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The spooky season is around the corner and we couldn't let it pass without sharing some horror movie suggestions, even for the weak-hearted. In this slogging thread, the HN team discusses their shared fear for horror films and a few movie suggestions for this Halloween. This thread by Mónica Freitas, Ellen Stevens, Linh Smooke, Limarc Ambalina and Jack Boreham occurred in slogging's official #random channel, and has been edited for readability. Slogging Mónica Freitas Oct 21, 2021, 2:55 PM As we get in the spooky season, what are some of your horror movies favorites? Mónica Freitas Oct 21, 2021, 2:59 PM I have to say I'm not big on horror movies unless it's daytime - I belong to that group that has nightmares after watching those kinds of movies. But I'm open to giving it another try if you have any suggestions. 👻 ❤ 1 Ellen Stevens Oct 21, 2021, 9:28 PM Literally was coming here to post this. I know it's cool to love horror movies, but I think there's enough horror in real life. My brain doesn't need to process any more haha. 😂 1 💚 1 Linh Smooke Oct 21, 2021, 10:41 PM Not big on horror movies either, since I’m such a scared cat… BUT, I learned recently that “spooky” has a racial undertone and I’m like whattttt 🥺 https://www.npr.org/sections/codeswitch/2017/10/24/559502238/this-halloween-what-does-it-mean-to-call-something-spooky Mónica Freitas Oct 25, 2021, 1:55 PM Linh Smooke omg! 🙊 I didn't know THAT! Mónica Freitas Oct 25, 2021, 1:56 PM Linh Smooke Ellen Stevens got us a guide for horror movies for people like us, weak-hearted: https://www.gq.com/story/horror-movies-for-people-who-hate-horror-movies ✅ 1 ❤ 1 Mónica Freitas Oct 25, 2021, 1:57 PM Jack Boreham, Limarc Ambalina Spill the beans! You're clear horror movies' fans. Linh Smooke Oct 25, 2021, 5:20 PM For the weak-hearted yas!!! that’s me! Linh Smooke Oct 25, 2021, 5:21 PM Will check them out. Been meaning to check out Squid Game and even that kinda makes me nervous because I heard that it's kinda scary, lol. I do watch through the entire Parasite movie and all Black Mirror episodes though! Then swore them off for the entire year after since I got too scared lol Ellen Stevens Oct 25, 2021, 8:18 PM I appreciate what that link is trying to do, but my brain has a tendency to shut off anything that looks even remotely suspicious. It's actually not ideal because I almost missed some awesome thriller movies doing that. There's something about horror, that just doesn't sit well with me. I'm really inquisitive, and I don't want my mind to start exploring or focusing on all that stuff. I don't think it's super healthy. Ellen Stevens Oct 25, 2021, 8:31 PM I generally watch things that have a highly intelligent, special main character, who doesn't adhere to social norms or a common viewpoint, but generally contributes to help society in some way. An anti-hero. I love the Blacklist ( by the way, the new season is out and I'm so happy about that), Sherlock Holmes, House of Cards, etc. It has to be intellectually stimulating. If there are too many fight scenes, blood scenes, romantic comedy foolishness, or if it's clearly trying to push some biased agenda, I immediately turn it off. Why people spend hours watching the mutilation of people, is just completely surreal to me. That's not profound, it's a waste of time. It's gross. Unnecessary and zero value add. Ellen Stevens Oct 25, 2021, 8:35 PM Tried watching Squid Game. SPOILER ALERT, don't read past this! It starts off with a person who spends all his money on gambling instead of his kid, and we're supposed to feel empathy towards his horrible decisions? Turned it off. Limarc Ambalina Oct 26, 2021, 4:01 AM I don't know how Mónica Freitas knew this but I absolutely love horror. I'm writing a zombie novel right now 😄 . My favorite zombie horror film is 28 days later. Favorite zombie tv show is Black Summer. In terms of Horror franchise, I really love when they're based on a true story to add creepiness, and with that in mind, I recommend The Conjuring. It isn't all jump scares, it's a great blend of yes some jump scares but a good story that creates a truly horrific atmosphere. Jack Boreham Oct 26, 2021, 7:32 AM Mónica Freitas, I wish I was, but honestly, I can't stand them. I'm a big scaredy-cat when it comes to horror movies; I can't watch 15 mins of one without losing my mind. I'm not sure what it is; I guess I wouldn't say I like being scared. Watching classic movies such as Psycho and even the Scream movies have etched into my mind a sense of fear. On a side note, I played a scary game on the oculus quest the other day. I lost my absolute mind. I not only screamed but nearly broke the headset 😂. Mónica Freitas Oct 26, 2021, 12:55 PM Linh Smooke Oh, Black Mirror is a mind twister. I could only watch one or two episodes at a time and take the rest of the day off to process them. Squid game! Same, I watched the first episode and half of the second, so I'm not really able to give you a decent heads up. The first episode, though it's weird, and there are killings, it's not exactly gore. But I'm guessing it gets more hardcore as the series goes on. Did you watch "Get out"? I managed to see that one. Then I found out there was a similar one called "Us" from the same director, but the trailer scared me off. So I get you 😂 Mónica Freitas Oct 26, 2021, 1:01 PM Ellen Stevens Oh, I'm a big fan of Sherlock! I get it. It's always fun watching that one character outsmart everyone when you thought it was doomed. Have you ever watched "Westworld"? It has that same feel - you don't really know what's going on, and you're trying to puzzle everything together. "Dark" is also a great series, but it's focused on the concept of time and time travel and how if time wasn't linear, it could have a huge domino effect. Mónica Freitas Oct 26, 2021, 1:06 PM Limarc Ambalina I can't handle movies with spirits possessions...it gets stuck in my mind. I thought you'd mention "The walking dead" as it was this massive thing for zombie fans. Want to try and sell me on "Black Summer"? I can never shake the idea that zombies are always the same in every movie - dumb, decomposing, brain eaters. Mónica Freitas Oct 26, 2021, 1:08 PM Jack Boreham Welcome to the team, ahaha. I feel you! I can only watch scary movies if it's daylight, I got company, and I'm busy doing other things...if I'm too focused, I won't be able to sleep that night. I feel almost like a kid hiding under the covers. Is there any other movie that has become your Haloween tradition? Limarc Ambalina Oct 27, 2021, 11:34 AM Mónica Freitas then you'll easily be sold by Black Summer. The draw of the show is that the filmography and pacing are so good that you're constantly on edge. You know how in horror films, you have a jump scare and then a sigh of relief and then a break until the next scary thing? Black summer is 100% tension and it usually just never breaks. Which is what you'd really be feeling in a zombie apocalypze wouldn't you? Constant fear. The tv show creates that atmosphere so well. Limarc Ambalina Oct 27, 2021, 11:34 AM " I can't handle movies with spirits possessions...it gets stuck in my mind." - then don't watch The Conjuring or The Haunting of Emily Rose 😄 😂 1 Limarc Ambalina Oct 27, 2021, 11:34 AM Both "based on true stories" 😂 1 Jack Boreham Oct 27, 2021, 1:40 PM Mónica Freitas I wouldn't say movie but rather cartoon traditions. I loved the Simpsons Halloween specials as well as other cartoon specials such as the SpongeBob ones. I guess I like them because they are not realistic 😂 https://youtu.be/FxY5FBtjCfM Mónica Freitas Oct 28, 2021, 10:49 AM Limarc Ambalina Ok, I'll give it a shot! Black summer it is. Oh, I'm not watching anything that has conjuring and haunting on the title 😂 Mónica Freitas Oct 28, 2021, 10:51 AM Jack Boreham I used to love them too! They were just the weirdest episodes. 😂

