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Where tests fall short: empirically analyzing oracle gaps in covered code
Get PDFBackground: Developers often rely on statement coverage to assess test suite quality. However, statement coverage alone may only lead to 10 % fault detection, necessitating more rigorous approaches. While mutation testing is effective, its execution and human analysis costs remain high. Identifying covered statements that are not checked by oracles (e.g., assertions) offers a cost-effective alternative; however, the lack of empirical evidence for selecting the appropriate Oracle Gap Calculation Approach (OGCA) prevents developers from making informed choices. Aims: This knowledge-seeking study compares oracle gap characteristics determined by different OGCAs to assist developers in choosing the most valuable approach for their use cases. Method: Using mixed-method empirical analysis, we conduct an in-depth evaluation of the oracle gaps produced using three OGCAs: Checked Coverage using a Dynamic Slicer (CCDS), Checked Coverage using an Observational Slicer (CCOS), and Pseudo-Tested Statement Identification (PTSI). Across 30 Java classes from six open-source projects, we report on a quantitative evaluation of gap prominence, distribution, fault detection correlation and execution times, as well as results from a qualitative manual inspection of the statement types found in the oracle gaps. Results: The qualitative analysis showed data-loading statements, iteration statements and output updates to be most prominent in the oracle gaps. PTSI identified the oracle gaps with the lowest median mutation score (0.32), highlighting areas requiring more fault detection improvement compared to CCDS(0.76) and CCOS(0.50). PTSI also had the shortest median execution time (19.9 seconds), far quicker than both CCDS (273.2 seconds) and CCOS (5957.1 seconds). Conclusions: PTSI quickly reveals the priority testing areas for improved fault detection, making it an effective OGCA for developers to identify where tests fall short
“It was a force for good but...”: a mixed-methods evaluation of the implementation of the High in Fat, Sugar and Salt (HFSS) legislation in England.
No full textBackground: In 2022, legislation in England restricted products high in fat, sugar, or salt (HFSS) in prominent store locations. This study explores retail sector (including enforcement of legislation in retail) responses to the legislation’s implementation. Methods: Four major UK retailers – ASDA, Morrisons, Sainsbury’s and Tesco - completed business-level online surveys. Interviews were conducted with representatives from three retailers (n=13; Asda, Morrisons, Sainsbury’s), the British Retail Consortium (n=1), and primary authority enforcement (n=1) (N=15). Data were collected between July and November 2024. Findings informed co-production workshops (n=3) to develop policy recommendations. Results: Stakeholders supported legislation aims but voiced concerns about complexities with interpretation and implementation, limited communication with government, access to product nutrition data, and delays to guidance. Retailers utilised various HFSS product promotional strategies while achieving compliance. Retailers reported very limited enforcement. These findings were synthesised and facilitated the development of seven policy recommendations through co-production workshops between academics and the Institute of Grocery distribution. Conclusion: HFSS legislation represents a landmark shift in regulation of the retail food environment, but clearer, timely guidance, data provision, and transparent co-production with actors cognisant of the food sector is needed to ensure legislation can be effectively implemented, enforced and evaluated
Default, Fraud, and Fraud Classification in UK COVID-19 Loan Schemes: Evidence from One Million Guaranteed Loans
Get PDFWe study default outcomes and fraud classification in UK COVID-19 government-guaranteed lending programmes using loan-level data of 1,006,579 limited-company facilities under the Bounce Back Loan Scheme (BBLS) and Coronavirus Business Interruption Loan Scheme (CBILS). We estimate logit models of loan outcomes, Cox proportional hazard models of time-to-default, and Accelerated Failure Time (AFT) models that quantify timing effects under four parametric survival distributions. We distinguish all-cause default, insolvency-related default, and defaults that lenders classify as fraud.
Three findings stand out. First, scheme design dominates risk: BBLS loans—issued under self-certification with minimal verification—show fraud odds approximately 160 times those of CBILS recipients, with Cox hazard ratios reaching 170. AFT models under the preferred generalised gamma specification show BBLS borrowers default 37 per cent sooner than CBILS recipients, with loan-to-sales ratio being the strongest predictor of accelerated failure, compressing survival time by 65 per cent per unit increase. Second, conventional credit risk indicators maintained predictive power despite pandemic disruption: probability of default scores, loan leverage, director experience, and board composition discriminate between outcomes across logit, Cox, and AFT specifications. Director experience and board size protect against accelerated failure, with governance effects compounding over time. Third, we document systematic under-classification of fraud at challenger banks and alternative finance/fintechs. Given default and controlling for borrower characteristics, these lenders classify fraud at only 40–50 per cent of the rate at main banks. AFT timing evidence reinforces this: challenger bank defaults cluster 30 per cent earlier than main bank defaults, consistent with rapid enforcement without fraud investigation. Propensity score analysis indicates approximately 2,500 fraud cases—representing £70 million—went undetected in challenger portfolios. We identified 1,904 cases (£76m in loans) as unambiguously fraudulent based on Companies House filings—loans to companies dissolved pre-pandemic or incorporated after scheme announcement—yet lenders rarely flagged these. Main banks screened out 28 per cent of such applicants at origination and classified 14 per cent of defaults as fraud; challenger banks screened out only 22 per cent and classified just 1.4 per cent as fraud.
