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Post-deployment monitoring (PDM) occurs in the late stages of a DevSecOps (DSO) pipeline. Its role in DSO is critical in providing feedback loops on system performance leading to desirable changes achieving long-term system and application sustainment. ...

In microservice platforms with high number of users and heavy traffic, it is necessary to monitor the system, take quick action against errors and ensure the maintainability of the system. However, debugging on these platforms can take a long time. This ...

CG crowds have become increasingly popular this last decade in the VFX and animation industry: formerly reserved to only a few high end studios and blockbusters, they are now widely used in TV shows or commercials. Yet, there is still one major ...

As games challenge traditional automated white-box test generators, the Neatest approach generates test suites consisting of neural networks that exercise the source code by playing the games. Neatest generates these neural networks using an evolutionary ...

Welcome to the The 4th International Workshop on Software Engineering and AI for Data Quality (SEA4DQ 2024), held in Brazil, on 16 July, 2024, co-located with the 32nd ACM International Conference on the Foundations of Software Engineering (FSE 2024). ...

In its most recognizable form, coevolution is a natural process. It is ubiquitous in natural systems whether they are biological or social. But, a community of Evolutionary Computation researchers have also used computation and algorithms to artificially ...

ChatGPT burst into people's lives at the end of 2022, heralding the arrival of large language models in particular, and generative AI in general. How best to see this moment in the development of AI. What is generative AI actually good for? And what are ...

Recent research shows that deep neural networks make wrong predictions when faced with adversarial examples with small perturbations added. In the setting of white-box attack, it is easy to generate adversarial samples with high attack success rate ...

Neural Architecture Search (NAS) is a promising approach to automate the design of neural network architectures, which can find architectures that perform better than manually designed ones. Hardware-aware NAS is a real-world application of NAS where the ...

Lexicase and ϵ-lexicase selection are state of the art parent selection techniques for problems featuring multiple selection criteria. Originally, lexicase selection was developed for cases where these selection criteria are unlikely to be in conflict ...

Multiagent learning allows agents to learn cooperative behaviors necessary to accomplish team objectives. However, coordination requires agents to learn diverse behaviors that work well as part of a team, a task made more difficult by all agents ...

Dynamic Vehicle Routing Problem (DVRP), is an extension of the classic Vehicle Routing Problem (VRP), which is a fundamental problem in logistics and transportation. Typically, DVRPs involve two stakeholders: service providers that deliver services to ...

To coordinate in multiagent settings, asymmetric agents (agents with distinct objectives and capabilities) must learn diverse behaviors that allow them to maximize their individual and team objectives. Hierarchical learning techniques partially address ...

Hyperspectral imaging brings important opportunities in a variety of fields due to the unprecedented amount of information it captures in numerous narrow and contiguous spectral bands. However, the high spectral and spatial dimensionality of ...

Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility location problems. While meta-heuristics have shown promising effectiveness ...

A major contributor to the quality of a deep learning model is the selection of the optimizer. We propose a new dual-joint search space in the realm of neural optimizer search (NOS), along with an integrity check, to automate the process of finding deep ...

Previous work in Neural Loss Function Search (NLFS) has shown a lack of correlation between smaller surrogate functions and large convolutional neural networks with massive regularization. We expand upon this research by revealing another disparity that ...

In the realm of robot design, co-design aims to optimize both the structure and the controller of a robot concurrently. One approach integrates genetic algorithms to optimize the soft robot's structure with deep reinforcement learning for the controller. ...

In the realm of machine learning, traditional model development and automated approaches like AutoML typically rely on layers of abstraction, such as tree-based or Cartesian genetic programming. Our study introduces "Guided Evolution" (GE), a novel ...

Coevolving cooperative teams creates a challenging joint-action discovery problem because fitness functions generally evaluate team performance rather than individual agent performance. Feedback "sparsity" where agents only receive feedback when they ...