Horror Movies For the Weak (and Not So Weak) Hearted

Imagine you're tasked with implementing a geo-fencing feature for your service. At first glance, dropping in an IP GeoLocation API might seem straightforward, but often, project constraints or requirements complicate the simplest solutions. Let's explore our options. Local GeoIP Database Implementation This method involves embedding a geolocation database directly within your application. It offers fast response times as it resides in-memory and operates independently of external services. However, this approach can be resource-intensive and harder to manage. Let’s see it in more detail. Pros Speed: IP lookups in milliseconds or less.
Reliability: No reliance on third-party services reduces failure chances.
Scalability: No extra costs as usage grows. Cons Resource Intensive: Each app instance consumes significant memory.
More Dependencies: Requires packages for decoding and looking up GeoIP data.
Challenging Updates: Updates require manual intervention or another self-hosted service. Using External GeoIP APIs (Often Paid) Opting for a third-party API to handle geolocation queries reduces your application's resource load and ensures access to the most current data, but it comes with its own challenges: Pros Minimal Resource Use: Outsourcing GeoIP data and logic doesn't burden your app's resources.
Ease of Implementation: Usually involves consuming a standard HTTP/GraphQL or gRPC API endpoint.
Fewer Dependencies: Often only requires an HTTP client.
Updates without hassle: Third-party will handle updates Cons Slower Responses: Calls to third-party services can introduce significant latency.
Increased Unreliability: Greater likelihood of failures due to network issues or third-party downtimes.
Scaling Costs: Costs can escalate as usage increases. Introducing Atlas: Combining the Best of Both Worlds Faced with these challenges, I developed Atlas—a robust solution designed to synergize the benefits of both approaches. Atlas acts as a self-hosting HTTP service that automatically manages and serves the latest MaxMind geolocation databases, providing: Low Latency: Similar to local databases, Atlas offers rapid response times. (often in a few milliseconds) Reliability and Ease of Updates: It autonomously handles database updates, removing the typical maintenance burdens. Cost Efficiency: No additional costs with scale in terms of third-party fees. Dead-Simple API: A very simple API that lets you implement quicker and focus on what matters. Open and Free: Atlas is Open Source and freely available under Apache-2.0 License. By hosting Atlas closer to your application but operating like a remote API, it offers a middle ground that maximizes efficiency without the drawbacks of either method we have discussed. Why Rust? Safe, Performant, and Resource-Efficient Atlas is written in Rust, a language chosen for its unparalleled safety, performance, and resource efficiency. Rust's ownership model ensures memory safety without the need for a garbage collector, which is particularly advantageous during database updates—these operations consume minimal memory, avoiding the typical overhead associated with garbage-collected languages. This design choice not only boosts Atlas's performance but also enhances its reliability, making it an ideal solution for managing GeoIP data efficiently and safely. How to Deploy and Use Atlas? Atlas Docker images are automatically built and pushed to Docker Hub via GitHub Actions, making it easy for anyone to use. For detailed instructions on using these Docker images or building Atlas from source and configuration parameters, please refer to the Atlas README.md. Atlas features a straightforward API, documented with OpenAPI 3.0 Specs. You can access the API documentation from the public internet here: https://atlas-rs.fly.dev/swagger-ui/. Here’s an example of how you can use curl to query the demo instance of Atlas (hosted publicly thanks to Fly.io and the GeoLite2 MaxMind database license) to retrieve country information for an IP address 8.8.8.8: > curl https://atlas-rs.fly.dev/geoip/lookup/country/8.8.8.8 | jq
{
  "results": {
    "8.8.8.8": {
      "country": {
        "iso_code": "US",
        // additional fields...
      },
      // additional results...
    }
  },
  "database_build_epoch": 1715091859
} Please note that this demo instance is intended only for demonstration and documentation purposes. Join the Atlas Community and Contribute As a newly introduced open-source project, Atlas is in the early stages of building its community. I am excited about the potential for Atlas to evolve and thrive with contributions from developers like you. Whether you're interested in refining functionality, adding new features, or simply improving documentation, your contributions are welcome and greatly valued. How You Can Contribute Try Atlas: The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage.


Report Issues: If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement.


Suggest Features: Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request.


Improve Documentation: As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable.


Spread the Word: If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Commitment to Open Source I am relatively new to Rust but bring many years of experience from other technology stacks. This project presents a great opportunity for more experienced Rust developers to contribute, ensuring that Atlas adheres to Rust's best practices and conventions since I know that it is not as good as it can be right now. I am committed to maintaining Atlas as a community-driven project and believe in open, collaborative development. I am eager to see how the community will shape Atlas's path forward and encourage you to join me in this journey, helping to build a tool that serves the needs of developers globally. In the end, I would like to give a special thanks to oschwald for creating the rust maxminddb crate which made Atlas possible. Future Directions As we continue to develop and improve Atlas, my vision is to create a robust, user-friendly minimal service. As of today, Atlas only supports MaxMind databases, but it would be beneficial to have an extensible implementation so more and more formats can be supported. Another aspect I would like to pay attention to is adding more endpoint types such as gRPC so that users can choose the best endpoint based on their requirements and needs. Thank you for taking the time to learn about Atlas. I am excited to see where we can take this project with your support and collaboration! Imagine you're tasked with implementing a geo-fencing feature for your service. At first glance, dropping in an IP GeoLocation API might seem straightforward, but often, project constraints or requirements complicate the simplest solutions. Let's explore our options. Local GeoIP Database Implementation Local GeoIP Database Implementation This method involves embedding a geolocation database directly within your application. It offers fast response times as it resides in-memory and operates independently of external services. However, this approach can be resource-intensive and harder to manage. Let’s see it in more detail. Pros Speed: IP lookups in milliseconds or less. Reliability: No reliance on third-party services reduces failure chances. Scalability: No extra costs as usage grows. Speed : IP lookups in milliseconds or less. Speed Reliability : No reliance on third-party services reduces failure chances. Reliability Scalability : No extra costs as usage grows. Scalability Cons Resource Intensive: Each app instance consumes significant memory. More Dependencies: Requires packages for decoding and looking up GeoIP data. Challenging Updates: Updates require manual intervention or another self-hosted service. Resource Intensive : Each app instance consumes significant memory. Resource Intensive More Dependencies : Requires packages for decoding and looking up GeoIP data. More Dependencies Challenging Updates : Updates require manual intervention or another self-hosted service. Challenging Updates Using External GeoIP APIs (Often Paid) Using External GeoIP APIs (Often Paid) Opting for a third-party API to handle geolocation queries reduces your application's resource load and ensures access to the most current data, but it comes with its own challenges: Pros Minimal Resource Use: Outsourcing GeoIP data and logic doesn't burden your app's resources. Ease of Implementation: Usually involves consuming a standard HTTP/GraphQL or gRPC API endpoint. Fewer Dependencies: Often only requires an HTTP client. Updates without hassle: Third-party will handle updates Minimal Resource Use : Outsourcing GeoIP data and logic doesn't burden your app's resources. Minimal Resource Use Ease of Implementation : Usually involves consuming a standard HTTP/GraphQL or gRPC API endpoint. Ease of Implementation Fewer Dependencies : Often only requires an HTTP client. Fewer Dependencies Updates without hassle : Third-party will handle updates Updates without hassle Cons Slower Responses: Calls to third-party services can introduce significant latency. Increased Unreliability: Greater likelihood of failures due to network issues or third-party downtimes. Scaling Costs: Costs can escalate as usage increases. Slower Responses : Calls to third-party services can introduce significant latency. Slower Responses Increased Unreliability : Greater likelihood of failures due to network issues or third-party downtimes. Increased Unreliability Scaling Costs : Costs can escalate as usage increases. Scaling Costs Introducing Atlas : Combining the Best of Both Worlds Introducing Atlas Faced with these challenges, I developed Atlas —a robust solution designed to synergize the benefits of both approaches. Atlas acts as a self-hosting HTTP service that automatically manages and serves the latest MaxMind geolocation databases, providing: Atlas Low Latency: Similar to local databases, Atlas offers rapid response times. (often in a few milliseconds) Low Latency : Similar to local databases, Atlas offers rapid response times. (often in a few milliseconds) Low Latency Reliability and Ease of Updates: It autonomously handles database updates, removing the typical maintenance burdens. Reliability and Ease of Updates : It autonomously handles database updates, removing the typical maintenance burdens. Reliability and Ease of Updates Cost Efficiency: No additional costs with scale in terms of third-party fees. Cost Efficiency : No additional costs with scale in terms of third-party fees. Cost Efficiency Dead-Simple API: A very simple API that lets you implement quicker and focus on what matters. Dead-Simple API : A very simple API that lets you implement quicker and focus on what matters. Dead-Simple API Open and Free: Atlas is Open Source and freely available under Apache-2.0 License. Open and Free: Atlas is Open Source and freely available under Apache-2.0 License. Open and Free : Atlas is Open Source and freely available under Apache-2.0 License. Open and Free By hosting Atlas closer to your application but operating like a remote API, it offers a middle ground that maximizes efficiency without the drawbacks of either method we have discussed. Why Rust? Safe, Performant, and Resource-Efficient Why Rust? Atlas is written in Rust, a language chosen for its unparalleled safety, performance, and resource efficiency. Rust's ownership model ensures memory safety without the need for a garbage collector, which is particularly advantageous during database updates—these operations consume minimal memory, avoiding the typical overhead associated with garbage-collected languages. This design choice not only boosts Atlas's performance but also enhances its reliability, making it an ideal solution for managing GeoIP data efficiently and safely. How to Deploy and Use Atlas? Atlas Docker images are automatically built and pushed to Docker Hub via GitHub Actions, making it easy for anyone to use. For detailed instructions on using these Docker images or building Atlas from source and configuration parameters, please refer to the Atlas README.md . Atlas README.md Atlas features a straightforward API, documented with OpenAPI 3.0 Specs. You can access the API documentation from the public internet here: https://atlas-rs.fly.dev/swagger-ui/ . https://atlas-rs.fly.dev/swagger-ui/ Here’s an example of how you can use curl to query the demo instance of Atlas (hosted publicly thanks to Fly.io and the GeoLite2 MaxMind database license) to retrieve country information for an IP address 8.8.8.8 : Fly.io 8.8.8.8 > curl https://atlas-rs.fly.dev/geoip/lookup/country/8.8.8.8 | jq
{
  "results": {
    "8.8.8.8": {
      "country": {
        "iso_code": "US",
        // additional fields...
      },
      // additional results...
    }
  },
  "database_build_epoch": 1715091859
} > curl https://atlas-rs.fly.dev/geoip/lookup/country/8.8.8.8 | jq
{
  "results": {
    "8.8.8.8": {
      "country": {
        "iso_code": "US",
        // additional fields...
      },
      // additional results...
    }
  },
  "database_build_epoch": 1715091859
} Please note that this demo instance is intended only for demonstration and documentation purposes. Please note that this demo instance is intended only for demonstration and documentation purposes. Join the Atlas Community and Contribute Join the Atlas Community and Contribute As a newly introduced open-source project, Atlas is in the early stages of building its community. I am excited about the potential for Atlas to evolve and thrive with contributions from developers like you. Whether you're interested in refining functionality, adding new features, or simply improving documentation, your contributions are welcome and greatly valued. How You Can Contribute Try Atlas: The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage. Report Issues: If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement. Suggest Features: Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request. Improve Documentation: As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable. Spread the Word: If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Try Atlas: The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage. Try Atlas : The best way to start is by using Atlas in your projects. Experiment with it, identify any issues, and suggest improvements based on your real-world usage. Try Atlas Report Issues: If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement. Report Issues : If you encounter any bugs or issues while using Atlas, please report them on the GitHub issues page. This feedback is crucial for continual improvement. Report Issues Suggest Features: Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request. Suggest Features : Have ideas for new features or enhancements? Open a discussion on the GitHub repository or contribute directly by submitting a pull request. Suggest Features Improve Documentation: As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable. Improve Documentation : As Atlas evolves, so too does the need for clear and comprehensive documentation. Whether it's enhancing the current content or writing tutorials and guides, your knowledge-sharing is invaluable. Improve Documentation Spread the Word: If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Spread the Word : If you find Atlas useful, consider sharing it within your networks. Blog about your experiences, talk about Atlas at meetups, or share it on social media. Spread the Word Commitment to Open Source I am relatively new to Rust but bring many years of experience from other technology stacks. This project presents a great opportunity for more experienced Rust developers to contribute, ensuring that Atlas adheres to Rust's best practices and conventions since I know that it is not as good as it can be right now. I am committed to maintaining Atlas as a community-driven project and believe in open, collaborative development. I am eager to see how the community will shape Atlas's path forward and encourage you to join me in this journey, helping to build a tool that serves the needs of developers globally. In the end, I would like to give a special thanks to oschwald for creating the rust maxminddb crate which made Atlas possible. special thanks to oschwald oschwald maxminddb Future Directions As we continue to develop and improve Atlas, my vision is to create a robust, user-friendly minimal service. As of today, Atlas only supports MaxMind databases, but it would be beneficial to have an extensible implementation so more and more formats can be supported. Another aspect I would like to pay attention to is adding more endpoint types such as gRPC so that users can choose the best endpoint based on their requirements and needs. Thank you for taking the time to learn about Atlas. I am excited to see where we can take this project with your support and collaboration!

