Code Agent development is an extremely active research area, where a reliable performance metric is critical for tracking progress and guiding new developments. This demand is underscored by the meteoric rise in popularity of SWE-Bench. This benchmark challenges code agents to generate patches addressing GitHub issues given the full repository as context. The correctness of generated patches is then evaluated by executing a human-written test suite extracted from the repository after the issue's resolution. However, constructing benchmarks like SWE-Bench requires substantial manual effort to set up historically accurate execution environments for testing. Crucially, this severely limits the number of considered repositories, e.g., just 12 for SWE-Bench. Considering so few repositories, selected for their popularity runs the risk of leading to a distributional mismatch, i.e., the measured performance may not be representative of real-world scenarios potentially misguiding development efforts. In this work, we address this challenge and introduce SetUpAgent, a fully automated system capable of historically accurate dependency setup, test execution, and result parsing. Using SetUpAgent, we generate two new datasets: (i) SWEE-Bench an extended version of SWE-Bench encompassing hundreds of repositories, and (ii) SWA-Bench a benchmark focusing on applications rather than libraries. Comparing these datasets to SWE-Bench with respect to their characteristics and code agent performance, we find significant distributional differences, including lower issue description quality and detail level, higher fix complexity, and most importantly up to 40% lower agent success rates.

This paper aims to investigate the mechanisms that contribute to propagation of competence in an Agile Scrum team. This study seeks to challenge the traditional view of bounded rationality (BR). An Agile Scrum team (Team) is expected to build problem solving competence quickly as the expected ramp up time continues to shrink. But the team has a mixture of expertise, competence and sociability levels that affect out-of-the-box performance. The objective is to expand BR into the social realm and see how teams can self-organize and reconfigure to allow effective problem solving. Studies have shown that agent-based computational simulation is an appropriate technique to explore this point from a theoretical perspective. The first step is to define the problem, discuss how senior team members exhibit high curiosity and apply sociability and cognitive resources to develop overall team competence. This dynamic is modeled and simulated in NetLogoR and the results are analyzed. Finally, some key findings are presented and discussed.

We are looking for an RPA Developer to join our team. The right person will be wanting to work within an intelligent, motivated, self-driven team with positive attitude, good communication skills and an effective problem-solving approach. You would also be very conscious of the importance of sharing knowledge and building other team members in the process. Objective: Design, develop, test automation workflows. Job Responsibilities: • Implement & develop RPA (UiPath) solutions in accordance with standard UiPath design principles and conventions using the best practices.

Do you fancy working for an innovative new game changing startup at the forefront of scientific advancement and social change in the field of Artificial Intelligence and MedTech? Astronomical AI is working on life saving technology that will have a massive impact on the world. We are recruiting a Senior Developer for an equity position, read on this is the role for you! Astronomical AI is an exciting and innovative new Artificial Intelligence startup specialising in MedTech. We are a pre-revenue start-up with an ambitious plan to grow and scale up the company.

Currently the top three programs in automated and simplified machine learning are DataRobot, RapidMiner, and BigMl. In this article, we will take a closer look at RapidMiner, and tell you what it does and how it can make life easier for you. It is critically important for any business to evaluate the "work force" indicator within certain time periods. This allows you to plan business projects, which are always largely based on human resources. One risk factor may be seasonal outbursts of colds, when every year in winter a significant number of employees are on sick leave.

I've been putting out more JavaScript courses, playing with new technologies, and I got really deep into React and Redux. Been having a lot of fun with those. Redux introduces a more functional approach to JavaScript state management, which is pretty amazing. I've enjoyed building it into my projects, and it has really simplified things like unit testing for your app's state management. I really appreciate the simplicity of Reducers in Redux. If you're not familiar with Redux, the reducers are regular reducer functions for the purposes of app state management. Using reducers for state management has been really amazing and has totally transformed the way I think about building apps, so I've been really happy with it. I've also played with Angular 2 just a little and I've been playing with TypeScript. I have mixed feelings about both of those so far. Angular 2, compared to React and Redux, feels like it has a lot more overhead and not a lot of benefit. Like writing unit tests for the views in Angular 2 is much more complicated than writing unit tests for things like Pure Components in React. Maybe as I use it more I'll start to "feel" it a little more. In TypeScript, I really like the IDE(Integrated development environment) type hinting that it gives you, and I like the Type Inference capability so you don't have to manually annotate everything, which is fantastic ... an amazing feature. I like that more than the type annotations available with Tern.js, but the problem is that since it needs to see how the types flow through the program, sometimes it will infer types that are a little too strict and you have to go in and manually loosen up the type annotations. Sometimes, it's really hard to do that especially if you use any kind of complicated functional programming techniques, which I tend to do once in a while.