Retry---the re-execution of a task on failure---is a common mechanism to enable resilient software systems. Yet, despite its commonality and long history, retry remains difficult to implement and test.

It is well known that valuable information is contained in the natural language components of software systems, like comments and manual, and such information can be used to improve system performance and reliability. Past research has attempted to extract such information through task-specific machine learning models and tool chains. Here, we investigate a general, one-model-fit-all solution through a state-of-the-art large language model (e.g., the GPT series). Our investigation covers three representative tasks: extracting locking rules from comments, synthesizing exception predicates from comments, and identifying performance-related configurations; it reveals challenges and opportunities in applying large language models to system maintenance tasks.

Upgrade is one of the most disruptive yet unavoidable maintenance tasks that undermine the availability of distributed systems. Any failure during an upgrade is catastrophic, as it further extends the service disruption caused by the upgrade. The increasing adoption of continuous deployment further increases the frequency and burden of the upgrade task. In practice, upgrade failures have caused many of today's high-profile cloud outages. Unfortunately, there has been little understanding of their characteristics.

Database-backed web applications manipulate large amounts of persistent data, and such applications often contain constraints that restrict data length, data value, and other data properties. Such constraints are critical in ensuring the reliability and usability of these applications. In this paper, we present a comprehensive study on where data constraints are expressed, what they are about, how often they evolve, and how their violations are handled. The results show that developers struggle with maintaining consistent data constraints and checking them across different components and versions of their web applications, leading to various problems. Guided by our study, we developed checking tools and API enhancements that can automatically detect such problems and improve the quality of such applications.