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Post‐harvest recovery of biodiversity is one of important goals in modern forestry. A variable retention (VR) approach has been of particular interest in North America because it promotes rapid faunal recovery, while minimizing negative lasting impacts of logging on the natural fauna. We studied responses of rove beetles (Coleoptera: Staphylinidae) to a broad range of retention harvests (2, 10, 20, 50 and 75% retention) in comparison to uncut controls as part of the Ecosystem Management Emulating Natural Disturbance (EMEND) experiment in the boreal mixedwood forest of western Canada. We sampled beetles using pitfall traps 1, 2, 11 and 16 years post‐harvest in replicated (n = 3) stands representing four cover types (deciduous‐dominated, deciduous with spruce understory, mixed and coniferous‐dominated). We collected 74 263 individuals distributed across 99 species (excluding Aleocharinae). Estimated species richness was highest in clear‐cuts until year 11, but by year 16 species richness was similar among treatments. Species composition initially varied strongly in relation to intensity of harvest treatments, but overall variation decreased with time, and by year 16, species composition overlapped among most treatment combinations. Assemblages recovered more quickly in early successional (deciduous‐dominated) than in late successional (mixed and conifer‐dominated) stands. Overall, our results show that rove beetle assemblages in stands harvested to all VR prescriptions converged more rapidly toward those in fire‐origin mature stands than did assemblages in clear‐cuts over the first 16 years post‐harvest. Thus, it demonstrates that even modest levels of forest retention can facilitate the recovery of staphylinid assemblages in managed landscapes.

Local spatial variation in species distributions is driven by a mix of abiotic and biotic factors, and understanding such hierarchical variation is important for conservation of biodiversity across larger scales. We sought to understand how variation in species composition of understory vascular plants, spiders, and carabid beetles is associated with concomitant spatial variation in forest structure on a 1-ha permanent plot in a never-cut mixedwood forest in central Alberta (Canada). Using correlations among dendrograms produced by cluster analysis we associated data about mapped distribution of all living and dead stems > 1 cm diameter at breast height with distributions of the three focal taxa sampled from regular grids across the plot. Variation in each of these species assemblages were significantly associated with several forest structure variables at various spatial scales, but the scale of the associations varied among assemblages. Variation in species richness and abundance was explained mostly by changes in basal area of trees across the plot; however, other variables (e.g. snag density and tree density) were also important, depending on assemblage. We conclude that fine-scale habitat variation is important in structuring spatial distribution of the species of the forest floor, even within a relatively homogeneous natural forest. Thus, assessments that ignore within-stand heterogeneity and management that ignores its maintenance will have limited utility as conservation measures for these taxa, which are major elements of forest biodiversity.

Cumulative effects of anthropogenic and natural disturbances have become increasingly relevant in the context of biodiversity conservation. Oil and gas (OG) exploration and extraction activities have created thousands of kilometers of linear footprints in boreal ecosystems of Alberta, Canada. Among these disturbances, seismic lines (narrow corridors cut through the forest) are one of the most common footprints and have become a significant landscape feature influencing the maintenance of forest interior habitats and biodiversity. Wildfire is a common stand‐replacing natural disturbance in the boreal forest, and as such, it is hypothesized that its effects can mitigate the linear footprint associated with OG exploration, but only a few studies have examined its effectiveness. We studied the short‐term (1 year post‐fire) response of rove‐beetle assemblages to the combined effects of wildfire and linear footprint in forest, edge, and seismic line habitats at burned and unburned peatlands along the southwest perimeter of the 2016 Horse River wildfire (Fort McMurray). While rove‐beetle species richness was higher in seismic lines in both the burned and unburned habitats compared with the adjacent peatland, diversity was greater only in seismic lines of burned areas. Abundance was lower in the burned adjacent peatland but similarly higher in the remaining habitats. Assemblage composition on seismic lines was significantly different from that in the adjacent forest and edge habitats within both burned and unburned sites. Moreover, species composition in burned seismic lines was different from either unburned lines or burned forest and edge. Euaesthethus laeviusculus and Gabrius picipennis were indicator species of burned line habitats, are sensitive to post‐fire landscapes and can occupy wet habitats with moss cover more efficiently than when these habitats are surrounded by unburned forest. Although these results are based on short‐term responses, they suggest that wildfire did not reduce the linear footprint, and instead, the cumulative effect of these two disturbances had a more complex influence on rove‐beetle recovery at the landscape level than for other invertebrates. Therefore, continued monitoring of these sites can become useful to evaluate changes over time and to better understand longer‐term biodiversity responses to the cumulative effects of wildfire and linear disturbances in boreal treed peatlands, given the long‐lasting effect of such disturbances. We studied the short‐term (1 year post‐fire) responses of rove beetle assemblages to the combined effects of wildfire and linear footprint in forest, edge and seismic line habitats at burned and unburned peatlands along the southwest perimeter of the 2016 Horse River wildfire, in Fort McMurray (Alberta), with the objective to address whether fire could be used as a natural silvicultural approach to mitigating the linear footprint. The major finding of our study is that wildfire did not reduce the linear footprint, and instead, had a more complex influence on rove beetle recovery at the landscape level.

