相关论文: Knights and Gold Stars: A Tale of InnerSource Ince…

The top-down approach of engineering software integration is considered in this parer. A set of advantages of this approach are presented, by examples. All examples are supplied by open source code.

It is hard to establish whether a company supports internal sustainability efforts (ISEs) like gender equality, diversity, and general staff welfare, not least because of lack of methodologies operationalizing these internal sustainability…

The reinforcement learning (RL) research area is very active, with an important number of new contributions; especially considering the emergent field of deep RL (DRL). However a number of scientific and technical challenges still need to…

Collaborative learning techniques have the potential to enable training machine learning models that are superior to models trained on a single entity's data. However, in many cases, potential participants in such collaborative schemes are…

How do incentive levels affect strategic behaviour? We address this with an experiment that separately identifies own- and opponent-incentive effects in two dominance-solvable games that differ in strategic complexity. Higher own incentives…

Many software developers rely on open source software for developing their applications and writing their source codes. Measuring an independent project's overall productivity is still an open problem for many technology companies. In this…

For job scheduling systems, where jobs require some amount of processing and then leave the system, it is natural for each user to provide an estimate of their job's time requirement in order to aid the scheduler. However, if there is no…

Intrinsic reward shaping has emerged as a prevalent approach to solving hard-exploration and sparse-rewards environments in reinforcement learning (RL). While single intrinsic rewards, such as curiosity-driven or novelty-based methods, have…

As software has become an integral part of scientific workflows, reproducible research practices must take it into account. In what way? Archiving source code is a necessary but insufficient condition. The ability to redeploy software…

In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals. To address the issues…

A growing number of researchers suggest that software process must be tailored to a project's context to achieve maximal performance. Researchers have studied 'context' in an ad-hoc way, with focus on those contextual factors that appear to…

[Background] An increasing number of commercial firms are participating in Open Source Software (OSS) projects to reduce their development cost and increase technical innovativeness. When collaborating with other firms whose sought values…

This paper is an introductory discussion on the cause of open source software vulnerabilities, their importance in the cybersecurity ecosystem, and a selection of detection methods. A recent application security report showed 44% of…

This paper introduces a novel method of adding intrinsic bonuses to task-oriented reward function in order to efficiently facilitate reinforcement learning search. While various bonuses have been designed to date, they are analogous to the…

Software development companies organize hackathons to encourage innovation. Despite many benefits of hackathons, in large-scale agile organizations where many teams work together, stopping the ongoing work results in a significant decrease…

Reinforcement learning has emerged as a promising paradigm for aligning diffusion and flow-matching models with human preferences, yet practitioners face fragmented codebases, model-specific implementations, and engineering complexity. We…

Context. Source code refactoring is a well-established approach to improving source code quality without compromising its external behavior. Motivation. The literature described the benefits of refactoring, yet its application in practice…

Although there are many approaches to implement intrinsically motivated artificial agents, the combined usage of multiple intrinsic drives remains still a relatively unexplored research area. Specifically, we hypothesize that a mechanism…

Recent work in hierarchical reinforcement learning has shown success in scaling to billions of timesteps when learning over a set of predefined option reward functions. We show that, instead of using a single reward function per option, the…

Reinforcement learning (RL) systems typically optimize scalar reward functions that assume precise and reliable evaluation of outcomes. However, real-world objectives--especially those derived from human preferences--are often uncertain,…