IP Geo-Location: Local Database vs. Remote API—Finding the Middle Ground With Atlas

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk;
(2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com;
(3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk;
(4) Jener Rasmussen, jener@jener.dk;
(5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com;
(6) Asger G. Weirsøe, asger@weircon.dk;
(7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk;
(8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. Table of Links Introduction Definitions Model Checking Tool Overview Evaluation Conclusion and References 4 Tool Overview CGAAL (https://github.com/d702e20/CGAAL) is written in Rust and consists of a command-line interface and a verification engine. The primary feature of CGAAL is the verification of ATL properties for CGSs. Verification is either done with a global algorithm or local algorithm, both of which are described in Section 3. If requested, a partial strategy witness can also be computed, which instructs how the given players must play to satisfy the given property. Another feature converts the model into dot graph format such that it can be visually rendered with Graphviz.[2] To model concurrent game structures for CGAAL, we designed a declarative Language for Concurrent Game Structure, called LCGS. In LCGS a CGS is defined with a series of declarations such that it is possible to find the successors of any state from the declarations alone. The language acts as an abstract representation of the CGS and allows us to save memory at the small cost of having to evaluate the expressions of declarations whenever successors are explored. The syntax of the language is inspired by PRISM-lang [11] used by the PRISM model checker to model stochastic multi-player games with rewards. However, LCGS differs in multiple ways. For instance, LCGS has player templates instead of modules, and templates have no effect unless there exists an instance of it. Additionally, synchronisations affecting the internal state of a player are much easier to declare. Example Use As an example, we want to check if a cowboy can guarantee to stay alive in a three way Mexican standoff. The standoff is simulated in rounds and in each round a cowboy can choose to wait, shoot the cowboy to the right, or shoot the cowboy to the left. If a cowboy is hit by two bullets, he dies. We model this scenario in Listing 1. The cowboy template is declared on lines 3-17. Herein, on line 6, we define that each cowboy can be hit by two bullets before being incapacitated, but we make this number a constant on line 1 so it is easy to change. Each cowboy has their own health variable and the combination of the values of these variables makes up a state. Line 7 defines how the health of a cowboy is updated in each transition. The value of opp_right.shoot_left and opp_left.shoot_right are 1 if this cowboy was shot by the given opponent to the right or left, respectively, otherwise 0. On line 10 we define a label called alive, which is a proposition that is true if the cowboy has more than zero health. Lines 13-15 define the actions of the cowboy template, and the expression to the right of the name is a condition defining in which states the action is available. As can be seen, a cowboy can always wait, but only shoot if they and their target are alive. Lastly, we declare three instances of the cowboy template on lines 20-22. We define the right and left opponent in the relabelling function after the name of the template. Any identifier in a template can be relabelled to another expression as long as the result is syntactically and semantically correct. The ATL formula ⟪billy⟫□billy.alive is satisfied if the cowboy billy has a strategy to stay alive. We can now run CGAAL with the following command: ./cgaal solver -m standoff.lcgs -f billy-can-stay-alive.atl, and CGAAL will tell us that the property is not satisfied. Billy has no strategy that can guarantee his survival. [2] Graphviz: https://graphviz.org/ This paper is available on Arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk; (2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com; (3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk; (4) Jener Rasmussen, jener@jener.dk; (5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com; (6) Asger G. Weirsøe, asger@weircon.dk; (7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk; (8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk; (2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com; (3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk; (4) Jener Rasmussen, jener@jener.dk; (5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com; (6) Asger G. Weirsøe, asger@weircon.dk; (7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk; (8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. Table of Links Introduction Introduction Definitions Definitions Model Checking Model Checking Tool Overview Tool Overview Evaluation Evaluation Conclusion and References Conclusion and References 4 Tool Overview CGAAL (https://github.com/d702e20/CGAAL) is written in Rust and consists of a command-line interface and a verification engine. The primary feature of CGAAL is the verification of ATL properties for CGSs. Verification is either done with a global algorithm or local algorithm, both of which are described in Section 3. If requested, a partial strategy witness can also be computed, which instructs how the given players must play to satisfy the given property. Another feature converts the model into dot graph format such that it can be visually rendered with Graphviz.[2] To model concurrent game structures for CGAAL, we designed a declarative Language for Concurrent Game Structure, called LCGS. In LCGS a CGS is defined with a series of declarations such that it is possible to find the successors of any state from the declarations alone. The language acts as an abstract representation of the CGS and allows us to save memory at the small cost of having to evaluate the expressions of declarations whenever successors are explored. The syntax of the language is inspired by PRISM-lang [11] used by the PRISM model checker to model stochastic multi-player games with rewards. However, LCGS differs in multiple ways. For instance, LCGS has player templates instead of modules, and templates have no effect unless there exists an instance of it. Additionally, synchronisations affecting the internal state of a player are much easier to declare. Example Use As an example, we want to check if a cowboy can guarantee to stay alive in a three way Mexican standoff. The standoff is simulated in rounds and in each round a cowboy can choose to wait, shoot the cowboy to the right, or shoot the cowboy to the left. If a cowboy is hit by two bullets, he dies. Example Use We model this scenario in Listing 1. The cowboy template is declared on lines 3-17. Herein, on line 6, we define that each cowboy can be hit by two bullets before being incapacitated, but we make this number a constant on line 1 so it is easy to change. Each cowboy has their own health variable and the combination of the values of these variables makes up a state. Line 7 defines how the health of a cowboy is updated in each transition. The value of opp_right.shoot_left and opp_left.shoot_right are 1 if this cowboy was shot by the given opponent to the right or left, respectively, otherwise 0. On line 10 we define a label called alive, which is a proposition that is true if the cowboy has more than zero health. Lines 13-15 define the actions of the cowboy template, and the expression to the right of the name is a condition defining in which states the action is available. As can be seen, a cowboy can always wait, but only shoot if they and their target are alive. Lastly, we declare three instances of the cowboy template on lines 20-22. We define the right and left opponent in the relabelling function after the name of the template. Any identifier in a template can be relabelled to another expression as long as the result is syntactically and semantically correct. The ATL formula ⟪billy⟫□billy.alive is satisfied if the cowboy billy has a strategy to stay alive. We can now run CGAAL with the following command: ./cgaal solver -m standoff.lcgs -f billy-can-stay-alive.atl, and CGAAL will tell us that the property is not satisfied. Billy has no strategy that can guarantee his survival. [2] Graphviz: https://graphviz.org/ This paper is available on Arxiv under CC 4.0 license. Arxiv