Large language models (LLMs) have achieved strong performance on code generation, but existing methods still struggle with repository-level code generation under executable validation. Under this evaluation setting, success is determined not by the plausibility of isolated code fragments, but by whether a generated multi-file repository can be successfully installed, have its dependencies and internal references resolved, be launched, and be validated in a real execution environment. To address this challenge, we propose EnvGraph, a framework for repository-level code generation that formulates repository executability as an environment alignment problem. EnvGraph jointly models two coupled conditions for successful repository execution, namely external dependency satisfaction and repository-internal reference resolution. It maintains a dual-layer environment representation, uses execution evidence to perform execution-evidence-based attribution, and guides repository generation through a unified targeted revision mechanism within an iterative alignment loop. We evaluate EnvGraph on repository-level code generation with three representative backbone LLMs and compare it against representative environment-aware and repository-level baselines. Experimental results show that EnvGraph consistently achieves the best performance on these repository-level benchmarks. In particular, it outperforms the strongest non-EnvGraph baseline by an absolute margin of 5.72--5.87 percentage points in Functional Correctness and 4.58--8.66 percentage points in Non-Functional Quality.

Automated test-generation research overwhelmingly assumes the correctness of focal methods, yet practitioners routinely face non-regression scenarios where the focal method may be defective. A baseline evaluation of EVOSUITE and two leading Large Language Model (LLM)-based generators, namely CHATTESTER and CHATUNITEST, on defective focal methods reveals that, despite achieving up to 83% branch coverage, none of the generated tests expose defects, due to a lack of awareness of developer intent. To resolve this problem, we first construct two new benchmarks, namely Defects4J-Desc and QuixBugs-Desc, for experiments, where each focal method is equipped with an additional Natural Language Description (NLD) to support code functionality understanding. Subsequently, we propose DISTINCT, a description-guided branch-consistency analysis framework that transforms LLMs into fault-aware test generators. DISTINCT carries three iterative components: (1) a Generator that derives initial tests based on the NLDs and the focal method, (2) a Validator that iteratively fixes uncompilable tests using compiler diagnostics, and (3) an Analyzer that iteratively aligns test behavior with NLD semantics via branch-level analysis. Extensive experiments confirm the effectiveness of our approach. Compared to state-of-the-art approaches, DISTINCT achieves an average improvement of 14.64% in Compilation Success Rate (CSR), 6.66% in Passing Rate (PR), and particularly 95.22% in Defect Detection Rate (DDR) across both benchmarks. In terms of code coverage, DISTINCT improves Statement Coverage (SC) by an average of 3.77% and Branch Coverage (BC) by 5.36%. These results set a new baseline for non-regressive test generation and highlight how description-driven reasoning enables LLMs to move beyond coverage chasing toward effective defect detection.

Large language models (LLMs) have achieved substantial progress in repository-level code generation. However, solving the same repository-level task often requires multiple attempts, while existing methods still optimize each attempt in isolation and do not preserve or reuse task-specific state across attempts. In this paper, we propose LiveCoder, a novel framework for repository-level code generation based on cross-attempt knowledge optimization. LiveCoder maintains persistent task-specific state from prior attempts to guide subsequent generation. This state includes success knowledge, which captures reusable signals from previously strong repositories, failure knowledge, which records unsuccessful outcomes and their diagnostic signals, and a historical-best repository, which preserves the strongest result found so far and prevents regression. These components collectively transform repeated repository generation into a persistent, knowledge-driven optimization process. We evaluate LiveCoder using four frontier LLMs on two representative repository-level code generation benchmarks. Extensive experimental results demonstrate the effectiveness and efficiency of LiveCoder, improving the functional score by up to 22.94 percentage points, increasing repository reuse to 81.58%, and reducing cost by up to 53.63% on RAL-Bench while maintaining broadly stable non-functional quality.