These findings reveal trade-offs in crisis lending design and highlight an overlooked issue in delegated guaranteed schemes: the state relies on heterogeneou
Coordinating transfers between bus and metro train in real time: A logic-based branch-and-bound method
Get PDFThe concept of Mobility-as-a-Service and on-demand public transit increasingly depends on integrating multiple transportation modes to offer seamless door-to-door travels. However, coordinating these modes in real time to address fluctuating demand and disturbances remains a major technical challenge. We tackle this issue by formulating a mixed-integer quadratic programming model to generate coordinated bus and train adjustment strategies. The model captures key factors such as traffic dynamics, passenger loads, and vehicle overtaking. To address the computational challenge of the mixed-integer property, we incorporate logic-based concepts into a branch-and-bound framework, analyzing the logical relationships among variables to improve solution efficiency. This logic-based branch-and-bound method exploits the distinct strengths of discrete and continuous components. It employs logical inference to guide the search, incorporates domain reduction to accelerate computation, and constructs reduced continuous optimization problems to efficiently update bounds and estimate logical values. Computational results demonstrate that the proposed coordinated adjustment strategy effectively improves vehicle punctuality and headway regularity, while reducing the number of stranded non-transfer and transfer passengers. The solution method demonstrates desirable computational efficiency in real-world settings, which is suitable for real-time applications
Learning-based robotic machining error prediction for high precision manufacturing
Get PDFHigh precision machining with robots is an open challenge. Achieving precision of dimensional and geometrical features with robotic machining would require compensation via feedback control which relies on accurate error prediction. Machining error prediction is a complex problem in high-precision manufacturing, where effective solutions must accurately estimate geometrical errors in different workpieces while minimizing quality inspection costs. It is also compounded by the need for real-time estimation for feedback control. This paper introduces a novel approach for predicting the quality of milled workpieces using low-cost, in-process signals and machine learning. The proposed method fuses internal machine controller commands—comprising end-effector trajectory coordinates and angular changes of six revolute joints in the robotic arm—with external laser tracker sensing signals that capture the real trajectory of the milling tool and predicts dimensional errors as would be obtained by a Coordinate Measuring Machine (CMM). To overcome the lack of knowledge of the dependence of the part dimensional error on the available signals, models with varying combinations of the sensors and the length of the time window of historical data for inclusion in the model were evaluated. In addition, five machine learning algorithms were selected, trained, evaluated and validated on data from two distinct workpieces and various spatial configurations. The best machine learning model achieved a sevenfold improvement in dimensional error prediction compared to solely using laser tracker data, with mean absolute error reduced from 0.0756 mm to 0.0097 mm. This study demonstrates the feasibility of using low-cost, in-process sensing signals to predict high-precision quality dimensional data that is normally measured by costly CMMs, enabling rapid part quality inspection and significant potential cost reduction
Nonlinear dynamics of rocking rigid blocks under stochastic base excitation: a stochastic-linearization approach
Get PDFThis paper introduces a semi-analytical framework for the nonlinear dynamics of rigid blocks rocking on nonlinear flexible foundations under stochastic base excitation. Unlike classical Housner and Winkler models that assume null or linear base-foundation interaction, the proposed approach incorporates uplift and velocity-dependent dissipation via a Hunt-Crossley-type law, augmented by a tunable cubic restoring component. The foundation is modeled as a distributed bed of nonlinear springs and dampers, combining Winkler-type linear stiffness with added nonlinearity to enable hardening or softening beyond the baseline response. Backbone amplitude dependence arises from contact geometry and is further shaped by the cubic stiffness term. The originally coupled, piecewise nonlinear rocking relations are condensed into a single governing equation using a quasi-static condensation scheme, with smooth analytical surrogates for restoring moment and damping across no-uplift and uplift regimes. A stochastic linearization scheme embedded in Lyapunov moment equations yields time-varying equivalent stiffness and damping, enabling second-order response statistics without repeated integration of highly nonlinear equations. Comparisons with Monte Carlo simulations show close agreement in rotation and rotational velocity variances across excitation intensities and foundation parameters, while achieving significant computational savings. The framework offers an efficient tool for parametric screening of rocking systems under complex soil-structure interaction
Aerosol iodine recycling is a major control on tropospheric reactive iodine abundance
