Autonomous vehicles have been rapidly developed as demand that provides safety and efficiency in transportation systems. As autonomous vehicles are designed based on open-source operating and computing systems, there are numerous resources aimed at building an operating platform composed of Ubuntu, Autoware, and Robot Operating System (ROS). However, no explicit guidelines exist to help scholars perform trouble-shooting due to incompatibility between the Autoware platform and Ubuntu operating systems installed in autonomous driving systems-equipped vehicles (i.e., Chrysler Pacifica). The paper presents an overview of integrating the Autoware platform into the autonomous vehicle's interface based on lessons learned from trouble-shooting processes for resolving incompatible issues. The trouble-shooting processes are presented based on resolving the incompatibility and integration issues of Ubuntu 20.04, Autoware.AI, and ROS Noetic software installed in an autonomous driving systems-equipped vehicle. Specifically, the paper focused on common incompatibility issues and code-solving protocols involving Python compatibility, Compute Unified Device Architecture (CUDA) installation, Autoware installation, and simulation in Autoware.AI. The objective of the paper is to provide an explicit and detail-oriented presentation to showcase how to address incompatibility issues among an autonomous vehicle's operating interference. The lessons and experience presented in the paper will be useful for researchers who encountered similar issues and could follow up by performing trouble-shooting activities and implementing ADS-related projects in the Ubuntu, Autoware, and ROS operating systems.

User Stories record what must be built in projects that use agile practices. User Stories serve both to estimate effort, generally measured in Story Points, and to plan what should be done in a Sprint. Therefore, it is essential to train software engineers on how to create simple, easily readable, and comprehensive User Stories. For that reason, we designed, implemented, applied, and evaluated a web application called User Story Tutor (UST). UST checks the description of a given User Story for readability, and if needed, recommends appropriate practices for improvement. UST also estimates a User Story effort in Story Points using Machine Learning techniques. As such UST may support the continuing education of agile development teams when writing and reviewing User Stories. UST's ease of use was evaluated by 40 agile practitioners according to the Technology Acceptance Model (TAM) and AttrakDiff. The TAM evaluation averages were good in almost all considered variables. Application of the AttrakDiff evaluation framework produced similar good results. Apparently, UST can be used with good reliability. Applying UST to assist in the construction of User Stories is a viable technique that, at the very least, can be used by agile developments to complement and enhance current User Story creation.

We present a novel multi-agent simulator named Multi-Agent eXperimenter (MAX) that is designed to simulate blockchain experiments involving large numbers of agents of different types acting in one or several environments. The architecture of MAX is highly modular, enabling easy addition of new models.

Thanks for reading the web version, you can subscribe to the Ops In Dev newsletter here. Happy new year to everyone who celebrates it! I'll cover the best learning pieces in this newsletter and invest in learning hot topics like AI/ML. I started my year early on January 2nd, and boom, a CI/CD pipeline failed with a fancy stack trace. Got me thinking - what if AI could assist with solving pipeline errors for better efficiency?

This was the year that saw GitHub Copilot move from a plug-in on Jetbrain, where it was first launched in 2021, to broad availability for the Visual Studio IDE in March. It was followed by the release of Amazon's code completion service, Code Whisperer, in June, and Replit's Ghostwriter in October. Tabnine, an AI startup for code generation, secured $15.5 million in funding, while another code-completion startup, Kite, died in the wake of Copilot's popularity. And then, too, by the end of the year, it all ended up as a big question mark when GitHub wound up in litigation over its use of open source repositories in Copilot. Although much of the focus in 2022 was on automated coding and code completion, it turns out that AI technologies transformed code in more subtle ways in the past year. "We don't believe we're going to see AI replace DevOps engineers or platform engineers, but really augment them," said Zach Zaro, co-founder and CEO of Coherence, a DevOps automation startup that leverages AI. "You have a lot happening at the application layer level -- AI coming to help developers write application code, not infrastructure code."

