Performance optimization is a critical yet challenging aspect of software development, often requiring a deep understanding of system behavior and algorithmic tradeoffs, and careful code modifications. Although recent advances in AI coding agents have accelerated code generation and bug fixing, little is known about how these agents perform on real-world performance optimization tasks.

The development and training of deep learning models have become increasingly costly and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for their downstream applications. The dynamics of the PTM supply chain remain largely unexplored, signaling a clear need for structured datasets that document not only the metadata but also the subsequent applications of these models. Without such data, the MSR community cannot comprehensively understand the impact of PTM adoption and reuse.

Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as “model hubs” support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult — there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing the data.We present an open-source dataset, PTMTorrent, to facilitate the evaluation and understanding of PTM packages. This paper describes the creation, structure, usage, and limitations of the dataset. The dataset includes a snapshot of 5 model hubs and a total of 15,913 PTM packages. These packages are represented in a uniform data schema for cross-hub mining. We describe prior uses of this data and suggest research opportunities for mining using our dataset.The PTMTorrent dataset (v1) is available at: https://app.globus.org/file-manager?origin_id=55e17a6e-9d8f-11ed-a2a2-8383522b48d9&origin_path=%2F%7E%2F.Our dataset generation tools are available on GitHub: https://doi.org/10.5281/zenodo.7570357

Data science has become prevalent in a large variety of domains. Inherent in its practice is an exploratory, probing, and fact finding journey, which consists of the assembly, adaptation, and execution of complex data science pipelines. The trustworthiness of the results of such pipelines rests entirely on their ability to be reproduced with fidelity, which is difficult if pipelines are not documented or recorded minutely and consistently. This difficulty has led to a reproducibility crisis and presents a major obstacle to the safe adoption of the pipeline results in production environments. The crisis can be resolved if the provenance for each data science pipeline is captured transparently as pipelines are executed. However, due to the complexity of modern data science pipelines, transparently capturing sufficient provenance to allow for reproducibility is challenging. As a result, most existing systems require users to augment their code or use specific tools to capture provenance, which hinders productivity and results in a lack of adoption. In this paper, we present Ursprung, 1 a transparent provenance collection system designed for data science environments. 2 The Ursprung philosophy is to capture provenance and build lineage by integrating with the execution environment to automatically track static and runtime configuration parameters of data science pipelines. Rather than requiring data scientists to make changes to their code, Ursprung records basic provenance information from system-level sources and combines it with provenance from application-level sources (e.g., log files, stdout), which can be accessed and recorded through a domain-specific language. In our evaluation, we show that Ursprung is able to capture sufficient provenance for a variety of use cases and only adds an overhead of up to 4%.

Modern analytics has produced wonders, but reproducing and verifying these wonders is difficult. Data provenance helps to solve this problem by collecting information on how data is created and accessed. Although provenance collection techniques have been used successfully on a smaller scale, tracking provenance in large-scale analytics environments is challenging due to the scale of provenance generated and the heterogeneous domains. Without provenance, analysts struggle to keep track of and reproduce their analyses. We demonstrate Ursprung, a provenance collection system specifically targeted at such environments. Ursprung transparently collects the minimal set of system-level provenance required to track the relationships between data and processes. To collect domain specific provenance, Usprung enables users to specify capture rules to curate application-specific logs, intermediate results etc. To reduce storage overhead and accelerate queries, it uses event hierarchies to synthesize raw provenance into compact summaries.