This study examines failure propagation within the Maven Central ecosystem, a critical software dependency repository, through a comprehensive analysis of dependency networks using the Goblin framework. Our dual-sampling methodology, investigating both top dependencies and random libraries, revealed two distinct failure propagation patterns that pose significant risks to ecosystem stability. Core infrastructure failures, particularly evident in cases like the AWS SDK family, can create immediate and widespread disruption. These libraries have many direct dependents - averaging 20,402 projects that would break immediately if the library fails. Furthermore, these core libraries themselves have extensive dependencies, with the AWS SDK family depending on 377 other libraries itself. The impact of failures spreads deeply through the ecosystem, with dependency chains reaching an average of 90.80 levels. Our analysis of peripheral projects reveals their significant cascading effects, with higher average dependency depths of 54.25 levels and chain lengths extending to 116.74 levels, as exemplified by cases like org.apache.camel:camel-swagger-java, which demonstrated a maximum chain length of 647 levels. Our findings highlight specific vulnerabilities in dependency network structures, showing that ecosystem resilience requires both protecting core infrastructure and managing dependency complexity.

Large Language Models (LLMs) have been widely adopted and are becoming ubiquitous and integral to software development. However, we have little knowledge as to how these tools are being used by software developers beyond anecdotal evidence and word-of-mouth reports. In this work, we present a study toward understanding how developers engage with and utilize LLMs by reporting the results of an empirical study identifying patterns in the conversation that developers have with LLMs. We identified a total of 19 topics describing the purpose of the developers in their conversations with LLMs. Our findings reveal that developers use LLMs to facilitate various aspects of their software development processes (e.g., information-seeking about programming languages and frameworks and soliciting high-level design recommendations) to a similar extent to which they use them for non-development purposes such as writing assistance, general purpose queries, and conducting Turing tests to assess the intrinsic capabilities of the models. This work not only sheds light on the diverse applications of LLMs in software development but also underscores their emerging role as critical tools in enhancing developer productivity and creativity as we move closer to widespread AI-assisted software development.

Team communication is essential for the development of modern software systems. For distributed software development teams, such as those found in many open source projects, this communication usually takes place using electronic tools. Among these, modern chat platforms such as Gitter are becoming the de facto choice for many software projects due to their advanced features geared towards software development and effective team communication. Gitter channels contain numerous messages exchanged by developers regarding the state of the project, issues and features of the system, team logistics, etc. These messages can contain important information to researchers studying open source software systems, developers new to a particular project and trying to get familiar with the software, etc. Therefore, uncovering what developers are communicating about through Gitter is an essential first step towards successfully understanding and leveraging this information.