This study evaluates the performance and usability of Mixed Reality (MR), Virtual Reality (VR), and camera stream interfaces for remote error resolution tasks, such as correcting warehouse packaging errors. Specifically, we consider a scenario where a robotic arm halts after detecting an error, requiring a remote operator to intervene and resolve it via pick-and-place actions. Twenty-one participants performed simulated pick-and-place tasks using each interface. A linear mixed model (LMM) analysis of task resolution time, usability scores (SUS), and mental workload scores (NASA-TLX) showed that the MR interface outperformed both VR and camera interfaces. MR enabled significantly faster task completion, was rated higher in usability, and was perceived to be less cognitively demanding. Notably, the MR interface, which projected a virtual robot onto a physical table, provided superior spatial understanding and physical reference cues. Post-study surveys further confirmed participants' preference for MR over other interfaces.

Bugs are inevitable in software development, and their reporting in open repositories can enhance software transparency and reliability assessment. This study aims to extract information from the issue tracking system Jira and proposes a methodology to estimate resolution time for new bugs. The methodology is applied to network project ONAP, addressing concerns of network operators and manufacturers. This research provides insights into bug resolution times and related aspects in network softwarization projects.

Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We introduce InsightBench, a benchmark dataset with three key features. First, it consists of 31 datasets representing diverse business use cases such as finance and incident management, each accompanied by a carefully curated set of insights planted in the datasets. Second, unlike existing benchmarks focusing on answering single queries, InsightBench evaluates agents based on their ability to perform end-to-end data analytics, including formulating questions, interpreting answers, and generating a summary of insights and actionable steps. Third, we conducted comprehensive quality assurance to ensure that each dataset in the benchmark had clear goals and included relevant and meaningful questions and analysis. Furthermore, we implement a two-way evaluation mechanism using LLaMA-3-Eval as an effective, open-source evaluator method to assess agents' ability to extract insights. We also propose AgentPoirot, our baseline data analysis agent capable of performing end-to-end data analytics. Our evaluation on InsightBench shows that AgentPoirot outperforms existing approaches (such as Pandas Agent) that focus on resolving single queries. We also compare the performance of open- and closed-source LLMs and various evaluation strategies. Overall, this benchmark serves as a testbed to motivate further development in comprehensive data analytics and can be accessed here: https://github.com/ServiceNow/insight-bench.

Despite significant improvements in robot capabilities, they are likely to fail in human-robot collaborative tasks due to high unpredictability in human environments and varying human expectations. In this work, we explore the role of explanation of failures by a robot in a human-robot collaborative task. We present a user study incorporating common failures in collaborative tasks with human assistance to resolve the failure. In the study, a robot and a human work together to fill a shelf with objects. Upon encountering a failure, the robot explains the failure and the resolution to overcome the failure, either through handovers or humans completing the task. The study is conducted using different levels of robotic explanation based on the failure action, failure cause, and action history, and different strategies in providing the explanation over the course of repeated interaction. Our results show that the success in resolving the failures is not only a function of the level of explanation but also the type of failures. Furthermore, while novice users rate the robot higher overall in terms of their satisfaction with the explanation, their satisfaction is not only a function of the robot's explanation level at a certain round but also the prior information they received from the robot.

Pull Requests (PRs) that are neither progressed nor resolved clutter the list of PRs, making it difficult for the maintainers to manage and prioritize unresolved PRs. To automatically track, follow up, and close such inactive PRs, Stale bot was introduced by GitHub. Despite its increasing adoption, there are ongoing debates on whether using Stale bot alleviates or exacerbates the problem of inactive PRs. To better understand if and how Stale bot helps projects in their pull-based development workflow, we perform an empirical study of 20 large and popular open-source projects. We find that Stale bot can help deal with a backlog of unresolved PRs as the projects closed more PRs within the first few months of adoption. Moreover, Stale bot can help improve the efficiency of the PR review process as the projects reviewed PRs that ended up merged and resolved PRs that ended up closed faster after the adoption. However, Stale bot can also negatively affect the contributors as the projects experienced a considerable decrease in their number of active contributors after the adoption. Therefore, relying solely on Stale bot to deal with inactive PRs may lead to decreased community engagement and an increased probability of contributor abandonment.

