Xiaoyu Lin

Artificial intelligence (AI) coding systems now range from inline completion to repository-level agents and platform-supported application builders; yet, software engineering still lacks a code-generation-centered operational taxonomy for describing how much work is delegated, under what conditions, and with what responsibility structure. This study proposes Levels of Automated Code Generation (LACG), a six-level taxonomy (L0–L5) for classifying automation in AI-augmented software construction. LACG is organized around four responsibility-aware concepts—The Software Development Task (SDT), Operational Capability Domain (OCD), fallback responsibility, and minimal risk condition—and is assigned to a declared configuration–SDT–OCD tuple rather than to a vendor brand or model family in the To reduce the risk that public vendor documentation reproduces marketing bias, the method separates declared affordance evidence from routine capability evidence and adopts an evidence-triangulation design. Public documentation is used only to identify configuration boundaries and declared affordances; independent software engineering benchmarks, agent studies, productivity studies, and taxonomy-evaluation literature are used to calibrate the level boundaries and constrain the claims. LACG is then applied to 30 representative current AI coding tool configurations using time-stamped public-documentation records, with boundary logic cross-checked against independent evidence on repository-level issue solving, agent tool use, and context-dependent productivity outcomes. Three anonymized human raters, selected for software engineering or AI-coding-tool expertise and independent of the authors and evaluated vendors, then classified the same prepared, blinded public-documentation records using the LACG coding manual. Exact three-rater agreement was 28/30 (93.3%); adjacent-level and majority agreement were both 30/30 (100.0%); mean pairwise quadratic-weighted Cohen’s kappa was 0.963; and Krippendorff’s alpha for ordinal ratings was 0.963. These agreement statistics test classification consistency over a structured documentary evidence base; they do not test actual tool behavior, direct execution, product performance, safety, productivity, or deployment outcomes. After adjudication, the final sample contains six L1 configurations, nine L2 configurations, and fifteen L3 configurations; no public configuration is classified as L4 or L5 under the fallback-responsibility criterion. The study supports preliminary, documentation-bound classification applicability, boundary calibration, and discriminative vocabulary development, not predictive validation or product-level performance claims. LACG provides an operational vocabulary for future empirical work on AI-augmented software construction, benchmark design, tool comparison, and responsibility allocation, while leaving outcome validation for governance, security, productivity, and procurement to subsequent empirical studies.

Effective weed management is essential for protecting crop yields in cotton production, yet conventional deep learning approaches often falter in detecting small or occluded weeds and can be restricted by large parameter counts. To tackle these challenges, we propose YOLO-ACE, an advanced extension of YOLOv5s, which was selected for its optimal balance of accuracy and speed, making it well suited for agricultural applications. YOLO-ACE integrates a Context Augmentation Module (CAM) and Selective Kernel Attention (SKAttention) to capture multi-scale features and dynamically adjust the receptive field, while a decoupled detection head separates classification from bounding box regression, enhancing overall efficiency. Experiments on the CottonWeedDet12 (CWD12) dataset show that YOLO-ACE achieves notable mAP@0.5 and mAP@0.5:0.95 scores—95.3% and 89.5%, respectively—surpassing previous benchmarks. Additionally, we tested the model’s transferability and generalization across different crops and environments using the CropWeed dataset, where it achieved a competitive mAP@0.5 of 84.3%, further showcasing its robust ability to adapt to diverse conditions. These results confirm that YOLO-ACE combines precise detection with parameter efficiency, meeting the exacting demands of modern cotton weed management.

The list-based threshold accepting (LBTA) algorithm is a sophisticated local search method that utilizes a threshold list to streamline the parameter tuning process in the traditional threshold accepting (TA) algorithm. This paper proposes an enhanced local search version of the LBTA algorithm specifically tailored for solving the 0–1 knapsack problem (0–1 KP). To maintain a dynamic threshold list, a feasible threshold updating strategy is designed to accept adaptive modifications during the search process. In addition, the algorithm incorporates an improved bit-flip operator designed to generate a neighboring solution with a controlled level of disturbance, thereby fostering exploration within the solution space. Each trial solution produced by this operator undergoes a repair phase using a hybrid greedy repair operator that incorporates both density-based and value-based add operator to facilitate optimization. The LBTA algorithm’s performance was evaluated against several state-of-the-art metaheuristic approaches on a series of large-scale instances. The simulation results demonstrate that the LBTA algorithm outperforms or is competitive with other leading metaheuristics in the field.

Nonheading Chinese cabbage is an important leafy vegetable, and quantitative identification and automated analysis of nonheading Chinese cabbage leaves are crucial for cultivating new varieties with higher quality, yield, and resistance. Traditional leaf phenotypic analysis relies mainly on phenotypic observation and the practical experience of breeders, leading to issues such as time consumption, labor intensity, and low precision, which result in low breeding efficiency. Considering these issues, a method for the extraction and analysis of phenotypes of nonheading Chinese cabbage leaves is proposed, targeting four qualitative traits and ten quantitative traits from 1500 samples, by integrating deep learning and OpenCV image processing technology. First, a leaf classification model is trained using YOLOv8 to infer the qualitative traits of the leaves, followed by the extraction and calculation of the quantitative traits of the leaves using OpenCV image processing technology. The results indicate that the model achieved an average accuracy of 95.25%, an average precision of 96.09%, an average recall rate of 96.31%, and an average F1 score of 0.9620 for the four qualitative traits. From the ten quantitative traits, the OpenCV-calculated values for the whole leaf length, leaf width, and total leaf area were compared with manually measured values, showing RMSEs of 0.19 cm, 0.1762 cm, and 0.2161 cm², respectively. Bland–Altman analysis indicated that the error values were all within the 95% confidence intervals, and the average detection time per image was 269 ms. This method achieved good results in the extraction of phenotypic traits from nonheading Chinese cabbage leaves, significantly reducing the personpower and time costs associated with genetic resource analysis. This approach provides a new technique for the analysis of nonheading Chinese cabbage genetic resources that is high-throughput, precise, and automated.