This study investigates the software vulnerability resolution time in the Maven ecosystem, focusing on the influence of CVE severity, library popularity as measured by the number of dependents, and version release frequency. The results suggest that critical vulnerabilities are addressed slightly faster compared to lower-severity ones. Library popularity shows a positive impact on resolution times, while frequent version updates are associated with faster vulnerability fixes. These statistically significant findings are based on a thorough evaluation of over 14 million versions from 658,078 libraries using the dependency graph database of Goblin framework. These results emphasize the need for proactive maintenance strategies to improve vulnerability management in open-source ecosystems.

Vulnerabilities in software libraries and reusable components cause major security challenges, particularly in dependency-heavy ecosystems such as Maven. This paper presents a large-scale analysis of vulnerabilities in the Maven ecosystem using the Goblin framework. Our analysis focuses on the aspects and implications of vulnerability types, documentation delays, and resolution timelines. We identify 77,393 vulnerable releases with 226 unique CWEs. On average, vulnerabilities take nearly half a decade to be documented and 4.4 years to be resolved, with some remaining unresolved for even over a decade. The delays in documenting and fixing vulnerabilities incur security risks for the library users emphasizing the need for more careful and efficient vulnerability management in the Maven ecosystem.

The emergence of AI tools such as ChatGPT is being used to assist with software development, but little is known of how developers utilize these tools as well as the capabilities of these tools in software engineering tasks. Using the DevGPT dataset, we conduct quantitative analyses of the tasks developers seek assistance from ChatGPT and how effectively ChatGPT addresses them. We also examine the impact of initial prompt quality on conversation length. The findings reveal where ChatGPT is most and least suited to assist in the identified 12 software development tasks. The insights from this research would guide the software developers, researchers, and AI tool providers in optimizing these tools for more effective programming aid.

We present the first four-dimension gold standard dataset to advance opinion mining focused on the software engineering domain. Through a well-defined sampling and annotation strategy leveraging multiple coders, we construct a corpus of 2,000 Stack Overflow posts labeled with four dimensions/tuples, including sentiments, polar facts, aspects, and named entities. This multidimensional ground truth dataset opens up new research opportunities for opinion mining in domain-adapted NLP tools for software engineering by capturing existing relationships between extracted elements at a more granular level. It also facilitates investigating the effects of sentiments in the developers' social forums.

In this study, we quantitatively analyze 1,756 AI-written Python code snippets in the DevGPT dataset and evaluate them for quality and security issues. We systematically distinguish the code snippets as either generated by ChatGPT from scratch (ChatGPT-generated) or modified user-provided code (ChatGPT-modified). The results reveal that ChatGPT-modified code more frequently displays quality issues compared to ChatGPT-generated code. The findings provide insights into the inherent limitations of AI-written code and emphasize the need for scrutiny before integrating such pieces of code into software systems.

This paper presents a large quantitative study of the contributions of females compared to males in open-source projects. Female participation is found substantially low and females are found more engaged in non-coding work compared to men. The findings are statistically significant and are derived from an in-depth analysis of over 10 thousand developers’ contributions to more than 81 million different projects in the World of Code (WoC) infrastructure. The insights from this study are useful in addressing gender disparity in the field.