DDB is presented, a source-level interactive debugger that extends interactive debugging capabilities to distributed applications and shows that each of these challenges admits a targeted solution.
Abstract
Interactive debugging is an effective tool for understanding program behavior at the source level, allowing developers to pause execution, navigate the call stack, and inspect runtime state. However, interactive debuggers are designed for single-process execution, and interactive debugging has been widely considered impractical for distributed systems. Call stacks stop at process boundaries, debugging state fails to survive infrastructure dynamics, and, most critically, debugger-induced execution pauses trigger catastrophic timeout cascades that destroy the intended debug flow. Consequently, developers are forced to abandon live hypothesis testing in favor of unwieldy and iterative log-and-redeploy cycles. We present DDB, a source-level interactive debugger that extends interactive debugging capabilities to distributed applications. We show that each of these challenges admits a targeted solution. To bridge disjoint processes, Distributed Backtrace (DBT) embeds compact causality metadata in every RPC and reconstructs a unified call stack across RPC boundaries. To manage the lifecycle of a distributed session, an intent-preserving control plane automatically coordinates and propagates breakpoints across dynamic process sets. To make pausing safe, Pause-Erased Time (PET) virtualizes each process's clock, decoupling logical time from physical pauses and preventing timeout cascades. DDB integrates with an RPC framework in 20-60 lines of code. Evaluated on gRPC, ServiceWeaver, Nu, and Quicksand across up to 122 processes, DDB achieves 30ms median cross-RPC backtrace latency, sub-5 ms time jump under repeated execution pauses, and adds 1-5% throughput overhead, comparable to attaching a single-process debugger. In a controlled user study, DDB achieves a 100% fault localization success rate (compared to 38.5% for baseline tools) with a median localization time of ~8 minutes.
Overall, TraceGate shows that rethinking debugging through controlled observability, rather than relying solely on stronger models or larger prompts, can make LLM-assisted repair more effective, efficient and controllable.
Nicolas Schuler, MateVincenzoScotti, RaffaelaMirandola· 0 citations
This paper proposes online abstract debugging, which performs abstract interpretation on demand as the developer steps through the program, and instantiate this model for WebAssembly and presents a prototype implementation, demonstrating that developers can effectively draw sound conclusions about complex, potentially non-terminating looping programs.
J. Alvim, Maarten Steevens, Christophe Scholliers et al.· 0 citations
Fault localization (FL) is a dominant debugging cost, yet most recent LLM-based FL systems rely on static or coverage-only signals. We introduce <sc>ANVIL-FL</sc> (Anchored Near-failure Value-Informed Localization), a two-turn, tool-free framework that combines LLM reasoning with failure-anchored runtime telemetry. A lightweight tracer runs the test session and stops at the first failure, recording project-scoped function calls/returns and exceptions. <sc>ANVIL-FL</sc> first narrows candidates by proximity to the failure chain, then ranks methods using value-informed I/O snapshots (arguments/returns) and executed code snippets, generating a Top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> list with rationales and confidence. On BugsInPy, <sc>ANVIL-FL</sc> achieves Top-1/3/5 accuracies of 57.6/68.6/70.3% on the SBFL-available subset (<inline-formula> <tex-math notation="LaTeX">$N=424$ </tex-math></inline-formula>) and 57.0/67.6/69.4% on our full in-scope set (<inline-formula> <tex-math notation="LaTeX">$N=454$ </tex-math></inline-formula>). Against the strongest SBFL baseline on the overlapping set, <sc>ANVIL-FL</sc> improves Top-1/3/5 by + 38.7/+ 39.1/+ 32.8 percentage points (pp). On an overlapping test set (<inline-formula> <tex-math notation="LaTeX">$N=452$ </tex-math></inline-formula>), <sc>ANVIL-FL</sc> approaches AutoFL’s accuracy (Top-1/3/5: 57.1/67.7/69.5% vs. 59.3/71.9/73.0%), while using about <inline-formula> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> fewer input and <inline-formula> <tex-math notation="LaTeX">$4.8\times $ </tex-math></inline-formula> fewer output tokens per bug. Tracing overhead is modest (median 9,394 events, 4.30 MB, 11.74 s), and the full pipeline localizes each bug end-to-end in a median of 44 s at roughly <inline-formula> <tex-math notation="LaTeX">${\$}0.008$ </tex-math></inline-formula> per bug. Ablations show that failure-chain anchoring dominates (removal: approx. −40.8 pp at Top-5), with runtime I/O and executed code providing smaller but notable gains. By combining dynamic runtime signals with structured LLM prompting, our framework demonstrates that failure-proximal runtime context can yield accurate, explainable, and cost-effective method-level fault localization within the BugsInPy setting.
Ahman J. Smith, Vijay K. Madisetti· IEEE Access· 0 citations
This work presents CodeMechanic, a bug-property-guided system for generating constrained mit- igations for spatial memory corruption that combines a two-dimensional static and dynamic context extractor with in-prompt debugging knowledge and stepwise val- idation to limit the effect of LLM errors.
Han Zheng, Rafaila Galanopoulou, Ilia Shumailov et al.· 0 citations
Distributed in-network programs are increasingly deployed in data centers for their performance benefits, but shifting application logic to switches also enlarges the failure domain. Ensuring their correctness before deployment is thus critical for reliability. While prior verification frameworks can efficiently verify programs running on a single switch, they overlook the common interactive behaviors in distributed settings, thereby missing related bugs that can cause system failures. This paper presents Procurator, a verification framework that efficiently captures interactive behaviors in distributed in-network programs. Procurator models each P4 pipeline as a reactive actor and unifies their interactions as message passing to capture interactive behaviors under an event-driven paradigm. To improve the verification efficiency, Procurator employs an intermediate representation (IR) pruner to reduce the execution space and a schedule-replay-based acceleration approach to avoid explicit exploration of long execution traces. Evaluation shows that Procurator uncovers 28 distinct bugs in twelve real-world distributed in-network systems, and achieves up to a 9.1X speedup over the state-of-the-art framework.
Mingyuan Song, Huan-Xing Shen, Jinghui Jiang et al.· Conference on Applications,...· 0 citations
SQL is declarative in nature and rich in its features. Writing semantically correct SQL queries and finding logical bugs in SQL are not easy, even for experienced programmers, who are often used to the mindset of working with general-purpose programming languages (GPLs). While there are many GPL debuggers, SQL debugging has received much less attention. In this paper, we present I-Rex, a SQL debugger that enables users to inspect the logical execution of SQL queries visually and interactively to identify and potentially fix logical bugs in the queries. I-Rex draws analogies to the debugging paradigm of GPLs (e.g., stepping, watchpoints, etc.), making it easier for programmers to adopt. However, unlike debugging GPLs, which involves executing the underlying program in full to the point of interest, I-Rex allows users to jump to arbitrary points of interest by leveraging the power of the database systems, through selective materialization and query rewrites. To simplify deployment, I-Rex acts as a lightweight middleware on top of the database system; it imposes no overhead to prepare a database for debugging and maintains no state in the database systems during debugging sessions. We demonstrate the effectiveness of I-Rex through performance experiments as well as a user study in an educational setting.
Yi-Hao Hu, Zian Chen, Z. Leong et al.· Proceedings of the VLDB Endo...· 0 citations
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