A Tool Overview of CGAAL: A Distributed On-The-Fly ATL Model Checker

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk;
(2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com;
(3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk;
(4) Jener Rasmussen, jener@jener.dk;
(5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com;
(6) Asger G. Weirsøe, asger@weircon.dk;
(7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk;
(8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. Table of Links Introduction Definitions Model Checking Tool Overview Evaluation Conclusion and References 2 Definitions We recall the definitions of concurrent games and alternating-time temporal logic. A computation starting in the state q is called a q-computation. Alternating-time Temporal Logic The alternating-time temporal logic (ATL) [1] is defined with respect to a finite set Π of propositions and a finite set Σ = {1,...,k} of players. An ATL formula is given by the abstract syntax: where p ∈ Π is a proposition and A ⊆ Σ is a set of players. The operators ⟪⋅⟫ and J⋅K are path quantifiers. We will refer to them as "enforce" and "despite", respectively. The ◯ ("next") and U ("until") are temporal operators. Additional temporal operators like ♢ ("eventually") and □ ("invariant") are derived as usual. Given a state q of a game structure S, we write q ⊧ φ to indicate that the state q satisfies the property described by φ. The satisfactory relation ⊧ is defined inductively: This paper is available on Arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk; (2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com; (3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk; (4) Jener Rasmussen, jener@jener.dk; (5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com; (6) Asger G. Weirsøe, asger@weircon.dk; (7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk; (8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk; (2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com; (3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk; (4) Jener Rasmussen, jener@jener.dk; (5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com; (6) Asger G. Weirsøe, asger@weircon.dk; (7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk; (8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. Table of Links Introduction Introduction Definitions Definitions Model Checking Model Checking Tool Overview Tool Overview Evaluation Evaluation Conclusion and References Conclusion and References 2 Definitions We recall the definitions of concurrent games and alternating-time temporal logic. A computation starting in the state q is called a q -computation. q q Alternating-time Temporal Logic The alternating-time temporal logic (ATL) [1] is defined with respect to a finite set Π of propositions and a finite set Σ = {1,...,k} of players. An ATL formula is given by the abstract syntax: Alternating-time Temporal Logic where p ∈ Π is a proposition and A ⊆ Σ is a set of players. The operators ⟪⋅⟫ and J⋅K are path quantifiers. We will refer to them as "enforce" and "despite", respectively. The ◯ ("next") and U ("until") are temporal operators. Additional temporal operators like ♢ ("eventually") and □ ("invariant") are derived as usual. Given a state q of a game structure S, we write q ⊧ φ to indicate that the state q satisfies the property described by φ. The satisfactory relation ⊧ is defined inductively: q q q This paper is available on Arxiv under CC 4.0 license. Arxiv Arxiv

CGAAL: A Distributed On-The-Fly ATL Model Checker: The Definitions You Should Know About

