.com/webinar-registration In this article you can catch the full replay including the questions and answers. If you watch on YouTube, please hit the “like” and “subscribe” buttons to make sure you get notifications of all the videos in the Winter Webinar series. Hitting “like” and “subscribe” on YouTube will not add you to any mailing lists from Embarcadero – the only effect is for YouTube to send you a notification the next time we upload a new webinar or start a live broadcast. Where can I see the replay of the “How To Create A Real Linux App Step By Step Guide” webinar? Here’s the full replay of the video. All the video replays are also uploaded to our YouTube channel. You can also find them in the “Learn” section of the RAD Studio IDE Welcome page. The plan is, as time goes on, for me to fill that “Learn” tab with a whole series of videos which take you through every aspect of creating cross-platform and desktop apps with RAD Studio on Windows, macOS, Linux, iOS, and Android. Where can I get the slides for the “How To Create A Real Linux App” step by step guide? Here are all the slides for “How To Create A Real Linux App Step By Step Guide”. Reduce development time and get to market faster with RAD Studio, Delphi, or C++Builder. Design. Code. Compile. Deploy. Start Free Trial   Upgrade Today    Free Delphi Community Edition   Free C++Builder Community Edition

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2024 Infrastructure Tech Predictions

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Ganesh Srinivasan, partner at Venrock, co-authored this article. 2023 was a rollercoaster like none other; from the death of the modern data stack sprawl to the birth of generative AI, we are only at the beginning of a new era in the ‘art of the possible.’ We guarantee 2024 won’t be a disappointment. With a new year approaching, it’s the perfect time for us to examine what we anticipate being the biggest developments in the year ahead. Here is what we think is going to happen in 2024: 1. OpenAI’s Reign Challenged With the emerging learnings in core neural net architectures that led to the transformer and dominance by OpenAI, it is likely that their imminent release of GPT5 will be surpassed in specific performance benchmarks by a new entrant on the backs of more efficient architectures, improved multimodal capabilities, better contextual understanding of the world and enhanced transfer learning. These new models will be built on emerging research in spatial networks, graph structures and combinations of various neural networks that will lead to more efficient, versatile and powerful capabilities. 2. Apple: The New Leader in Generative AI One of the most important players in the generative AI space is only starting to show their cards. 2024 will be the year Apple launches its first set of generative AI capabilities, unlocking the true potential of an AI-on-the-edge, closed architecture with full access to your personal data – showing that Apple is actually the most important company in the generative AI race. 3. Building for Client-First The last decade has reflected a shift away from fat clients to server-side rendering and compute. But the world is changing back to the client. Mobile-first experiences will be required to work in offline mode. Real-time experiences require ultra-low latency transactions. Running LLMs will increasingly be required to run on the device to increase performance and reduce costs. 4. Death of Data Infrastructure Sprawl The rapid growth of the data infrastructure needs of enterprises has led to an increasing sprawl of point solutions, from data catalogs, data governance, reverse extract, transform, load, and airflow alternatives to vector databases and yet another lakehouse. The pendulum will swing back to unified platforms and fewer silos to bring down the total cost of ownership and operating overhead going into 2024. 5. Approaching the AI Winter Generative AI in 2023 could be best characterized as the ‘art of the possible,’ with 2024 being the true test to see if prototypes convert into production use cases. With the peak of the hype cycle likely here, 2024 will experience the stage of disillusionment where enterprises discover where generative AI can create margin-positive impact and where the costs outweigh the benefits. 6. The Misinformation Threat While image and video diffusion models have unlocked a new era for digital creation and artistic expression, there’s no doubt that its dark side has not yet taken its toll. With a presidential election in the wings, diffusion models as a machine for political disinformation will emerge to become the next major disinformation weapon of choice. 7. AI’s Real-World Breakthrough Coming out of the ‘field of dreams’ era for AI, 2024 will represent a breakthrough for commercial use cases in AI, particularly in the physical world. Using AI for physical world modalities will unlock our ability to […]

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How To Use std::invoke In C++ 17?

