Software logs serve as valuable resources for understanding system running and are extensively used in diverse software maintenance tasks. As software systems get more complex and log data grows, a good log dataset is fundamental for developing automated log analysis tools. However, current log datasets are limited in three aspects, i.e., narrow in scope, lacking context information, and outdated. To bridge this gap, in this paper, we aim to extract software logs from online resources (e.g., JIRA issue reports, GitHub repositories, and Stack Overflow discussions), which concern various types of software systems and provide context for logs, such as observed behaviors and expected behaviors. This work introduces WoL, a dataset comprising real-world logs along with their contextual information. WoL currently contains over 2.5 million log messages or logging statements from diverse online resources and is publicly available to facilitate reproducible research. WoL can be used for various log-related tasks, including understanding logging intent and quality, anomaly detection, and linking logs with software artifacts for contextual analysis. WoL is publicly available on Zenodo and will be continuously updated. Furthermore, based on WoL, we develop a search engine, LogSearch, to support user queries.

Modern programming languages are constantly evolving, introducing new language features and APIs to enhance software development practices. Software developers often face the tedious task of upgrading their codebase to new programming language versions. Recently, large language models (LLMs) have demonstrated potential in automating various code generation and editing tasks, suggesting their applicability in automating code upgrade. However, there exists no benchmark for evaluating the code upgrade ability of LLMs, as distilling code changes related to programming language evolution from real-world software repositories’ commit histories is a complex challenge. In this work, we introduce CoUpJava, the first large-scale dataset for code upgrade, focusing on the code changes related to the evolution of Java. CoUpJava comprises 10,697 code upgrade samples, distilled from the commit histories of 1,379 open-source Java repositories and covering Java versions 7–23. The dataset is divided into two subsets: CoUpJava-FINE, which captures fine-grained method-level refactorings towards new language features; and CoUpJava-COARSE, which includes coarse-grained repository-level changes encompassing new language features, standard library APIs, and build configurations. Our proposed dataset provides high-quality samples by filtering irrelevant and noisy changes and verifying the compilability of upgraded code. Moreover, CoUpJava reveals diversity in code upgrade scenarios, ranging from small, fine-grained refactorings to large-scale repository modifications.