DevSecOps tools such as GitLab's One DevOps Platform plan to inject AI into developer workflows to shore up secure coding, a shift IT pros and analysts say is timely as security AI becomes more popular. In IT and security operations, AIOps tools can reduce the number of alerts IT pros must respond to or narrow down the root cause of incidents as distributed cloud-native infrastructure grows more and more complex. The same kind of overload that's led IT ops teams to embrace artificial intelligence and machine learning has creeped into the developer side of the DevSecOps model as well, according to IT analysts. "Cloud services and modern software development processes, such as microservices application architectures, create a much greater scale of software releases and attack exposures," said Melinda Marks, an analyst at Enterprise Strategy Group, a division of TechTarget. "That, coupled with the cybersecurity skills gap, means that they are looking for ways to reduce tedious, manual tasks to work more efficiently and reduce staff burnout." The movement to shift security left into DevOps workflows is bringing along applications for AI assistance as well, from vendors such as Palo Alto Networks' Prisma Cloud and GitLab.

It won't be in the Olympics anytime soon but Oki Mek considers artificial intelligence "a team sport." As the chief artificial intelligence officer for the Department of Health and Human Services, Mek may be a little biased, but as his agency works through its AI strategy -- released in January -- collaboration and knowledge exchange will be paramount. The strategy aims to promote AI adoption, and to ensure that algorithms are fair, legal and ethical. Three core pieces of the strategy are adoption and bringing the entire department up to speed on the language of AI; scaling best practices, and accelerated adoption. As for the first piece, Mek said culture change plays a pivotal role. "The main risks here is not AI itself, it's not the technology itself, it's more of a culture shift.

DevOps platform GitLab today announced that it has acquired UnReview, a machine learning-based tool that helps software teams recommend the best reviewers for when developers want to check in their latest code. GitLab, which is looking to bring more of these machine learning capabilities to its platform, will integrate UnReview's capabilities into its own code review workflow. The two companies did not disclose the price of the acquisition. "Last year we decided that the future of DevOps includes ML/AI, both within the DevOps lifecycle as well as the growth of adoption of ML/AI with our customers," David DeSanto, GitLab's senior director, Product Management – Dev & Sec, told me. He noted that when GitLab recently surveyed its customers, 75% of the teams said they are already using AI/ML.

GitLab announced this week it has acquired UnReview, a provider of a tool that employs machine learning algorithms to both identify which expert code reviewers to assign to a project based on the quality of their previous efforts and current workloads. David DeSanto, senior director for product management at GitLab, said the acquisition of UnReview is the latest step in an AI strategy that, in addition to optimizing DevOps processes, will also eventually unify machine learning operations (MLOps) and DevOps workflows. Accessed via the Dev section of the GitLab platform, UnReview will also be employed to manage the overall code review process. DeSanto said GitLab is committed to employing AI technologies to automate workflows and compressing cycle times across all stages of the DevSecOps life cycle. The goal is to not eliminate the need for DevOps teams but rather eliminate low-level tasks that conspire to hamper productivity, while at the same time improving application security, noted DeSanto.

DevOps and agile programming continue to make inroads into software-development teams, with the two development methodologies accounting for more than two-thirds (68%) of the practices at companies polled in a recent survey, according to a report published by development-tools maker GitLab on Tuesday. The adoption coincides with developers taking an increasing role in securing software -- so-called "shifting left" -- with 39% of developers "feeling fully response for security," up from 28% last year, while 32% share responsibility for security with other teams, according to survey results. Overall, the security outlook among developers has increased significantly over the past year, with 72% calling their organization's security either "good" or "strong," up from 59% the prior year. This year, more than any other year, integrating security into DevOps -- often called DevSecOps, SecDevOps, or secure DevOps -- is a reality, says Johnathan Hunt, vice president of security at GitLab. "Last year, often no one knew who owned security, and the adoption of DevSecOps was stagnant -- you could see that," he says.