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There is a new library feature in the C++17 standard, it is std::invoke which is a useful feature to uniformly invoke callable entities. In this post, we explain what std::invoke is and how we can use it in examples. First, let’s remind ourselves about what is a callable object and what is a functor in modern C++. What is callable object and what is functor in modern C++? A callable object (some call it a functor) is an object that can be used as a function or function pointer by using the operator(). This term is not the same as a function term in programming. We can pass many arguments with them; thus, we don’t need to define many global variables, we can use these kinds of variables in the scope that we use. Here you can find more details about it. What is std::invoke in C++ 17? The std::invoke call is a library feature in C++ 17 that allows invoking a method at run time and improved in C++20 and C++23 with invoke_r. It is defined in the  header and useful to write libraries with the same behavior as the standard’s magic INVOKE rule. You can use std::invoke to call a function or method, a lambda expression, or a member function, or can be used to access a data member, or you can use to invoke a function object. In C++17 it is defined as below,   template std::invoke_result_t     invoke( F&& f, Args&&… args ) noexcept();   In C++20 it is defined as below,   template constexpr std::invoke_result_t     invoke( F&& f, Args&&… args ) noexcept();   And since C++23, there is invoke_r and it is defined as below,   template constexpr R invoke_r( F&& f, Args&&… args ) noexcept();   How can we use std::invoke with a parametric function in C++17? Here is a simple std::invoke example with a parametric function. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18   #include   int myf(int x, int y) { return x*x+y*y; }   int main() { int z = std::invoke(myf, 1, 2);   std::cout

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The Future of DevOps: Predictions and Insights From Industry Experts

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DevOps is a crucial part of the ever-evolving field of technology, shaping the future of software development and operational efficiency. Here are the trends, transformations and breakthroughs that will redefine the DevOps landscape in 2024. 2024: The Year for DevOps In 2024, DevOps is poised for a transformative journey. Automation is predicted to surge to unprecedented levels, reshaping development workflows and expediting deployment cycles. Continuous integration and continuous delivery (CI/CD) pipelines are expected to attain new heights of efficiency, facilitating rapid and reliable software releases. DevOps, synonymous with agility, is foreseen as a key driver of innovation and efficiency in software development.Expert Insight: Ramendeep Bhurjee, VP, Cigniti Technologies. BizDevOps Redefines Software 2024 will witness BizDevOps redefining how businesses approach software development and operations. The integration of business stakeholders into the development process is expected to reach new levels of maturity. Continuous feedback loops between business, development and operations teams will become standard practice. Automation will undergo further refinement, enabling swift adaptation to changing market dynamics.Expert Insight: Raghu Krovvidy, chief delivery officer, Cigniti Technologies. DevOps and Agile Convergence A convergence between DevOps and Agile practices is anticipated to enhance software development. Breaking down silos and improving collaboration for faster, high-quality development is the goal. Tools supporting continuous integration and delivery are deemed crucial in this integrated approach, streamlining the path from development to deployment.Expert Insight: Paul Lechner, VP of product management, Appfire. Faster Development Life Cycles Continue The relentless march towards faster development life cycles to meet escalating demand is expected to persist in 2024. As organizations push new applications into production more swiftly, a focus on real-time security practices within the CI/CD pipeline is crucial during source code development.Expert Insight: Dan Hopkins, VP of engineering, StackHawk. Agile Development Shapes the Future In the realm of development, agile practices will continue shaping the future of innovation by incorporating advanced technologies and methodologies. The adoption of Scaled Agile Frameworks like SAFe is predicted to be a significant facet of agile development in 2024.Expert Insight: Nitin Garg, VP of practice delivery, AgreeYa Solutions. Fostering a Human-Centric Agile Mindset Companies are expected to realize that agile transformation must be holistic, involving shorter cycles and business-side changes beyond just software. A shift towards reinvigorating the human-centric aspects of agile development is seen as essential for success.Expert Insights: Tina Behers, VP of enterprise agility, Aligned Agility; Jon Kern, Agile Consultant, Adaptavist. Moving From Tracking Developer Productivity to Engineering Efficiency Leaders are anticipated to shift their focus from tracking individual developer productivity to engineering efficiency. The measurement will transition from individual metrics to team-centered metrics around engineering efficiency.Expert Insight: Ori Keren, co-founder and CEO, LinearB. Collaboration – The Future of DevOps In the era of multi-cloud architectures and diverse vendor reliance, the future of DevOps is expected to hinge on strengthened collaboration. DevOps professionals are set to forge robust partnerships with traditionally siloed teams, emphasizing automation to seamlessly engage at critical junctures.Expert Insight: Erez Tadmor, Cybersecurity Evangelist, Tufin. Recognition of the 99% Developers Businesses are predicted to recognize the significance of the “99% Developers” who work outside the limelight but contribute significantly to software development. Understanding the needs of this majority is seen as crucial for sustained business success.Expert Insight: Jean Yang, Head of Product, Observability, Postman. Debugging Remains a Challenge Debugging is expected to remain a […]

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