Manually backporting critical patches to long-term support versions is both error-prone and often overlooked, resulting in substantial security risks. Progress in this area is constrained by the absence of datasets that capture the semantic complexities across versions, inherent to backporting in large Java ecosystems. To address this gap, we present JavaBackports, a curated dataset of 491 real-world backport instances, systematically selected and manually validated from more than 11,000 candidate patches in fifteen widely used open-source Java projects: Druid, Elasticsearch, Hadoop, Kafka, among others and four major JDK versions (jdk11, jdk17, jdk21, jdk25). To assess the utility of JavaBackports, we conduct preliminary experiments to evaluate the effectiveness of the state-of-the-art Large Language Models (LLMs) in zero-shot automatic patch backporting. The results indicate that current LLMs struggle with backporting tasks, particularly when the required changes involve non-trivial logical or structural modifications. These findings demonstrate both the difficulty of the problem and the potential of JavaBackports to stimulate new research directions in automated software maintenance and repair.

Automated Program Repair (APR) improves developer productivity by saving debugging and bug-fixing time. While APR has been extensively explored for C/C++ and Java programs, there is little research on bugs in PHP programs due to the lack of a benchmark PHP bug dataset. This is surprising given that PHP has been one of the most widely used server-side languages for over two decades, being used in a variety of contexts such as e-commerce, social networking, and content management. This paper presents a benchmark dataset of PHP bugs on real-world applications called BugsPHP, which can enable research on analysis, testing, and repair for PHP programs. The dataset consists of training and test datasets, separately curated from GitHub and processed locally. The training dataset includes more than 600,000 bug-fixing commits. The test dataset contains 513 manually validated bug-fixing commits equipped with developer-provided test cases to assess patch correctness.

Institute, Warsaw, Poland This paper describes the results of the IEEE BigData 2023 Keystroke Verification Challenge1(KVC), that considers the biometric verification performance of Keystroke Dynamics (KD), captured as tweet-long sequences of variable transcript text from over 185,000 subjects. The data are obtained from two of the largest public databases of KD up to date, the Aalto Desktop and Mobile Keystroke Databases, guaranteeing a minimum amount of data per subject, age and gender annotations, absence of corrupted data, and avoiding excessively unbalanced subject distributions with respect to the considered demographic attributes. Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3.33% and 3.61% achieved by the best team respectively in the desktop and mobile scenario, outperforming the current state of the art biometric verification performance for KD. Hosted on CodaLab2, the KVC will be made ongoing to represent a useful tool for the research community to compare different approaches under the same experimental conditions and to deepen the knowledge of the field.