AI coding agents allow software developers to generate code quickly, which raises a practical question for project managers and open source maintainers: can vibe coders with less development experience substitute for expert developers? To explore whether developer experience still matters in AI-assisted development, we study 22,953 Pull Requests (PRs) from 1,719 vibe coders in the GitHub repositories of the AIDev dataset. We split vibe coders into lower experience vibe coders (ExpLow) and higher experience vibe coders (ExpHigh) and compare contribution magnitude and PR acceptance rates across PR categories. We find that ExpLow submits PRs with larger volume (2.15 × more commits and 1.47 × more files changed) than ExpHigh. Moreover, ExpLow PRs, when compared to ExpHigh, receive 4.52 × more review comments, and have 31% lower acceptance rates, and remain open 5.16 × longer before resolution. Our results indicate that low-experienced vibe coders focus on generating more code while shifting verification burden onto reviewers. For practice, project managers may not be able to safely replace experienced developers with low-experience vibe coders without increasing review capacity. Development teams should therefore combine targeted training for novices with adaptive PR review cycles.

Continuous Integration and Deployment (CI/CD) workflows are central to modern software delivery, yet the reliability of agentic AI bots operating within these workflows remain underexplored. Using pull requests (PRs), commits, and repositories from the AIDev dataset, we retrieved associated CI/CD workflow runs via the GitHub Actions API and analyzed 61,837 runs from 2,355 repositories, all triggered by PRs generated by five AI bots: Claude, Devin, Cursor, Copilot, and Codex. We observed substantial agent-dependent differences in workflow reliability, with Copilot and Codex achieving the highest success rates ∼ 93% and ∼ 94% respectively. At the repository level, we find a negative correlation between AI agent contribution frequency and workflow success rate, suggesting that a higher frequency of Agentic PRs may hinder CI/CD workflow reliability. We defined a taxonomy of 13 categories against 3,067 agentic PRs whose associated workflows failed, and observed a trend analysis that indicates visually observable shifts from functional to non-functional PR categories over time, although these trends are not statistically significant. Our findings motivate the need for actionable guidance on integrating AI agents into CI/CD workflows and prioritizing safeguards in workflows where failures are most likely to occur.

Distributed applications, in particular web applications, often depend on a centralized database. The results of database operations depend on the state of database at that time and often also on the order of execution of operations performed by concurrent clients. Verification of such applications requires modeling all these possible orders so that the user can determine which are incorrect orderings and can prevent them with transactions or business logic. However, straightforward exploration leads to state space explosion. Partial order reduction prunes orderings that are equivalent to other orderings already explored. We present a novel technique of Effective Partial Order Reduction (EPOR) for model checking software of Java applications sharing database state. EPOR improves upon prior work by performing a more precise analysis and supports many more operations. The key idea behind EPOR is that monitoring the effect of database operations inside database implementation gives a more precise view of operation dependencies than what can be achieved from an external view. Like prior work, EPOR also relies on Java Pathfinder model checker for model checking Java application. However, unlike prior work, there is additional instrumentation inside the database that enables our precise analysis and allows supporting more constructs. Our results improve upon prior work by achieving significant reduction in number of states explored and thus enables more effective model checking of database applications with concurrent operations.

Stored procedures in database management systems are often used to implement complex business logic. Correctness of these procedures is critical for correct working of the system. However, testing them remains difficult due to many possible states of data and database constraints. This leads to mostly manual testing. Newer tools offer automated execution for unit testing of stored procedures but the test cases are still written manually.