This paper is available on Arxiv under CC 4.0 license.
Authors:
(1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk;
(2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com;
(3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk;
(4) Jener Rasmussen, jener@jener.dk;
(5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com;
(6) Asger G. Weirsøe, asger@weircon.dk;
(7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk;
(8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. Table of Links Introduction Definitions Model Checking Tool Overview Evaluation Conclusion and References We present CGAAL, our efficient on-the-fly model checker for alternating-time temporal logic (ATL) on concurrent game structures (CGS). We present how our tool encodes ATL as extended dependency graphs with negation edges and employs the distributed on-the-fly algorithm by Dalsgaard et al. Our tool offers multiple novel search strategies for the algorithm, including DHS which is inspired by PageRank and uses the in-degree of configurations as a heuristic, IHS which estimates instability of assignment values, and LPS which estimates the distance to a state satisfying the constituent property using linear programming. CGS are input using our modelling language LCGS, where composition and synchronisation are easily described. We prove the correctness of our encoding, and our experiments show that our tool CGAAL is often one to three orders of magnitude faster than the popular tool PRISM-games on case studies from PRISM’s documentation and among case studies we have developed. In our evaluation, we also compare and evaluate our search strategies, and find that our custom search strategies are often significantly faster than the usual breadth-first and depth-first search strategies. 1 Introduction Software plays a large role in our everyday lives, making decisions, enabling efficient communication, ensuring safety, and many more critical tasks. Furthermore, the complexity of the software and the decisions they have to make are ever-increasing due the to interconnectivity and reactive nature of modern systems. These software systems mutually depend on, communicate with, and guide each other based on their collective and internal states. Even a few interconnected systems that are not necessarily themselves too complex can give rise to an extremely complex system as a whole. Safety-critical software that operates in contexts where errors could lead to human casualties or significant capital losses requires methods to verify their correctness. Unfortunately, such methods are difficult to implement due to the sheer state-space explosion that arises from the inherent parallel composition of systems. In this paper, we consider systems that can be expressed as discrete multiplayer games in which the actors can perform actions concurrently. That is, in each configuration of the game, each player simultaneously chooses an action they wish to perform and the resulting decision vector then deterministically results in the next configuration of the game. Such concurrent games are extremely expressive and can be used to model a variety of complex systems and lends themselves inherently, by their concurrent nature, to describing multi-actor systems. The properties that we wish to verify on these systems are those that can be expressed by Alternating-time Temporal Logic (ATL) [1], a game theoretic extension of Computation Tree Logic (CTL) [2, 3], that like CTL can be used to describe safety and liveness properties. ATL extends CTL by replacing the existential and universal path quantifiers with so-called coalition quantifiers. These coalition quantifiers describe properties of the game in which a coalition of players can either enforce some desired outcome or cannot avoid an outcome. Of course, proof of satisfaction of such property is done by finding a strategy showing that the players can indeed enforce said property or by showing that no matter what strategy the players follow they cannot avoid said property. One existing tool that can model check concurrent (stochastic) games is PRISM-games [12], an extension of the probabilistic model checker PRISM [11]. PRISM excels at stochastic models such as discreteand continuous-time Markov chains, Markov decision processes, and (priced) probabilistic timed automata, for which it can verify various properties described in LTL , CSL, and probabilistic CTL*. There are multiple verification engines in PRISM, both symbolic and explicit state. The PRISM-games extension focuses on stochastic games in which game-theoretic approaches are needed. PRISM-games can among other things synthesise strategies and reason about equilibria-based properties, where players may have distinct, but not necessarily conflicting objectives. These properties are typically described with probabilistic ATL with rewards. In version 3.0 [12], PRISM-games added support for concurrent stochastic games. In [14], Liu and Smolka present a global and a local algorithm to compute fixed-point boolean vertex assignments in dependency graphs with directed hyper-edges representing dependencies between vertex values. The global algorithm has a better worst-case running time of the two, but the on-the-fly local algorithm only explores the graph as needed to compute the fixed-point assignment. Many problems have since been encoded as dependency graphs. Notably, model-checking problems for CTL can be encoded as dependency graphs and the algorithms by Liu and Smolka can be used to compute the satisfaction relation enabling model-checking using dependency graphs. J. F. Jensen et al. [9] show that weighted CTL can be encoded in dependency graphs as well and introduce symbolic edges to handle weights efficiently. In [10], M. C. Jensen et al. introduce symbolic dependency graphs, an encoding of probabilistic weighted CTL, and an implementation of a local and global algorithm. A. Dalsgaard et al. [4] present a distributed extension to the local algorithm to boost the performance and show how to model Petri Nets problems using CTL and dependency graphs. Furthermore, they extend dependency graphs by adding components and negation edges to solve formulae with negations. An abstract dependency graph framework is presented by S. Enevoldsen et al. in [6] where they generalise the various extensions of the dependency graphs with a general algorithm for any Noetherian partial order domain. In the closely related work of [5], S. Enevoldsen et at. uses abstract dependency graphs to model check probabilistic ATL for weighted stochastic games. In multiple works [4, 10] the local algorithm is found to be faster more often than the global algorithm in practice. In this paper, we introduce the tool CGAAL (Concurrent Games AALborg), a model checker implemented in Rust that allows for the verification of ATL properties on concurrent games. CGAAL works by encoding the model checking problem as an extended dependency graph and by implementing the on-the-fly local distributed algorithm by Dalsgaard et al. [4]. The concurrent games are described and inputted to the tool using LCGS, our PRISM-language inspired model language, and for specific ATL formulae, we can output a strategy describing how to ensure satisfaction given a positive result. Furthermore, CGAAL implements a variety of strategies for searching the problem state space: a heuristic inspired by PageRank [15] and two heuristic search strategies that exploit the state vector representation of our model, alongside the usual breadth-first and depth-first approach. We conduct experiments comparing our different search strategies with each other on several case studies and show that having more available compute threads often leads to speed-ups of 1-2 orders of magnitude. We also perform experiments comparing our tool on a selection of case studies to the state-of-the-art PRISM-games. Though while PRISM-games is a tool specialised for probabilistic games we find that our implementation often outperforms PRISM-games and especially whenever we are not required to compute the entire fixed point in which case we are often 1-3 orders of magnitude faster. Again, we emphasise that PRISM games is meant for probabilistic games and as such this is not a completely fair one-to-one comparison. Outline. This paper is structured as follows. Section 2 introduces the formal definitions of concurrent games and alternating-time temporal logic. In Section 3 we present how model checking of ATL properties in concurrent games can be encoded in extended dependency graphs. We also give a short explanation of the CERTAINZERO algorithm and our search strategies. In Section 4 we give a short example of how to use CGAAL and the LCGS language. An evaluation of our tool and a comparison with PRISM can be found in Section 5, and we conclude on our findings in Section 6. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Authors: (1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk; (2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com; (3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk; (4) Jener Rasmussen, jener@jener.dk; (5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com; (6) Asger G. Weirsøe, asger@weircon.dk; (7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk; (8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. This paper is available on Arxiv under CC 4.0 license. Authors: Authors: (1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk; (2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com; (3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk; (4) Jener Rasmussen, jener@jener.dk; (5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com; (6) Asger G. Weirsøe, asger@weircon.dk; (7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk; (8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. Table of Links Introduction Introduction Definitions Definitions Model Checking Model Checking Tool Overview Tool Overview Evaluation Evaluation Conclusion and References Conclusion and References We present CGAAL, our efficient on-the-fly model checker for alternating-time temporal logic (ATL) on concurrent game structures (CGS). We present how our tool encodes ATL as extended dependency graphs with negation edges and employs the distributed on-the-fly algorithm by Dalsgaard et al. Our tool offers multiple novel search strategies for the algorithm, including DHS which is inspired by PageRank and uses the in-degree of configurations as a heuristic, IHS which estimates instability of assignment values, and LPS which estimates the distance to a state satisfying the constituent property using linear programming. CGS are input using our modelling language LCGS, where composition and synchronisation are easily described. We prove the correctness of our encoding, and our experiments show that our tool CGAAL is often one to three orders of magnitude faster than the popular tool PRISM-games on case studies from PRISM’s documentation and among case studies we have developed. In our evaluation, we also compare and evaluate our search strategies, and find that our custom search strategies are often significantly faster than the usual breadth-first and depth-first search strategies. 1 Introduction Software plays a large role in our everyday lives, making decisions, enabling efficient communication, ensuring safety, and many more critical tasks. Furthermore, the complexity of the software and the decisions they have to make are ever-increasing due the to interconnectivity and reactive nature of modern systems. These software systems mutually depend on, communicate with, and guide each other based on their collective and internal states. Even a few interconnected systems that are not necessarily themselves too complex can give rise to an extremely complex system as a whole. Safety-critical software that operates in contexts where errors could lead to human casualties or significant capital losses requires methods to verify their correctness. Unfortunately, such methods are difficult to implement due to the sheer state-space explosion that arises from the inherent parallel composition of systems. In this paper, we consider systems that can be expressed as discrete multiplayer games in which the actors can perform actions concurrently. That is, in each configuration of the game, each player simultaneously chooses an action they wish to perform and the resulting decision vector then deterministically results in the next configuration of the game. Such concurrent games are extremely expressive and can be used to model a variety of complex systems and lends themselves inherently, by their concurrent nature, to describing multi-actor systems. The properties that we wish to verify on these systems are those that can be expressed by Alternating-time Temporal Logic (ATL) [1], a game theoretic extension of Computation Tree Logic (CTL) [2, 3], that like CTL can be used to describe safety and liveness properties. ATL extends CTL by replacing the existential and universal path quantifiers with so-called coalition quantifiers. These coalition quantifiers describe properties of the game in which a coalition of players can either enforce some desired outcome or cannot avoid an outcome. Of course, proof of satisfaction of such property is done by finding a strategy showing that the players can indeed enforce said property or by showing that no matter what strategy the players follow they cannot avoid said property. One existing tool that can model check concurrent (stochastic) games is PRISM-games [12], an extension of the probabilistic model checker PRISM [11]. PRISM excels at stochastic models such as discreteand continuous-time Markov chains, Markov decision processes, and (priced) probabilistic timed automata, for which it can verify various properties described in LTL , CSL, and probabilistic CTL*. There are multiple verification engines in PRISM, both symbolic and explicit state. The PRISM-games extension focuses on stochastic games in which game-theoretic approaches are needed. PRISM-games can among other things synthesise strategies and reason about equilibria-based properties, where players may have distinct, but not necessarily conflicting objectives. These properties are typically described with probabilistic ATL with rewards. In version 3.0 [12], PRISM-games added support for concurrent stochastic games. In [14], Liu and Smolka present a global and a local algorithm to compute fixed-point boolean vertex assignments in dependency graphs with directed hyper-edges representing dependencies between vertex values. The global algorithm has a better worst-case running time of the two, but the on-the-fly local algorithm only explores the graph as needed to compute the fixed-point assignment. Many problems have since been encoded as dependency graphs. Notably, model-checking problems for CTL can be encoded as dependency graphs and the algorithms by Liu and Smolka can be used to compute the satisfaction relation enabling model-checking using dependency graphs. J. F. Jensen et al. [9] show that weighted CTL can be encoded in dependency graphs as well and introduce symbolic edges to handle weights efficiently. In [10], M. C. Jensen et al. introduce symbolic dependency graphs, an encoding of probabilistic weighted CTL, and an implementation of a local and global algorithm. A. Dalsgaard et al. [4] present a distributed extension to the local algorithm to boost the performance and show how to model Petri Nets problems using CTL and dependency graphs. Furthermore, they extend dependency graphs by adding components and negation edges to solve formulae with negations. An abstract dependency graph framework is presented by S. Enevoldsen et al. in [6] where they generalise the various extensions of the dependency graphs with a general algorithm for any Noetherian partial order domain. In the closely related work of [5], S. Enevoldsen et at. uses abstract dependency graphs to model check probabilistic ATL for weighted stochastic games. In multiple works [4, 10] the local algorithm is found to be faster more often than the global algorithm in practice. In this paper, we introduce the tool CGAAL (Concurrent Games AALborg), a model checker implemented in Rust that allows for the verification of ATL properties on concurrent games. CGAAL works by encoding the model checking problem as an extended dependency graph and by implementing the on-the-fly local distributed algorithm by Dalsgaard et al. [4]. The concurrent games are described and inputted to the tool using LCGS, our PRISM-language inspired model language, and for specific ATL formulae, we can output a strategy describing how to ensure satisfaction given a positive result. Furthermore, CGAAL implements a variety of strategies for searching the problem state space: a heuristic inspired by PageRank [15] and two heuristic search strategies that exploit the state vector representation of our model, alongside the usual breadth-first and depth-first approach. We conduct experiments comparing our different search strategies with each other on several case studies and show that having more available compute threads often leads to speed-ups of 1-2 orders of magnitude. We also perform experiments comparing our tool on a selection of case studies to the state-of-the-art PRISM-games. Though while PRISM-games is a tool specialised for probabilistic games we find that our implementation often outperforms PRISM-games and especially whenever we are not required to compute the entire fixed point in which case we are often 1-3 orders of magnitude faster. Again, we emphasise that PRISM games is meant for probabilistic games and as such this is not a completely fair one-to-one comparison. Outline . This paper is structured as follows. Section 2 introduces the formal definitions of concurrent games and alternating-time temporal logic. In Section 3 we present how model checking of ATL properties in concurrent games can be encoded in extended dependency graphs. We also give a short explanation of the CERTAINZERO algorithm and our search strategies. Outline In Section 4 we give a short example of how to use CGAAL and the LCGS language. An evaluation of our tool and a comparison with PRISM can be found in Section 5, and we conclude on our findings in Section 6. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv

