Because mobile apps are extremely popular and often mission critical nowadays, companies invest a great deal of resources in testing the apps they provide to their customers. Testing is particularly important for Android apps, which must run on a multitude of devices and operating system versions. Unfortunately, as we confirmed in many interviews with quality assurance professionals, app testing is today a very human intensive, and therefore tedious and error prone, activity. To address this problem, and better support testing of Android apps, we propose a new technique that allows testers to easily create platform independent test scripts for an app and automatically run the generated test scripts on multiple devices and operating system versions. The technique does so without modifying the app under test or the runtime system, by (1) intercepting the interactions of the tester with the app and (2) providing the tester with an intuitive way to specify expected results that it then encode as test oracles. We implemented our technique in a tool named Barista and used the tool to evaluate the practical usefulness and applicability of our approach. Our results show that Barista (1) can faithfully encode user defined test cases as test scripts with built-in oracles that can run on multiple platforms and (2) outperforms two popular tools with similar functionality. Barista and our experimental infrastructure are publicly available.

The dairy industry faces significant challenges in data integration and analysis, which are critical for informed decision-making, operational optimization, and sustainability. Data integration—combining data from diverse sources, such as herd management systems, sensors, and diagnostics—remains difficult due to the lack of standardization, infrastructure barriers, and proprietary concerns. This commentary explores these issues based on insights from a multidisciplinary group of stakeholders, including industry experts, researchers, and practitioners. Key challenges discussed include the absence of a national animal identification system in the US, high IT resource costs, reluctance to share data due to competitive disadvantages, and differences in global data handling practices. Proposed pathways forward include developing comprehensive data integration guidelines, enhancing farmer awareness through training programs, and fostering collaboration across industry, academia, and technology providers. Additional recommendations involve improving data exchange standards, addressing interoperability issues, and leveraging advanced technologies, such as artificial intelligence and cloud computing. Emphasis is placed on localized data integration solutions for farm-level benefits and broader research applications to advance sustainability, traceability, and profitability within the dairy supply chain. These outcomes provide a foundation for achieving streamlined data systems, enabling actionable insights, and fostering innovation in the dairy industry.

Fault localization has been one of the most manual and costly software debugging activities. The spectrum-based fault localization is the most studied and evaluated fault localization approach. Mutation-based fault localization is a promising approach but with a high computational cost. We propose a novel mutation execution strategy named Failed-Test Oriented Mutant Execution Strategy (FTMES) for improving the efficacy of fault localization techniques and also reduce the computational cost of these techniques. Our proposed approach and eight other baselines were evaluated against 221 real faults. The results show that FTMES outperformed others with respect to efficiency (computational cost) while maintaining similar accuracy.

Summary: The Dairy Victory Platform (DVP) addresses the critical gap in accessible, qualified economic information for dairy farms. It leverages monthly test-day data from Dairy Herd Improvement-processing centers and commodity market prices. By considering the geographic locations of farms, the DVP calculates vital decisionmaking key performance indicators (KPI) such as feed efficiency (FE), milk income over feed cost (IOFC), and net income margin. This enables zootechnical and economic benchmarking of farms. Designed for dairy farmers seeking to automate performance measurement and analysis, the DVP offers potential insights for economic enhancement, moving beyond stressful and often imprecise manual data analysis. The platform's architecture is robust and sophisticated, encompassing data integration, benchmarking, analytics, simulation, and recommendation modules.

The lactation curve is essential for developing effective feeding plans, optimizing breeding, and strategizing milk production for dairy farms. However, health disorders, as well as external factors such as heat stress, dietary changes, and certain management practices can cause perturbations (temporary drops in milk yield) that shift the fitted lactation curve downward, making it difficult to accurately estimate the potential lactation ability of dairy cows. This study aims to evaluate the applicability of unsupervised machine learning techniques for detecting outliers in daily milk yield data and estimating the expected lactation curve in the absence of perturbations, referred to as the unperturbed lactation curve (ULC). Using the Wood model as the baseline lactation curve, we compared ULC derived from 3 unsupervised machine learning models (UMLM), specifically one-class support vector machines, isolation forest, and local outlier factor, with those from 2 previously proposed models: the perturbed lactation model (PLM) and the iterative Wood model (IWM). We first conducted a simulation study using 1,000 simulated lactations over a 305-d period, each including 1 to 15 perturbations (mean ± SD: 4.00 ± 1.46), to assess perturbation detection performance. Across all UMLM, sensitivities (∼61%), precisions (∼82%), and their harmonic means (F₁ scores, ∼70%) did not differ significantly. The UMLM outperformed the baseline Wood model in sensitivity (51.5%) and F₁ score (64.2%) while maintaining comparable precision (83.8%). Their F₁ scores also exceeded those of the PLM (53.2%) and IWM (66.8%), indicating more balanced curve adjustment and improved perturbation detection. We then applied the models to observed daily milk yield data from 2,831 lactation records of 1,636 Holstein cows collected over a 10-year period at the University of Wisconsin-Madison Agricultural Research Station. The comparison focused on the goodness-of-fit of ULC, computational efficiency, curve shape, and the validity of identified perturbations. The UMLM demonstrated relatively high computational efficiency in establishing the ULC, and these ULC showed better goodness-of-fit and shapes more consistent with the baseline Wood curve than the PLM and IWM. The upward shifts in the ULC from the UMLM were more conservative than those from the IWM and PLM, yet seemed reasonable based on previous reports on the impact of health disorders on milk yield. Additionally, these upward shifts by the UMLM may help identify potential perturbations that went undetected with the baseline Wood curve. In contrast, the PLM and IWM showed limitations in detecting potential perturbations, especially during early lactation. These findings suggest that unsupervised machine learning techniques can effectively detect potential outliers in daily milk yield data and adequately estimate the expected lactation curve in the absence of perturbations. However, the generalizability of the findings may be limited by the use of data from only Holstein cows at a single farm and the absence of health, environmental, and management records. Moreover, the current UMLM do not account for fixed effects (e.g., breed, parity, calving season) or long-term impacts of health disorders, which may hinder accurate lactation curve modeling. Future studies should consider incorporating more flexible modeling approaches and multifarm datasets with detailed background records.