Code review is a key practice in software engineering, where developers evaluate code changes to ensure quality and maintainability. Links to issues and external resources are often included in Pull Requests (PRs) to provide additional context, yet they are typically discarded in automated tasks such as PR summarization and code review comment generation. This limits the richness of information available to reviewers and increases cognitive load by forcing context-switching. To address this gap, we present AILinkPreviewer, a tool that leverages Large Language Models (LLMs) to generate previews of links in PRs using PR metadata, including titles, descriptions, comments, and link body content. We analyzed 50 engineered GitHub repositories and compared three approaches: Contextual LLM summaries, Non-Contextual LLM summaries, and Metadata-based previews. The results in metrics such as BLEU, BERTScore, and compression ratio show that contextual summaries consistently outperform other methods. However, in a user study with seven participants, most preferred non-contextual summaries, suggesting a trade-off between metric performance and perceived usability. These findings demonstrate the potential of LLM-powered link previews to enhance code review efficiency and to provide richer context for developers and automation in software engineering. The video demo is available at https://www.youtube.com/ watch?v $=h 2 q \mathrm{H} 4 \mathrm{R} t r B 3 \mathrm{E}$, and the tool and its source code can be found at https://github.com/c4rtune/AILinkPreviewer.

Social media platforms have become more influential than traditional news sources, shaping public discourse and accelerating the spread of information. With the rapid advancement of artificial intelligence (AI), open-source software (OSS) projects can leverage these platforms to gain visibility and attract contributors. In this study, we investigate the relationship between Hacker News, a social news site focused on computer science and entrepreneurship, and the extent to which it influences developer activity on the promoted GitHub AI projects. We analyzed 2,195 Hacker News (HN) stories and their corresponding comments over a two-year period. Our findings reveal that at least 19 % of AI developers promoted their GitHub projects on Hacker News, often receiving positive engagement from the community. By tracking activity on the associated 1,814 GitHub repositories after they were shared on Hacker News, we observed a significant increase in forks, stars, and contributors. These results suggest that Hacker News serves as a viable platform for AI-powered OSS projects, with the potential to gain attention, foster community engagement, and accelerate software development.

In this digital era, our privacy is under constant threat as our personal data and traceable online/offline activities are frequently collected, processed and transferred by many software applications. Privacy attacks are often formed by exploiting vulnerabilities found in those software applications. The Common Weakness Enumeration (CWE) and Common Vulnerabilities and Exposures (CVE) systems are currently the main sources that software engineers rely on for understanding and preventing publicly disclosed software vulnerabilities. However, our study on all 922 weaknesses in the CWE and 156,537 vulnerabilities registered in the CVE to date has found a very small coverage of privacy-related vulnerabilities in both systems, only 4.45% in CWE and 0.1% in CVE. These also cover only a small number of areas of privacy threats that have been raised in existing privacy software engineering research, privacy regulations and frameworks, and relevant reputable organisations. The actionable insights generated from our study led to the introduction of 11 new common privacy weaknesses to supplement the CWE system, making it become a source for both security and privacy vulnerabilities.

Abstract The recent advent of data protection laws and regulations has emerged to protect privacy and personal information of individuals. As the cases of privacy breaches and vulnerabilities are rapidly increasing, people are aware and more concerned about their privacy. These bring a significant attention to software development teams to address privacy concerns in developing software applications. As today’s software development adopts an agile, issue-driven approach, issues in an issue tracking system become a centralised pool that gathers new requirements, requests for modification and all the tasks of the software project. Hence, establishing an alignment between those issues and privacy requirements is an important step in developing privacy-aware software systems. This alignment also facilitates privacy compliance checking which may be required as an underlying part of regulations for organisations. However, manually establishing those alignments is labour intensive and time consuming. In this paper, we explore a wide range of machine learning and natural language processing techniques which can automatically classify privacy requirements in issue reports. We employ six popular techniques namely Bag-of-Words (BoW), N-gram Inverse Document Frequency (N-gram IDF), Term Frequency-Inverse Document Frequency (TF-IDF), Word2Vec, Convolutional Neural Network (CNN) and Bidirectional Encoder Representations from Transformers (BERT) to perform the classification on privacy-related issue reports in Google Chrome and Moodle projects. The evaluation showed that BoW, N-gram IDF, TF-IDF and Word2Vec techniques are suitable for classifying privacy requirements in those issue reports. In addition, N-gram IDF is the best performer in both projects.