Introducing CGAAL: A Distributed On-The-Fly ATL Model Checker With Heuristics

Last week, two remarkable events occurred: Meta announced its Llama 3, the most capable and highly anticipated open large language model (LLM) to date, and Boston Dynamics introduced its new fully electric Atlas robot platform — a stark departure from their hydraulic robots! At first glance, these developments may seem unrelated, but they are actually deeply interconnected, with the potential to synergistically drive each other forward and reshape how we work with and implement AI. At the heart of this connection is the transformative power of advanced AI. Breakthroughs in natural language processing and machine learning, exemplified by Llama 3, extend beyond language alone. These techniques, from deep neural networks to reinforcement learning, are also propelling significant advancements in computer vision, motion planning, and robot control. As language models like Llama 3 continue to expand the boundaries of AI’s ability to understand and interact with the world, they also lay the groundwork for more intelligent and capable robots. The implications, of course, extend beyond communication. The same techniques used to train LLMs on vast amounts of text data are also applicable to learning from massive datasets of sensor readings, images, and simulations for robots. Moreover, the open-source nature of models like Llama 3 democratizes access to state-of-the-art AI, enabling a broader spectrum of researchers and companies to integrate these capabilities into their robotic systems. At least start playing on that field! Every successful (and unsuccessful) attempt by Atlas to manipulate an object, navigate a cluttered factory floor, or assist a human worker becomes a valuable data point for the model, just as it does for Llama 3. Using this innovative model, Meta developed a standalone app, meta.ai, and embedded it into WhatsApp, Instagram, and Facebook, collecting vast and highly personalized data. This could lead us toward synthetic social networks or — which is more likely — an extremely personalized, embodied AI experience. As Llama 3 and the electric Atlas converge, they may accelerate each other’s development, blurring the lines between software and hardware, bits and atoms. As language models become more proficient at understanding and interacting with the world, robots will become more capable of applying that knowledge. This doesn’t necessarily mean evil. More capable and advanced robots, which can be fine-tuned and improved with your personal data and specifics, could take over mundane tasks at home. Imagine, instead of hundreds of text editors, finally having a robot that can load and unload the dishwasher and do the laundry. Currently, launches like Llama 3 are seen as enhancing AI’s understanding and processing capabilities, but in the long term, they will be one of the milestones in building and deploying machines that are finely attuned and aligned with us to assist in our daily lives. https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true Other Impressive Models: (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper
Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper
Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper
Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper
Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper
Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper
YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper
RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper
RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper
JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper News from The Usual Suspects © Google open-sourced Gemini Cookbook
and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “capacity to deliver capable AI”
DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life Hugging Face https://x.com/QGallouedec/status/1782430246957994422?embedable=true Stanford published the 2024 AI Index report, reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. Enjoyed This Story? I write a weekly analysis of the AI world in the Turing Post newsletter. We aim to equip you with comprehensive knowledge and historical insights so you can make informed decisions about AI and ML. Last week, two remarkable events occurred: Meta announced its Llama 3 , the most capable and highly anticipated open large language model (LLM) to date, and Boston Dynamics introduced its new fully electric Atlas robot platform — a stark departure from their hydraulic robots! At first glance, these developments may seem unrelated, but they are actually deeply interconnected, with the potential to synergistically drive each other forward and reshape how we work with and implement AI. Llama 3 Llama 3 electric Atlas robot electric Atlas robot At the heart of this connection is the transformative power of advanced AI. Breakthroughs in natural language processing and machine learning, exemplified by Llama 3, extend beyond language alone. These techniques, from deep neural networks to reinforcement learning, are also propelling significant advancements in computer vision, motion planning, and robot control. As language models like Llama 3 continue to expand the boundaries of AI’s ability to understand and interact with the world, they also lay the groundwork for more intelligent and capable robots. The implications, of course, extend beyond communication. The same techniques used to train LLMs on vast amounts of text data are also applicable to learning from massive datasets of sensor readings, images, and simulations for robots. Moreover, the open-source nature of models like Llama 3 democratizes access to state-of-the-art AI, enabling a broader spectrum of researchers and companies to integrate these capabilities into their robotic systems. At least start playing on that field! Every successful (and unsuccessful) attempt by Atlas to manipulate an object, navigate a cluttered factory floor, or assist a human worker becomes a valuable data point for the model, just as it does for Llama 3. Using this innovative model, Meta developed a standalone app, meta.ai , and embedded it into WhatsApp, Instagram, and Facebook, collecting vast and highly personalized data. This could lead us toward synthetic social networks or — which is more likely — an extremely personalized, embodied AI experience. As Llama 3 and the electric Atlas converge, they may accelerate each other’s development, blurring the lines between software and hardware, bits and atoms. As language models become more proficient at understanding and interacting with the world, robots will become more capable of applying that knowledge. meta.ai meta.ai synthetic social networks synthetic social networks This doesn’t necessarily mean evil. More capable and advanced robots, which can be fine-tuned and improved with your personal data and specifics, could take over mundane tasks at home. Imagine, instead of hundreds of text editors, finally having a robot that can load and unload the dishwasher and do the laundry. Currently, launches like Llama 3 are seen as enhancing AI’s understanding and processing capabilities, but in the long term, they will be one of the milestones in building and deploying machines that are finely attuned and aligned with us to assist in our daily lives. https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true https://youtu.be/29ECwExc-_M?si=UyexFH1jOfd5TGY9&embedable=true Other Impressive Models: (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) (I didn’t send you the FOD digest last Monday because I was at a conference dedicated to citizen diplomacy. Therefore, today, we have an extensive list of recently launched models that are worth checking out, along with other relevant research papers.) Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper Mixtral-8x22B: Mistral AI introduced a scalable sparse mixture of expert models optimizing cost and latency by selectively using parameters during inference, offering a high capacity and efficiency model for further training and applications →read the paper Mixtral-8x22B: →read the paper Rerank 3: Launched by Cohere, this model enhances enterprise search and Retrieval Augmented Generation systems, improving accuracy in document retrieval across multiple languages and data formats →read the paper Rerank 3: →read the paper Idefics2: Hugging Face's model that excels in integrating text and image data, significantly improving on OCR and visual question-answering tasks →read the paper Idefics2: →read the paper Reka Core, Flash, and Edge: A series of multimodal language models from Reka that process text, images, video, and audio, demonstrating high performance across diverse tasks →read the paper Reka Core, Flash, and Edge: →read the paper Ferret-UI: Developed by Apple, this model specializes in mobile UI interaction, enhancing user experience by accurately performing tasks tailored to the unique properties of UI screens →ead the paper Ferret-UI: →ead the paper Zamba: Zyphra's compact and efficient SSM Hybrid model is designed for performance with reduced training data needs, optimized for consumer hardware →read the paper Zamba: →read the paper YaART: Yandex's advanced text-to-image diffusion model that optimizes training efficiency and quality with smaller, high-quality datasets →read the paper YaART: →read the paper RHO-1: A novel approach by Xiamen University focusing on Selective Language Modeling to enhance efficiency by prioritizing useful tokens during training →read the paper RHO-1: →read the paper RecurrentGemma: Google DeepMind's model that moves past traditional transformers by incorporating recurrences for more efficient long-sequence processing →read the paper RecurrentGemma: →read the paper JetMoE: An economical LLM from the MIT-IBM Watson AI Lab using a mixture-of-experts architecture to achieve high performance at reduced costs →read the paper JetMoE: →read the paper News from The Usual Suspects © Google Google open-sourced Gemini Cookbook and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “capacity to deliver capable AI” DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life open-sourced Gemini Cookbook Gemini Cookbook and is merging its Research, DeepMind, and Responsible AI teams to accelerate its “ capacity to deliver capable AI ” merging capacity to deliver capable AI DeepMind published an article about the ethics of advanced AI assistants, stressing the significance of their integration into daily life published Hugging Face Hugging Face https://x.com/QGallouedec/status/1782430246957994422?embedable=true https://x.com/QGallouedec/status/1782430246957994422?embedable=true Stanford Stanford published the 2024 AI Index report, reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. published the 2024 AI Index report , reflecting the escalating impact of AI on society. It contains data with new estimates on AI training costs and detailed insights into the responsible AI landscape. It also introduces a new chapter on AI's influence on science and medicine. Highlights include the substantial cost of training state-of-the-art models, such as GPT-4 and Gemini Ultra; the dominance of the U.S. in producing top AI models; and significant investment growth in generative AI despite overall funding declines. Additionally, the report notes a major increase in AI-related regulations in the U.S. and heightened public awareness and nervousness about AI's future impact. the 2024 AI Index report Enjoyed This Story? I write a weekly analysis of the AI world in the Turing Post newsletter. We aim to equip you with comprehensive knowledge and historical insights so you can make informed decisions about AI and ML. Turing Post

