Cross-project defect prediction (CPDP) is a promising solution for new software projects lacking historical data. However, modern privacy/compliance constraints and limited sharing budgets often restrict how many labeled source instances can be transferred across organizations. Existing instance selection strategies typically prioritize “safe” majority instances or rely on random sampling, which can miss boundary-defining defects under very small budgets. To address this, we propose weakness-aware subset selection (WASS), a framework for source-side local curation under a source-sharing budget. WASS combines target-domain similarity, weakness-aware scoring, and class-aware quota allocation to curate a compact transferable subset of source instances. Extensive experiments on 88 directed same-family CPDP pairs with seven diverse classifiers (including transfer component analysis [TCA] + logistic regression [LR] and support vector machine [SVM]) show a clear 1%–5% Golden Window under the evaluated protocol. The experimental protocol also clarifies source-sharing accounting, repeated-run randomness, and family-level variation. Therefore, WASS is a practical, plug-and-play cold-start accelerator for source-sharing-constrained CPDP.
Driven by the rapid advancement of un-manned aerial vehicle (UAV) and deep learning technologies, UAV surveillance has emerged as a vital supplement to conventional ground monitoring systems due to its flexible deployment, wide coverage, and real-time capability. However, most state-of-the-art human action recognition models are trained on ground-level video data and struggle to adapt to the inherent characteristics of aerial imagery, including extremely small object scales, low resolution, complex background interference, and severe mutual occlusion. To address these technical bottlenecks in UAV-based human detection, this paper presents a lightweight yet high-precision detection method, dubbed RDDR-YOLO, based on YOLOv8s. Extensive experiments are conducted on two public benchmark datasets, VisDrone2019 and TinyPerson. Results show that RDDR-YOLO achieves mAP50 values of 43.9% and 36.4% on the two datasets, outperforming the YOLOv8s baseline by 7.0% and 9.6%, respectively. Its human localization precision reaches 41.2%, yielding an improvement of 14.5%. Moreover, the parameter count is reduced by 67.6%, and the inference speed reaches 70 FPS. The proposed method substantially improves the detection accuracy of small-scale human targets in UAV scenarios while maintaining competitive real-time performance. It can serve as the 2D localization module in the first stage of a two-stage human action recognition framework, effectively supporting human pose estimation and motion capture in UAV-based scenarios. The method holds significant practical potential in smart city security, emergency search and rescue, crowd management, and other related fields.
Decision-making in software development involves balancing multiple technical and organizational factors. While multicriteria decision-making (MCDM) methods offer systematic approaches to such problems, they typically do not account for practitioners' emotional states, which may influence judgments. This study examines how affective states impact decision-making and proposes a framework integrating psychometric evaluation with MCDM techniques. We present a framework combining MCDM, specifically the best-worst method (BWM) for problem formulation, with an affective scale, specifically the International Positive and Negative Affect Schedule - Short Form (I-PANAS-SF), to estimate the individual's affective state and mitigate the potential negative influence it might have on their decision. An experiment was conducted to observe how the valence of the affective state (whether positive or negative) may impact the importance assigned to decision criteria. Evaluations were filtered to include inputs from participants exhibiting positive affect, and the resulting prioritization outcomes were compared with an external benchmark dataset. The results indicate that the framework produced different aggregated decisions depending on affective valence, with positive-affect filtering yielding distinct prioritization patterns. Whether these differences improve decision quality requires direct comparison with affect-agnostic baselines. The exploratory comparison with the benchmark dataset revealed variations in task valuation that may be associated with affective states. These findings demonstrate the feasibility of incorporating psychometric assessment into MCDM-based software engineering (SE) decisions while highlighting the need for further validation using larger and more diverse samples, as well as careful consideration of ethical and practical deployment challenges.
Agile philosophy emphasises flexibility, adaptability and human-centred software development. However, in medical software development, integrating regulatory compliance and safety-critical requirements within Agile methods introduces significant tension. Compliance and safety-critical are often treated as afterthoughts rather than integrated in a dynamic, Agile workflow, leading to costly rework and system failures. To address these challenges, this study employed a qualitative, multi-method research design spanning four phases. Two phases involved in-depth interviews with 30 Agile practitioners from the UK, Nigeria and India, recruited using snowball sampling. In the third phase, a novel practice-based process model was developed. To establish the model’s practice relevance and transferability, the model was evaluated with eight Agile practitioners in the UK and Nigeria in the fourth phase. To improve credibility, the model was further validated through a case study involving Nigerian practitioners managing a Canadian medical software project. Data from each phase were inductively analysed using techniques informed by grounded theory: open coding, memoing, constant comparison and theoretical saturation. The study identified 39 Agile practices used by practitioners for medical software development. The findings reveal three novel artefacts, the compliance backlog, safety clinical backlog and misuse backlog, and two novel ceremonies: internal clinical demoing and the compliance review. In addition, three specialised roles emerged: the medical scrum master, clinical tester and regulatory expert. These practices were not theorised a priori but emerged inductively from practitioners’ interviews. Practitioners reported that regulatory compliance and safety outcomes improve when regulatory and safety-critical requirements are embedded dynamically within Agile workflows rather than treated as external constraints. This research contributes a novel, empirically grounded taxonomy of Agile roles, ceremonies and artefacts that goes beyond merely proposing another process design; it presents an operationalised Agile workflow shaped by the real experiences of Agile practitioners in regulated medical software environments. Reflecting how compliance and safety activities are coordinated in practice, the study demonstrates the embedding of ’compliance and safety-by-design’ within Agile software development. We recommend using the model, as it includes novel practices for reconciling contextual influences, including weak regulatory implementation and siloed development practices.
Automated radiology report generation (RRG) aims to reduce radiologist workload while improving reporting consistency and clinical reliability. Despite its importance for diagnosis, manual reporting remains time-consuming and error-prone. Existing encoder-decoder models often struggle to align visual and textual representations, resulting in incomplete or inconsistent reports. Large language models (LLMs) provide strong linguistic fluency for automated RRG, but most existing approaches still follow a linear generation paradigm and lack explicit reasoning, self-evaluation, and structured clinical grounding. To address these limitations, we propose SMK-RRG, a self-critical multisource Knowledge framework for RRG. The proposed framework integrates structured clinical knowledge, pathology-aware visual evidence, and iterative self-refinement within a unified LLM-driven architecture. It is implemented through self-critical thought (SCT) prompting, which performs multistage candidate report generation, LLM-based self-critique, and CheXbert-guided clinical selection among candidate reports. By enabling critique-guided prompt evolution across refinement stages, SCT moves beyond conventional chain-based prompting and supports nonlinear, clinically grounded reasoning. Experiments on public chest X-ray benchmarks demonstrate that SMK-RRG produces more accurate, interpretable, and clinically consistent reports, highlighting the effectiveness of self-critique and structured information integration for reliable clinical reports.
