We make use of four internal 12 GB NVIDIA TIT AN Xp GPUs to perform our experiments. At initialization of each environment, four UEs are randomly stationed 1.5 meters above the The L TE base station lies at ( x, z) = (40 m, 3m) . We use random seed values from 0 to 63, inclusive, for this parameter. Do not distribute. of four We train PTD3 for 10,000 steps, instead of 1,000,000 steps, which we do for TD3+BC.

Large language model-based agents show promise for software engineering, but environment configuration remains a bottleneck due to heavy manual effort and scarce large-scale, high-quality datasets. Existing benchmarks assess only end-to-end build/test success, obscuring where and why agents succeed or fail. We introduce the Environment Configuration Diagnosis Benchmark, Enconda-bench, which provides process-level trajectory assessment of fine-grained agent capabilities during environment setup-planning, perception-driven error diagnosis, feedback-driven repair, and action to execute final environment configuration. Our task instances are automatically constructed by injecting realistic README errors and are validated in Docker for scalable, high-quality evaluation. Enconda-bench combines process-level analysis with end-to-end executability to enable capability assessments beyond aggregate success rates. Evaluations across state-of-the-art LLMs and agent frameworks show that while agents can localize errors, they struggle to translate feedback into effective corrections, limiting end-to-end performance. To our knowledge, Enconda-bench is the first framework to provide process-level internal capability assessment for environment configuration, offering actionable insights for improving software engineering agents.

We make use of four internal 12 GB NVIDIA TIT AN Xp GPUs to perform our experiments. At initialization of each environment, four UEs are randomly stationed 1.5 meters above the The L TE base station lies at ( x, z) = (40 m, 3m) . We use random seed values from 0 to 63, inclusive, for this parameter. Do not distribute. of four We train PTD3 for 10,000 steps, instead of 1,000,000 steps, which we do for TD3+BC.

Recent advances in Large Language Models (LLMs) have enabled researchers to focus on practical repository-level tasks in software engineering domain. In this work, we consider a cornerstone task for automating work with software repositories--environment setup, i.e., a task of configuring a repository-specific development environment on a system. Existing studies on environment setup introduce innovative agentic strategies, but their evaluation is often based on small datasets that may not capture the full range of configuration challenges encountered in practice. To enable further benchmark extension and usage for model tuning, we implement two automatic metrics: a static analysis check for missing imports in Python and a compilation check for JVM languages. We demonstrate the applicability of our benchmark by evaluating three environment setup approaches, including a simple zero-shot baseline and two agentic workflows, that we test with two powerful LLM backbones, GPT-4o and GPT-4o-mini. The best approach manages to successfully configure 6.69% repositories for Python and 29.47% repositories for JVM, suggesting that E The dataset and experiment trajectories are available at https://jb.gg/envbench. Recent advances in Large Language Models (LLMs) have enabled their application across many domains, including software engineering (Fan et al., 2023). In this work, we focus on another repository-level task that programmers face regularly-- environment setup, i.e., configuring the system to work with an arbitrary software project, for instance, a freshly cloned GitHub repository. It usually entails installing the dependencies but might include arbitrary project-specific steps, such as installing additional system packages, setting the correct environment variables, and more. A well-maintained project should be straightforward to set up, however, in practice, it is not always the case. For instance, setting up the repository is perceived to be the most challenging part of reproducing Natural Language Processing (NLP) research results, according to Storks et al. (2023), it may take up to several hours.

Generative AI technologies promise to transform the product development lifecycle. This study evaluates the efficiency gains, areas for improvement, and emerging challenges of using GitHub Copilot, an AI-powered coding assistant. We identified 15 software development tasks and assessed Copilot's benefits through real-world projects on large proprietary code bases. Our findings indicate significant reductions in developer toil, with up to 50% time saved in code documentation and autocompletion, and 30-40% in repetitive coding tasks, unit test generation, debugging, and pair programming. However, Copilot struggles with complex tasks, large functions, multiple files, and proprietary contexts, particularly with C/C++ code. We project a 33-36% time reduction for coding-related tasks in a cloud-first software development lifecycle. This study aims to quantify productivity improvements, identify underperforming scenarios, examine practical benefits and challenges, investigate performance variations across programming languages, and discuss emerging issues related to code quality, security, and developer experience.

Training a Machine Learning (ML) model is only one step in the ML lifecycle. There's no purpose to ML if you cannot get a response from your model. You must be able to host your trained model for inference. There's a variety of hosting/deployment options that can be used for ML, with one of the most popular being TensorFlow Serving. TensorFlow Serving helps take your trained model's artifacts and host it for inference.

Either to identify the best model or to understand the nuances of the model with different changes to the data or the hyperparameters -- you would want to perform numerous machine learning experiments. The results could be interesting that enable the process of model selection. As part of my job, I usually have to perform several ML experiments which can be -- (say) to test the effectiveness of dimensionality reduction techniques, text preprocessing techniques (in case of an NLP model), or simple things like playing with the size of the test set. Either way, you might have to run a single code multiple times and record all the observations for comparison later. This is slightly different from the hyperparameter tuning process, and our aim is to identify the technique that best suits our problem.

For a given audio dataset, can we do audio classification using Spectrogram? We'll be converting our audio files into their respective spectrograms and use spectrogram as images for our classification problem. A Spectrogram is a visual representation of the spectrum of frequencies of a signal as it varies with time. For this experiment, I'm going to use the following audio dataset from Kaggle For my experiment, I have rented a Linux virtual machine on Google Could Platform (GCP) and I'll be performing all the steps from there. Now that we have our audio data in place, let's create spectrograms for each audio file.

One and two are pre-trained deep learning models, meaning that they are provided to you as-is by OpenCV. The Movidius NCS will perform inference using each of these models. The third recognizer model is not a form of deep learning. Rather, it is our SVM machine learning face recognition model. The RPi CPU will have to handle making face recognition predictions using it. We also load our label encoder which holds the names of the people our model can recognize (Line 42). Let's initialize our video stream: Line 47 initializes and starts our VideoStream object. We wait for the camera sensor to warm up on Line 48. Line 51 initializes our FPS counter for benchmarking purposes.

Javed Qadrud-Din was an Insight Fellow in Fall 2017. He is currently a machine learning engineer at Casetext where he works on natural language processing for the legal industry. Prior to Insight, he was at IBM Watson. BERT can be pre-trained on a massive corpus of unlabeled data, and then fine-tuned to a task for which you have a limited amount of data. This allows BERT to provide significantly higher performance than models that are only able to leverage a small task-specific dataset.