Commit Message Generation (CMG) approaches aim to automatically generate commit messages based on given code diffs, which facilitate collaboration among developers and play a critical role in Open-Source Software (OSS). Very recently, Large Language Models (LLMs) have demonstrated extensive applicability in diverse code-related task. But few studies systematically explored their effectiveness using LLMs. This paper conducts the first comprehensive experiment to investigate how far we have been in applying LLM to generate high-quality commit messages. Motivated by a pilot analysis, we first clean the most widely-used CMG dataset following practitioners' criteria. Afterward, we re-evaluate diverse state-of-the-art CMG approaches and make comparisons with LLMs, demonstrating the superior performance of LLMs against state-of-the-art CMG approaches. Then, we further propose four manual metrics following the practice of OSS, including Accuracy, Integrity, Applicability, and Readability, and assess various LLMs accordingly. Results reveal that GPT-3.5 performs best overall, but different LLMs carry different advantages. To further boost LLMs' performance in the CMG task, we propose an Efficient Retrieval-based In-Context Learning (ICL) framework, namely ERICommiter, which leverages a two-step filtering to accelerate the retrieval efficiency and introduces semantic/lexical-based retrieval algorithm to construct the ICL examples. Extensive experiments demonstrate the substantial performance improvement of ERICommiter on various LLMs for code diffs of different programming languages. Meanwhile, ERICommiter also significantly reduces the retrieval time while keeping almost the same performance. Our research contributes to the understanding of LLMs' capabilities in the CMG field and provides valuable insights for practitioners seeking to leverage these tools in their workflows.

As an emerging operating system, HarmonyOS has a significant demand for software migration from platforms such as Android and iOS, where the User Interface (UI) translation accounts for a critical link. However, the latest UI development has shifted to declarative paradigms, e.g., Kotlin Jetpack Compose (KJC) for Android, SwiftUI for iOS, and ArkUI for HarmonyOS, rendering prior translation approaches inapplicable, as they target either backend logic or legacy imperative UIs. As such, this paper targets ArkUI and proposes an automatic translation approach, namely ArkTrans, to port UI files from Android and iOS to HarmonyOS. ArkTrans overcomes two salient challenges during the translation: (1) Programming Language (PL) unfamiliarity, and (2) severe syntactic chaos. Towards the first challenge, ArkTrans heuristically constructs ArkUI skeletons by extracting metadata from source PL, thereby guiding LLMs' initial translation. As for the second challenge, ArkTrans executes empirically revealed post-fixing rules via pattern matching to repair most of the remaining syntactic errors. To examine the effectiveness of ArkTrans, we construct a 100-sample parallel UI page translation benchmark from KJC/SwiftUI to ArkUI at the file level. Extensive experiments demonstrate that LLMs with direct/one-shot prompting cannot translate a single compilable UI page. In contrast, at most 90.67\\% ArkTrans-translated files can be successfully compiled with high visual fidelity.

Automated Program Repair (APR) has benefited from the code understanding and generation capabilities of Large Language Models (LLMs). Existing feedback-based APR methods iteratively refine candidate patches using test execution feedback and have shown promising results. However, most rely on outcome-level failure symptoms, such as stack traces, which show how failures are observed but fail to expose the intermediate runtime states critical for root-cause analysis. As a result, LLMs often infer bug causes without sufficient runtime evidence, leading to incorrect patches. To address this limitation, we propose DebugRepair, a self-directed debugging framework for LLM-based APR. DebugRepair enhances patch refinement with intermediate runtime evidence collected through simulated debugging. It consists of three components: test semantic purification, simulated instrumentation, and debugging-driven conversational repair. Together, they reduce noisy test context, collect runtime traces through targeted debugging statements with rule-based fallback, and progressively refine candidate patches using prior attempts and newly observed runtime states. We evaluate DebugRepair on three benchmarks across Java and Python. Experiments show that DebugRepair achieves state-of-the-art performance against 15 approaches. With GPT-3.5, it correctly fixes 224 bugs on Defects4J, outperforming prior SOTA LLM-based methods by 26.2%. With DeepSeek-V3, it correctly fixes 295 Defects4J bugs, surpassing the second-best baseline by 59 bugs. Across five additional backbone LLMs, DebugRepair improves repair performance by 51.3% over vanilla settings. Ablation studies further confirm the effectiveness of all components.

Current Large Language Models (LLMs) have advanced automated unit test generation but face a critical limitation: they often neglect to construct the necessary test fixtures, which are the environmental setups required for a test to run. To bridge this gap, this paper proposes Fixturize, a diagnostic framework that proactively identifies fixture-dependent functions and synthesizes test fixtures accordingly through an iterative, feedback-driven process, thereby improving the quality of auto-generated test suites of existing approaches. For rigorous evaluation, the authors introduce FixtureEval, a dedicated benchmark comprising 600 curated functions across two Programming Languages (PLs), i.e., Python and Java, with explicit fixture dependency labels, enabling both the corresponding classification and generation tasks. Empirical results demonstrate that Fixturize is highly effective, achieving 88.38%-97.00% accuracy across benchmarks in identifying the dependence of test fixtures and significantly enhancing the Suite Pass rate (SuitePS) by 18.03%-42.86% on average across both PLs with the auto-generated fixtures. Owing to the maintenance of test fixtures, Fixturize further improves line/branch coverage when integrated with existing testing tools of both LLM-based and Search-based by 16.85%/24.08% and 31.54%/119.66% on average, respectively. The findings establish fixture awareness as an essential, missing component in modern auto-testing pipelines.