AI Industries Converge: Llama 3 and Electric Atlas Have More In Common Than You Think

In today's world, where everything is interconnected, it's important to understand how our digital infrastructure, including the internet and our own smaller networks, performs. Whether you're a seasoned network expert or just starting out, having the right tools at your disposal can make a big difference. In this article, we’ll take a closer look at two helpful tools: and . Both are network measurement platforms that allow you to evaluate the performance and reliability of networks using a globally distributed probe network. RIPE Atlas Globalping We’ll also compare the two platforms and see in which use cases the newer Globalping platform can be a great RIPE Atlas alternative. By the end of the article, you may even want to use both platforms together to get the best insights! Introducing the Platforms To start, let's give you a brief introduction to the two tools: Introducing Globalping Founded by the jsDelivr team in 2021, Globalping is a platform that relies on an extensive probe network hosted in users' homes and offices worldwide. The platform aims to provide an excellent user experience, making it straightforward for users from all backgrounds to create their measurements effortlessly. The platform is entirely open source, and all tools, including the API, are free to use. You can also support the project and its users by hosting one or more probes. Globalping offers various official integrations and tools to ensure a seamless experience. For example, you can use the CLI tool to bring the platform's capabilities to your terminal. Or you can add the Slack app to your workspace, which enables your team to collaborate and troubleshoot effectively, making Globalping an interesting RIPE Atlas alternative. Introducing RIPE Atlas RIPE Atlas was founded by the RIPE Network Coordination Centre (NCC) in 2010, and it has since grown its network to consist of over 12000 probes to measure internet connectivity worldwide. All data collected by the platform is publicly available, which makes it popular with researchers and organizations interested in monitoring and learning about the state of the internet in real-time. By spending credits, you can create your own measurements and view the results in their dashboard, rendered over maps, or as raw data. You can earn credits in various ways, such as hosting a probe or purchasing credits directly, although that’s limited to bigger enterprises. In addition, there are several tools available, including data parsers, CLI tools, and apps, which have been created by both the RIPE NCC and the community. Comparing the Two Platforms Below, you'll find a list of criteria along with some comments on how each platform handles and performs in those areas. Our goal is to emphasize the strengths of both platforms, as each one has its own features and benefits, catering to a wide range of users and use cases. Probe Network Globalping: Has a quickly and continuously growing probe network. Anyone can host a probe – all you need to do is run the probe's Docker container on an internet-connected machine. RIPE Atlas: Offers an extensive probe network across the globe. If you want to host a probe, you need to apply for one first. If the network would benefit from your probe's location, you'll get a hardware probe sent to your address where you need to set it up. Measurement Types Globalping: All tests report their results back in real-time, creating a feeling of local testing and allowing different kinds of new use cases. Supported test types are: Ping, Traceroute, DNS, HTTP (including SSL), and MTR. RIPE Atlas: The platform is focused more on scheduled measurements resulting in delays that may happen due to RIPE's complexity. Supported test types are:
Ping, Traceroute, DNS, HTTP, SSL, and NTP. Measurements and Costs Globalping: Enables you to create measurements for free, regardless of the tool you use, whether it is the API, CLI tool, or Slack app. However, some rate limits are in place to safeguard the API, which are usually sufficient for most users. But, if you need more, you can increase the limit by becoming a GitHub sponsor or earn free credits by hosting multiple probes at home, in the office, or on hosting providers. RIPE Atlas: Allows you to create measurements, but it costs credits. You can obtain credits by hosting a probe, requesting credits as a researcher, receiving transfers from other users, or buying credits by becoming a corporate sponsor. However, becoming a sponsor may be too pricey for individuals. Internet Health Monitoring Globalping: Doesn't offer public internet health monitoring capabilities at the moment. Instead, it focuses on providing measurement creation capabilities to users. RIPE Atlas: Offers public access to all measurements. You can analyze internet health data by viewing maps and graphs, which provide detailed insights. Custom Monitoring Capabilities Globalping: You can leverage the API to develop your own monitoring solution, which many companies already do. The Globalping team is also working on building a custom and scheduled monitoring platform. This platform will use the probe network to provide real-time and historical data on the performance and quality of any internet endpoint. RIPE Atlas: You can specify intervals and timeframes for running measurements, and then analyze the results using maps and graphs that the platform generates and displays on its website. Use Cases Globalping: Designed to be used by users with various backgrounds and networking skills. It allows you to create network measurements effortlessly and for free, making it a great RIPE Atlas alternative when you want to learn more about networking or investigate your own networks in-depth. Overall, Globalping is suitable for both newcomers and network experts. RIPE Atlas: Tailored for researchers, academics, and anyone who needs access to aggregated internet connectivity data. The platform also allows you to investigate the impact of global or local incidents on internet health, such as natural catastrophes. Commercial Use Globalping: Doesn't differentiate between personal and commercial use, and you don't need an account or authentication for basic tests. RIPE Atlas: If you plan to use the RIPE Atlas platform to provide paid services or products, there are some rules you need to follow. These rules include, for example, displaying the platform's logo and description. The platform's credit-based usage system also applies here. Data Usage Globalping: Measurement results are stored for a limited time; only users with the measurement link can view them. Also, probe owners can see the tests their probe ran. RIPE Atlas: All measurements conducted on the platform are publicly accessible. Tools and Integrations Globalping: Provides user-friendly tools and integrations for custom development, Slack, and GitHub. The Globalping team officially maintains all tools, ensuring ongoing development, support, and a consistent and modern UX. RIPE Atlas: You can find many open-source tools, including CLI tools and visualization libraries, on the website and on GitHub. Some of these tools were created during hackathons and may not be regularly maintained, so keep this in mind when searching for tools. Getting Involved Globalping: Invites you to host one or more probes, become a GitHub sponsor, or contribute to the platform on GitHub. The team welcomes any feedback and feature requests, which are essential for the platform's continuous improvement and growth. RIPE Atlas: Encourages you to host a probe or an anchor, sponsor the project by buying credits, become an ambassador, or contribute to the open-source tools. Is Globalping a RIPE Atlas Alternative? Our comparison between Globalping and RIPE Atlas showed that both platforms have unique benefits: With its extensive public internet health data, RIPE Atlas is great for researchers and network experts who require detailed analysis. On the other hand, Globalping's modern approach, user-friendly UX, and free measurements make it compelling for a wide range of users. This makes it a great alternative to RIPE Atlas for individuals as well as companies ranging from startups to enterprises. However, each platform has its features and strengths, so we think it's worth using them together. By leveraging Globalping to run real-time measurements in a comfortable way and RIPE Atlas to obtain aggregated data, you can get a more in-depth view of the state of the internet and your own networks. If you're new to Globalping or looking for a straightforward way to get started with networking, we invite you to ! visit our website and create your first measurements

