troubleshoot toolchains

Early-stage software development—spanning requirements elicitation, testing, and deployment—is hindered by ill-defined tasks and dense manual intervention points, impeding automation. Traditional CI/CD pipelines address only post-coding phases, leaving semantic gaps between underspecified stages unbridged. Method: We propose “workflow-as-software,” a novel paradigm that models end-to-end development as programmable workflows. Leveraging large language models (LLMs) as universal semantic adapters, our approach automatically reconciles heterogeneous task semantics. It integrates domain-specific workflow orchestration, a lightweight domain-specific language (DSL), and semantic translation interfaces. Contribution/Results: Evaluated in production at Volvo, the method reduced test automation effort by 2–3 full-time engineers and compressed the end-to-end development-to-deployment cycle to two months. It marks the first demonstration of LLM-driven, fully automated software delivery across the entire lifecycle—from requirements to deployment—thereby extending automation beyond conventional CI/CD boundaries.

Implementation discrepancies across software repository mining tools severely threaten the validity of empirical findings. Method: We conduct a dual-tool comparative analysis of 10 large-scale open-source projects, systematically identifying how minor implementation differences—such as commit parsing logic and author deduplication rules—induce up to 500% deviation in key metrics (e.g., commit count, developer count). We propose a “tool-level configuration + post-hoc normalization” co-optimization framework to mitigate metric divergence and perform multi-tool experiments, quantitative consistency assessment, and code-level root-cause analysis. Contribution/Results: We identify six technical sources undermining data validity and establish the first validity assessment paradigm for Mining Software Projects Research (MSPR) explicitly addressing tool heterogeneity—thereby enabling rigorous, reproducible, and comparable empirical software engineering studies.

This study addresses the lack of standardized guidance for effectively integrating technical debt management tools into existing CI/CD practices, which hinders the continuous control of technical debt. By systematically analyzing approximately 600,000 Travis CI configuration files and 50,000 auxiliary scripts from GitHub, the authors identify 3,684 pipelines that integrate technical debt management tools. Their findings reveal that such integrations predominantly rely on external script invocations and frequently exhibit configuration anti-patterns, notably the absence of feedback mechanisms. This work provides empirical evidence of current integration practices and prevalent anti-patterns, offering actionable insights to inform the design of better tooling and improve CI/CD integration strategies for technical debt management.

This work addresses a critical yet overlooked reliability issue in code generated by large language models (LLMs): despite passing compilation and unit tests, such code often fails in deployment due to structural inconsistencies—such as missing configurations, invalid imports, or omitted security controls—that evade detection by conventional CI/SAST tools. The paper introduces the “patchwork problem” to characterize these cross-module global defects, proposes an eight-category taxonomy specific to LLM-generated code, and formalizes structural consistency via invariants derived from a multidimensional code graph encompassing imports, calls, dependencies, configurations, and routing. Building on this foundation, the authors design a hybrid verification framework that integrates traditional static analysis with custom graph-based invariant checkers to precisely identify structural flaws invisible to existing tools. Empirical evaluation reveals that such defects are pervasive across major LLMs under diverse prompting strategies and exhibit distinct model-specific patterns.

This work addresses the challenge of formally modeling and verifying high-level coordination logic in smart contracts within decentralized systems. It proposes a formal method based on coordination models that supports dynamic roles, data-driven state transitions, and external coordination interfaces. For the first time, this approach integrates formal coordination models with automated code generation and test case synthesis for smart contracts, yielding a platform-agnostic toolchain extensible to multiple contract languages. The expressiveness and engineering practicality of the method are demonstrated through the modeling and implementation of several representative coordination patterns.

This study addresses the lack of systematic understanding regarding how GitHub Actions workflows are used in real-world scenarios, how developers respond to workflow failures, and how these practices relate to project characteristics. Combining large-scale quantitative analysis of 258,300 workflow runs with qualitative case studies across 21 diverse repositories, this work identifies three typical patterns developers employ to handle workflow failures and uncovers a “configuration–usage gap”—where YAML configurations exist but workflows remain effectively unused. Furthermore, the study empirically validates five hypotheses linking project features to workflow usage intensity, revealing a significant positive correlation between high usage intensity and low failure rates. These findings provide actionable empirical evidence for improving CI/CD practices.

This study addresses the imbalance in the test pyramid—characterized by an overreliance on coarse-grained integration and system tests, which leads to difficulties in fault localization and slow execution—by proposing, for the first time, a method to automatically generate unit tests from existing integration tests. The approach combines static and dynamic analysis to automatically isolate component dependencies and enhance coverage at the unit level. Implemented as a Node.js tool and evaluated on twelve open-source JavaScript projects, the technique produces high-quality unit tests that significantly improve test suite structure, thereby increasing both testing efficiency and maintainability.

This work addresses the lack of structured, verifiable, and governable tool support for large language model (LLM) agents in operational tasks, where existing approaches are often static or manually integrated, struggling to balance security and extensibility. The authors propose the “Tool Capsule” paradigm, which encapsulates tools as standardized units comprising intent, contract, implementation, policy, and verification evidence. They design an efficient intent-scoped routing mechanism enabling on-demand, secure tool invocation. The system integrates a sandboxed verification pipeline, MCP-compatible routing, credential binding, and lifecycle governance. Experiments demonstrate a micro F1 score of 0.901 across 83 routing tests with a 99.2% reduction in context overhead; all 25 end-to-end tasks produced valid toolkits (micro F1 = 0.940), with 23 successfully passing real-time sandbox validation.

This work addresses the challenge of silent updates to large language models (LLMs) by service providers, which often occur without version changes and can lead to behavioral drift and functional regressions, while existing mechanisms lack deployment-side control over compatibility governance. Framing LLM updates as a software supply chain governance problem, this study proposes a deployment-side control framework that defines rule-based production contracts, constructs risk-category-oriented test suites, and enforces compatibility gates to validate model safety and performance prior to updates. Experimental results demonstrate that the approach effectively uncovers fine-grained regressions missed by aggregate metrics, while also highlighting critical challenges in test design, threshold calibration, and drift attribution.