Get PDFTropospheric reactive iodine influences the oxidizing capacity of the atmosphere and serves as an important source of ultra-fine particles. However, the paucity of observations of gas-phase and aerosol iodine, combined with incomplete understanding and representation of iodine chemistry in models, leads to substantial uncertainties in understanding iodine abundance, speciation, and impacts. Motivated by known gaps in previous modeling studies, we introduced speciated aerosol iodine and aerosol iodide recycling to the global chemical transport model, GEOS-Chem. Modeled aerosol iodine is speciated into fine and coarse mode soluble organic iodine (SOI), iodate, and iodide. Aerosol iodide is recycled into the gas phase via heterogeneous chemistry involving halogen nitrates and hypohalous acids to form I2, ICl, and IBr, which represents an additional source of gas-phase iodine to the atmosphere. Iodide dehalogenation doubles the tropospheric burden of reactive iodine (Iy) while reducing model-measurement bias for IO and aerosol iodine. The rate of aerosol iodine conversion to Iy is more than twice as fast as the combined rates of inorganic ocean emissions and the photolysis of organic iodine gases, suggesting that aerosols are important in mediating the abundance and lifetime of tropospheric Iy. The incorporation of SOI and iodate into the model prevents iodide dehalogenation by partitioning iodide into less reactive reservoirs, which has a stabilizing effect for reactive iodine chemistry. These findings have implications for reactive halogen abundances and global oxidant budgets in the troposphere
Conduction shape factors for thermal analysis of energy walls under varying boundary conditions
Get PDFEmbedded retaining walls in the buildings can be converted to energy walls by incorporating embedded heat exchanger pipes connected to a heat pump system, which can assist in decarbonising heating. To support the effective design of such systems, fast and reliable models are required to predict the thermal performance of energy walls. Analytical shape factors provide a convenient and computationally efficient method for estimating steady-state heat transfer rates within the wall. These shape factors are mathematical expressions that relate the temperature difference between surfaces to the resulting heat flux under steady conditions. While analytical shape factor equations have been successfully applied to other types of ground heat exchangers, their application and validations for energy walls, which have more complex thermal boundary conditions, remain unexplored. This study investigates the suitability of shape factor equations, originally developed for fuel transportation pipelines and other applications which share similar geometries, to the thermal analysis of energy walls. Given that energy walls may encounter varying thermal boundary conditions, e.g. air-exposed vs. fully embedded, both scenarios are analysed. The results present the first systematic parametric validation across realistic energy wall geometric variations (pipe spacing, diameter, wall thickness and cover depth) and thermal conductivity ratios to evaluate shape factors for practical design applications. The performance of analytical shape factors is benchmarked against results from steady-state and transient numerical models. The findings demonstrate that, with the developed methodology, existing shape factor equations can be successfully extended to energy walls and other energy geostructures exhibiting planar thermal behaviour
Teaching practices in forensic linguistics and forensic phonetics
Get PDFThe aim of this study is to provide an overview of the views and ideas that current teachers of forensic linguistics and forensic phonetics (FL-FP) have around their pedagogical practice. The study focuses on teaching practices in FL-FP within higher education institutions and explores how FL-FP teachers navigate potentially challenging issues in their work. A survey elicited opinions around FL-FP teaching from 30 teaching practitioners, focusing on areas including content warnings, mental health protection, the use of real forensic data in teaching, ethical issues, guidelines, and standards. The results indicate that the participants are generally in favor of the use of content warnings, that they believe students and staff should be provided with well-being support, that the FL-FP syllabus should refer to authentic cases, and that teaching materials should not be adapted to remove references to serious crimes. The findings could be viewed as a springboard for further discussion around how best to teach FL-FP topics to students in universities
First study of the nuclear response to fast hadrons via angular correlations between pions and slow protons in electron-nucleus scattering
Get PDFWe report on the first measurement of angular correlations between high-energy pions and slow protons in electron-nucleus (eA) scattering, providing a new probe of how a nucleus responds to a fast-moving quark. The experiment employed the CLAS detector with a 5-GeV electron beam incident on deuterium, carbon, iron, and lead targets. For heavier nuclei, the pion-proton correlation function is more spread-out in azimuth than for lighter ones, and this effect is more pronounced in the πp channel than in earlier ππ studies. The proton-to-pion yield ratio likewise rises with nuclear mass, although the increase appears to saturate for the heaviest targets. These trends are qualitatively reproduced by state-of-the-art eA event generators, including BeAGLE, eHIJING, and GiBUU, indicating that current descriptions of target fragmentation rest on sound theoretical footing. At the same time, the precision of our data exposes model-dependent discrepancies, delineating a clear path for future improvements in the treatment of cold-nuclear matter effects in eA scattering
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