Large Language Models (LLMs) have revolutionized software engineering (SE), showcasing remarkable proficiency in various coding tasks. Despite recent advancements that have enabled the creation of autonomous software agents utilizing LLMs for end-to-end development tasks, these systems are typically designed for specific SE functions. We introduce HyperAgent, an innovative generalist multi-agent system designed to tackle a wide range of SE tasks across different programming languages by mimicking the workflows of human developers. HyperAgent features four specialized agents-Planner, Navigator, Code Editor, and Executor-capable of handling the entire lifecycle of SE tasks, from initial planning to final verification. HyperAgent sets new benchmarks in diverse SE tasks, including GitHub issue resolution on the renowned SWE-Bench benchmark, outperforming robust baselines. Furthermore, HyperAgent demonstrates exceptional performance in repository-level code generation (RepoExec) and fault localization and program repair (Defects4J), often surpassing state-of-the-art baselines.

Machine learning can be applied to time series datasets. These are problems where a numeric or categorical value must be predicted, but the rows of data are ordered by time. A problem when getting started in time series forecasting with machine learning is finding good quality standard datasets on which to practice. In this post, […]

The discovering ML.NET series continues. With the release of v0.3.0, it is time to look at performing K-means clustering using F# and Microsoft's new ML.NET framework. The use case will be to use examination attributes to classify mammogram results. For reference, previous ML.NET series posts are below: As I mentioned in the previous posts, there is a disclaimer: ML.NET is in its early stages. I found a couple interface idiosyncrasies I suspect will change over time.

Expanding on my previous post, F# and ML.NET Regression, the current post will take a look at performing classification using Microsoft's new ML.NET framework. The task at hand will be to use biomechanical attributes to classify patient vertebra conditions into normal (NO), disk hernia (DH), or spondilolysthesis (SL) categories. As I mentioned in the previous post, there is a disclaimer: ML.NET is in its early stages. I found a couple implementation and interface idiosyncrasies I suspect will change over time. Just keep that in mind moving forward.

In digital libraries, a table, as a specific document component as well as a condensed way to present structured and relational data, contains rich information and often the only source of .that information. In order to explore, retrieve, and reuse that data, tables should be identified and the data extracted. Table recognition is an old field of research. However, due to the diversity of table styles, the results are still far from satisfactory, and not a single algorithm performs well on all different types of tables. In this paper, we randomly take samples from the CiteSeerX to investigate diverse table styles for automatic table extraction. We find that table headers are one of the main characteristics of complex table styles. We identify a set of features that can be used to segregate headers from tabular data and build a classifier to detect table headers. Our empirical evaluation on PDF documents shows that using a Random Forest classifier achieves an accuracy of 92%.