RIPE Atlas and Globalping: Choosing the Right Network Measurement Platform

RIPE Atlas এবং Globalping: সঠিক নেটওয়ার্ক পরিমাপ প্ল্যাটফর্ম নির্বাচন করা

RIPE Atlas und Globalping: Auswahl der richtigen Netzwerkmessplattform

RIPE Atlas y Globalping: elección de la plataforma de medición de red adecuada

RIPE Atlas et Globalping : choisir la bonne plate-forme de mesure de réseau

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RIPE Atlas と Globalping: 適切なネットワーク測定プラットフォームの選択

RIPE Atlas 및 Globalping: 올바른 네트워크 측정 플랫폼 선택

RIPE Atlas e Globalping: Escolhendo a plataforma certa de medição de rede

RIPE Atlas и Globalping: выбор правильной платформы для сетевых измерений

RIPE Atlas ve Globalping: Doğru Ağ Ölçüm Platformunu Seçmek

RIPE Atlas và Toàn cầu hóa: Chọn nền tảng đo lường mạng phù hợp

RIPE Atlas 和 Globalping：选择正确的网络测量平台

We don't usually think of Git as a debugging tool. Surprisingly, Git shines not just as a version control system but also as a potent ally when dealing with the tricky matter of regressions. debugging https://www.youtube.com/watch?v=yZuPHEBbjY&embedable=true As a side note, if you like the content of this and the other posts in this series, check out my that covers his subject. If you have friends who are learning to code, I'd appreciate a reference to my If you want to get back to Java after a while, check out my Debugging book t Java Basics book. Java 8 to 21 book . The Essence of Debugging with Git Before we tap into the advanced aspects of , it's essential to understand its foundational premise. Git is known for tracking changes and managing code history, but the tool is a hidden gem for regression detection. Regressions are distinct from generic bugs. They signify a backward step in functionality—where something that once worked flawlessly now fails. Pinpointing the exact change causing a regression can be akin to finding a needle in a haystack, particularly in extensive codebases with long commit histories. git bisect git bisect Traditionally, developers would employ a manual, binary search strategy—checking out different versions, testing them, and narrowing down the search scope. This method, while effective, is painstakingly slow and error-prone. automates this search, transforming what used to be a marathon into a swift sprint. Git bisect Setting the Stage for Debugging Imagine you're working on a project, and recent reports indicate a newly introduced bug affecting the functionality of a feature that previously worked flawlessly. You suspect a regression but are unsure which commit introduced the issue among the hundreds made since the last stable version. Initiating Bisect Mode To start, you'll enter bisect mode in your terminal within the project's Git repository: git bisect start This command signals Git to prepare for the bisect process. Marking the Known Good Revision Next, you identify a commit where the feature functioned correctly, often a commit tagged with a release number or dated before the issue was reported. Mark this commit as "good": git bisect good a1b2c3d Here, represents the hash of the known good commit. a1b2c3d Marking the Known Bad Revision Similarly, you mark the current version or a specific commit where the bug is present as "bad": git bisect bad z9y8x7w is the hash of the bad commit, typically the latest commit in the repository where the issue is observed. z9y8x7w Bisecting to Find the Culprit Upon marking the good and bad commits, Git automatically jumps to a commit roughly in the middle of the two and waits for you to test this revision. After testing (manually or with a script), you inform Git of the result: If the issue is present: git bisect bad If the issue is not present: git bisect good Git then continues to narrow down the range, selecting a new commit to test based on your feedback. Expected Output After several iterations, Git will isolate the problematic commit, displaying a message similar to: Bisecting: 0 revisions left to test after this (roughly 3 steps)
[abcdef1234567890] Commit message of the problematic commit Reset and Analysis Once the offending commit is identified, you conclude the bisect session to return your repository to its initial state: git bisect reset Notice that bisect isn't linear. Bisect doesn't scan through the revisions in a sequential manner. Based on the good and bad markers, Git automatically selects a commit approximately in the middle of the range for testing (e.g., commit #6 in the following diagram). This is where the non-linear, binary search pattern starts, as Git divides the search space in half instead of examining each commit sequentially. This means fewer revisions get scanned, and the process is faster. Advanced Usage and Tips The magic of lies in its ability to automate the binary search algorithm within your repository, systematically halving the search space until the rogue commit is identified. git bisect offers a powerful avenue for debugging, especially for identifying regressions in a complex codebase. To elevate your use of this tool, consider delving into more advanced techniques and strategies. These tips not only enhance your debugging efficiency but also provide practical solutions to common challenges encountered during the bisecting process. Git bisect Script Automation for Precision and Efficiency Automating the bisect process with a script is a game-changer, significantly reducing manual effort and minimizing the risk of human error. This script should ideally perform a quick test that directly targets the regression, returning an exit code based on the test's outcome. : Imagine you're debugging a regression where a web application's login feature breaks. You could write a script that attempts to log in using a test account and checks if the login succeeds. The script might look something like this in a simplified form: Example #!/bin/bash
# Attempt to log in and check for success
if curl -s http://yourapplication/login -d "username=test&password=test" | grep -q "Welcome"; then
  exit 0 # Login succeeded, mark this commit as good
else
  exit 1 # Login failed, mark this commit as bad
fi By passing this script to , Git automatically executes it at each step of the bisect process, effectively automating the regression hunt. git bisect run Handling Flaky Tests with Strategy Flaky tests, which sometimes pass and sometimes fail under the same conditions, can complicate the bisecting process. To mitigate this, your automation script can include logic to rerun tests a certain number of times or to apply more sophisticated checks to differentiate between a true regression and a flaky failure. : Suppose you have a test that's known to be flaky. You could adjust your script to run the test multiple times, considering the commit "bad" only if the test fails consistently: Example #!/bin/bash
# Run the flaky test three times
success_count=0
for i in {1..3}; do
  if ./run_flaky_test.sh; then
    ((success_count++))
  fi
done

# If the test succeeds twice or more, consider it a pass
if [ "$success_count" -ge 2 ]; then
  exit 0
else
  exit 1
fi This approach reduces the chances that a flaky test will lead to incorrect bisect results. Skipping Commits with Care Sometimes, you'll encounter commits that cannot be tested due to reasons like broken builds or incomplete features. is invaluable here, allowing you to bypass these commits. However, use this command judiciously to ensure it doesn't obscure the true source of the regression. git bisect skip : If you know that commits related to database migrations temporarily break the application, you can skip testing those commits. During the bisect session, when Git lands on a commit you wish to skip, you would manually issue: Example git bisect skip This tells Git to exclude the current commit from the search and adjust its calculations accordingly. It's essential to only skip commits when absolutely necessary, as skipping too many can interfere with the accuracy of the bisect process. These advanced strategies enhance the utility of in your debugging toolkit. By automating the regression testing process, handling flaky tests intelligently, and knowing when to skip untestable commits, you can make the most out of for efficient and accurate debugging. Remember, the goal is not just to find the commit where the regression was introduced but to do so in the most time-efficient manner possible. With these tips and examples, you're well-equipped to tackle even the most elusive regressions in your projects. git bisect git bisect Unraveling a Regression Mystery In the past, we got to use , when working on a large-scale web application. After a routine update, users began reporting a critical feature failure: the application's payment gateway stopped processing transactions correctly, leading to a significant business impact. git bisect We knew the feature worked in the last release but had no idea which of the hundreds of recent commits introduced the bug. Manually testing each commit was out of the question due to time constraints and the complexity of the setup required for each test. Enter . The team started by identifying a "good" commit where the payment gateway functioned correctly and a "bad" commit where the issue was observed. We then crafted a simple test script that would simulate a transaction and check if it succeeded. git bisect By running , followed by marking the known good and bad commits and executing the script with , we set off on an automated process that identified the faulty commit. Git efficiently navigated through the commits, automatically running the test script on each step. In a matter of minutes, pinpointed the culprit: a seemingly innocuous change to the transaction logging mechanism that inadvertently broke the payment processing logic. git bisect start git bisect run git bisect Armed with this knowledge, we reverted the problematic change, restoring the payment gateway's functionality and averting further business disruption. This experience not only resolved the immediate issue but also transformed our approach to debugging, making a go-to tool in our arsenal. git bisect Final Word The story of the payment gateway regression is just one example of how can be a lifesaver in the complex world of software development. By automating the tedious process of regression hunting, not only saves precious time but also brings a high degree of precision to the debugging process. git bisect git bisect As developers continue to navigate the challenges of maintaining and improving complex codebases, tools like underscore the importance of leveraging technology to work smarter, not harder. Whether you're dealing with a mysterious regression or simply want to refine your debugging strategies, offers a powerful yet underappreciated solution to swiftly and accurately identify the source of regressions. Remember, the next time you're faced with a regression, might just be the debugging partner you need to uncover the truth hidden within your commit history. git bisect git bisect git bisect Also published . here

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