Background: Machine Learning (ML) systems rely on data to make predictions, the systems have many added components compared to traditional software systems such as the data processing pipeline, serving pipeline, and model training. Existing research on software maintenance has studied the issue-reporting needs and resolution process for different types of issues, such as performance and security issues. However, ML systems have specific classes of faults, and reporting ML issues requires domain-specific information. Because of the different characteristics between ML and traditional Software Engineering systems, we do not know to what extent the reporting needs are different, and to what extent these differences impact the issue resolution process. Objective: Our objective is to investigate whether there is a discrepancy in the distribution of resolution time between ML and non-ML issues and whether certain categories of ML issues require a longer time to resolve based on real issue reports in open-source applied ML projects. We further investigate the size of fix of ML issues and non-ML issues. Method: We extract issues reports, pull requests and code files in recent active applied ML projects from Github, and use an automatic approach to filter ML and non-ML issues. We manually label the issues using a known taxonomy of deep learning bugs. We measure the resolution time and size of fix of ML and non-ML issues on a controlled sample and compare the distributions for each category of issue.

AI has become a game-changer tool in the IT sector. Artificial intelligence and automation have significantly transformed how organizations run their production lines. As AI tools can garner real-time insights, it has facilitated the companies' design and product innovation techniques. When applied correctly, AI and automation can help develop better, faster, and cheaper business techniques. Automation tools can be deployed to automate repetitive tasks, allowing the IT staff to focus on strategic tasks instead of administrative work.

Fonixa uses artificial intelligence, deep learning and voice-emotion technology to transcribe and analyse up to 5,000 hours of customer calls and scans them for emotional patterns to identify happy, sad and angry customers, get insights and improve their customer's experience. This solution can help you eliminate the time and money needed to screen calls manually, and fully automate the process with usable data insights Ubility uses predictive analytics, artificial intelligence and machine learning to help customer support agents quickly find the knowledge base articles and methods they need to support a customer reducing resolution times from minute/hours to seconds, what's more, is it even suggest how you should be talking to each customer in a way that relates to them Dynamics 365 Field Service uses the power of predictive analytics and machine learning to Automate and improve scheduling to dispatch the right technician and get the most value out of your resources Dynamics 365 Field Service uses predictive analytics and machine learning to help empower technicians with a 360-degree view of your customers and real-time guidance to improve resolution time and earn customer trust. Fonixa uses artificial intelligence, deep learning and voice-emotion technology to transcribe and analyse up to 5,000 hours of customer calls and scans them for emotional patterns to identify happy, sad and angry customers, get insights and improve their customer's experience. Ubility uses predictive analytics, artificial intelligence and machine learning to help customer support agents quickly find the knowledge base articles and methods they need to support a customer reducing resolution times from minute/hours to seconds, what's more, is it even suggest how you should be talking to each customer in a way that relates to them Dynamics 365 Field Service uses predictive analytics and machine learning to help empower technicians with a 360-degree view of your customers and real-time guidance to improve resolution time and earn customer trust.

Throughout the course of a day it's pretty common to speak to a number of colleagues and clients, yet we now have the ability to converse with an unlikely but familiar source: Salesforce's Einstein. Many Salesforce users will already have some experience with this artificial intelligence component, as it's been a central feature in Salesforce's cloud services, but Einstein now has become even more sophisticated. Unveiled at Dreamforce last week as one of the latest innovations, AI and voice-based functionality are now integrated into Einstein to give users a new way to access valuable information with voice control. Marc Benioff's opening keynote included a demo of Einstein's new voice capability, which was given an even greater focus during the Salesforce Einstein keynote led by Marco Casalaina, VP of Product Management. He shared a stat from Fortune that 7 out of 10 businesses using AI say they've achieved little to nothing from their efforts and investments.

In the last 10 years, we've seen some significant breakthroughs in the domain of artificial intelligence (AI) and machine learning. In 2011, IBM Watson showed the world that it can be a reality TV show winner. In 2014, Google acquired an AI company called DeepMind, and one of its project, AlphaGo, beat the European Go champion in 2015. In 2016, Google made its TensorFlow library open source, which made machine learning accessible to the masses. Last year, people were left dumbfounded when Google Duplex made a haircut appointment over the phone.