This paper advocates a reasoning-centered perspective in which debugging systems generate, empirically challenge, and curate multiple hypotheses, treating explanations as first-class artifacts and accuracy as a constraint rather than the sole goal.
Abstract
Performance debugging in microservice systems is commonly framed as an optimization problem that seeks a single "best" root cause under accuracy-driven objectives. We argue that this framing creates an optimization trap: it obscures the reality of production environments, where evidence is incomplete, failures interact, and engineers must reason under uncertainty. This paper advocates a reasoning-centered perspective in which debugging systems generate, empirically challenge, and curate multiple hypotheses, treating explanations as first-class artifacts and accuracy as a constraint rather than the sole goal. We introduce the notion of a Reasoning Surface—a conceptual debugging artifact that maintains multiple, empirically grounded explanations and makes uncertainty and trade-offs explicit, rather than collapsing diagnosis into a single opaque result. Through an initial instantiation, we illustrate how this perspective leads to qualitatively different debugging behavior, particularly under operational stress, and argue for a shift beyond single-number leaderboards toward systems that support human-centered reasoning.
Autonomous R\&D agents now write, run, and improve executable artifacts under automated evaluation---but largely as laboratory instruments: shown on curated benchmarks, with gains that are hard to trace to a cause and costs well above what sustained engineering practice absorbs. The limitation is structural. Most systems treat each attempt as nearly self-contained, so logs, memories, and search trees record what happened without establishing which design element produced an improvement, whether its evidence survived validation, or how it recombines with others. Long campaigns therefore keep re-learning the same lessons. We introduce Praxist, a lineage-centered generational system that converts reproducible artifacts and evaluator outcomes into a typed evidence graph of findings, lane-structured frontiers, and agendas. Separating local artifact construction from cohort-level evidence synthesis lets later attempts inherit validated mechanisms, unresolved claims, and useful constraints, and leaves results attached to an inspectable lineage. On the standardized 75-task MLE-bench suite, the finalized official-grader results give Praxist 60 medals (80.0\%), 49 of them gold, against 55 medals (73.3\%) and 34 gold for a Claude Code baseline on Claude Opus 4.8---at a recorded model spend of US\$3,054 versus US\$38,370, roughly a twelfth of the cost. Four case studies---quantitative trading, LiDAR-inertial-visual SLAM, tokamak magnetic control, and rocket landing---carry the same process into open-ended engineering problems, improving on each task-native baseline in headline accuracy, survival, or resource cost, with the discovery path on record. Stronger artifacts at an order of magnitude less spend, each backed by an auditable lineage, are, to our knowledge, first brought together here: the operating profile production research requires, not the one a benchmark demonstration establishes.
Jin Li, Ahmed Murtadha, Zhiying Wang et al.· 0 citations
WasmMend is presented, the first system to automatically repair Native-Wasm functional discrepancies and demonstrates the value of divergence-guided reasoning for cross-platform repair.
The scientific method has long guided empirical research in Software Engineering (SE), but the complexity of modern software systems often hinders its systematic application. This paper introduces ECLAIR, a causally grounded AI framework that integrates Large Language Models (LLMs) into every stage of the scientific process, from hypothesis generation to analysis and interpretation. ECLAIR treats LLMs as active scientific agents operating under the principles of causal inference, within a human-in-the-loop design that safeguards against the risks of unsound automated reasoning. We demonstrate the framework through a case study examining how prompt design influences code generation accuracy in two LLMs. Results show that, for both models, instruction-style, longer few-shot, and signature-augmented prompts yield small negative causal effects on accuracy, illustrating how causal reasoning provides a principled foundation for explaining why software phenomena occur. This study presents the first causally grounded structured methodology for embedding LLMs within the scientific method in SE, designed around the epistemological demands of empirical SE research, establishing a basis for rigorous AI-assisted research.
Alejandro Velasco, Daniel Rodríguez-Cárdenas, Dipin Khati et al.· 0 citations
Results show that CUA root-cause diagnosis can provide actionable repair signals rather than merely post-hoc explanations and show that CUA root-cause diagnosis can provide actionable repair signals.
Wei-Jia Zhang, Kunlun Zhu, Zeyi Liu et al.· 0 citations
Programming error messages are critical for software development, yet they remain difficult for novice programmers to interpret. While Large Language Models (LLMs) can rewrite these errors into clearer explanations, it remains unclear whether increased readability improves objective debugging performance or how explanation styles should align with programmer skill. We present a multi-stage crowdsourced study N=103 evaluating skill-targeted, LLM-generated Python error messages. Using a custom proficiency assessment, we categorized participants by skill level and tested standard interpreter messages against two LLM-generated styles: pragmatic (action-oriented) and contingent (scaffolded explanations). We measured both objective debugging metrics (fix rate, attempts, time-to-fix) and subjective perceptions (readability, cognitive load, tone). Our results show that while LLM-rewritten messages significantly improved subjective evaluations, with pragmatic messages rated as clearer and less cognitively demanding, these perceived gains did not translate into statistically significant improvements in objective debugging performance. This highlights a critical human-AI complementarity gap: explanations that feel better to users do not necessarily make them more effective debuggers. We discuss design implications for adaptive AI feedback systems, arguing that future tools should pivot from static skill-targeted rewriting toward dynamic adjustments based on a user's real-time repair trajectory.
Alexandru-Radu Moraru, Shreyan Biswas, U. Gadiraju· 0 citations
An exploratory review of the emerging gray literature, which largely agrees on what a well-engineered loop contains: triggered agent runs bounded by machine-checkable stop conditions, persistent state files, verifier sub-agents, token budgets, and defined points of escalation to humans.
Jai Lal Lulla, Vahram Nersesyan, Seyedmoein Mohsenimofidi et al.· 0 citations