Logs are essential for understanding Continuous Integration (CI) behavior, particularly for diagnosing build failures and performance regressions. Yet their growing volume and verbosity make both manual inspection and automated analysis increasingly costly, time-consuming, and environmentally costly. While prior work has explored log compression, anomaly detection, and LLM-based log analysis, most efforts target structured system logs rather than the unstructured, noisy, and verbose logs typical of CI workflows.

AI agents are increasingly used in software development, yet their interaction with CI/CD configurations is not well studied. We analyze 8,031 agentic pull requests (PRs) from 1,605 GitHub repositories where AI agents touch YAML configurations, encompassing 711,923 file changes across five agents. CI/CD configuration files account for only 3.25% of agent changes, with significant variation across agents (Devin: 4.83%, Codex: 2.01%, p < 0.001). When agents change CI/CD, 96.77% target GitHub Actions. Agentic PRs with CI/CD changes merge slightly less often than others (67.77% vs. 71.80%), except for Copilot, whose CI/CD changes are merged 15.63 percentage points more often. Across 99,930 workflow runs, we observe comparable build success rates for CI/CD and non-CI/CD changes (75.59% vs. 74.87%), though three agents have significantly higher success when changing CI/CD. These results show that AI agents rarely change CI/CD and focus mainly on GitHub Actions, yet their configuration changes are as reliable as regular code. Copilot’s strong CI/CD performance despite lower overall acceptance suggests emerging configuration specialization, with implications for agent training and DevOps automation.

AI coding agents are reshaping software development through both autonomous and human-mediated pull requests (PRs). When developers use AI agents to generate code under their own accounts, code authorship attribution becomes critical for repository governance, research validity, and understanding modern development practices. We present the first study on fingerprinting AI coding agents, analyzing 33,580 PRs from five major agents (OpenAI Codex, GitHub Copilot, Devin, Cursor, Claude Code) to identify behavioral signatures. With 41 features spanning commit messages, PR structure, and code characteristics, we achieve 97.2% F1-score in multi-class agent identification. We uncover distinct fingerprints: Codex shows unique multiline commit patterns (67.5% feature importance), and Claude Code exhibits distinctive code structure (27.2% importance of conditional statements). These agent-specific signatures reveal that AI coding tools produce detectable behavioral patterns, suggesting potential for identifying AI contributions in software development repositories.

AI coding agents can autonomously generate pull requests (PRs), yet little is known about how their contributions compare to those of humans. We analyze 33,596 agent-generated PRs (APRs) and 6,618 human PRs (HPRs) to compare code-change characteristics and message quality. We observe that APR-introduced symbols (functions and classes) are removed much sooner than those in HPRs (median time to removal 3 vs. 34 days) and are also removed more often (symbol churn 7.33% vs. 4.10%), reflecting a focus on other tasks like documentation and test updates. Agents generate stronger commit-level messages (semantic similarity 0.72 vs. 0.68) but lag humans at PR-level summarization (PR–commit similarity 0.86 vs. 0.88). Commit message length is the best predictor of description quality, indicating reliance on individual commits over full-PR reasoning. These findings highlight a gap between agents’ micro-level precision and macro-level communication, suggesting opportunities to improve agent-driven development workflows.

Mobile apps face strong pressure for fast and reliable updates. Continuous Integration (CI) helps automate builds, tests, and releases, but its impact on mobile development remains underexplored. Despite the widespread use of CI, little is known about how it affects development activity, release speed, and user-facing outcomes in mobile projects. Existing studies mostly focus on CI adoption in general-purpose software, providing limited insight into mobile-specific dynamics, such as app store visibility and user engagement. In this paper, we analyze open-source Android apps to (1) compare CI adopters and non-adopters, (2) characterize adoption patterns using activity and bug metrics, and (3) assess pre/post adoption changes and user-facing outcomes. We observe that CI adopters are larger and more active, with faster and more regular releases. CI adoption is concentrated in integration- and reliability-intensive categories (e.g., finance and productivity) and is associated with higher Google Play Store engagement (more downloads and reviews) without lower ratings. Overall, CI adoption aligns with practices that support sustained delivery, higher project visibility, and stronger user engagement in mobile ecosystems.