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    How Does Experience Influence Developer Perceptions of Atoms of Confusion?
    (Software REBELs, 2026) Shi, Guoshuai; Kazemi, Farshad; McIntosh, Shane; Godfrey, Michael W.
    Atoms of Confusion (AoCs) are small, syntactically valid code patterns that can increase cognitive load during program comprehension. Earlier research suggested that AoCs are common and potentially harmful, but more recent studies have questioned whether their effects generalize beyond less experienced developers. This confirmatory study aims to reexamine whether the presence of AoCs slows comprehension or alters repair preferences. Moreover, we examine whether these effects are moderated by developers’ programming experience. We investigate task completion time and the kind of repairs developers prefer when interacting with code containing AoCs. We propose a two-phase study consisting of a pre-screening questionnaire and a controlled experiment. The questionnaire will function as a qualification instrument. In the experiment, participants will complete eight Java comprehension tasks, four with an AoC and four without. For each task, developers are asked to identify a seeded defect and to rank three functionally equivalent repairs differing in AoC inclusion. Task completion time and the top-ranked repair will be analyzed using mixed-effects linear and multinomial regression models, with AoC presence as the manipulated factor and programming experience as a covariate.
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    In Silico Multi-Scale Investigation of Lung Tissue Mechanics and Injury
    (University of Waterloo, 2026-02-25) Singh, Dilaver
    Biofidelity in lung tissue mechanical response and injury prediction is a critical aspect of human body modeling, since the lungs are one of the life-sustaining organs and thus present a high priority injury and fatality risk. Human body models (HBMs) are becoming integral to safety systems design across a range of industries including automotive, defense, and sports. The lungs in particular present numerous challenges to continuum scale HBMs due to their high mechanical compliance, complex heterogeneous structure, and the transient nature of respiration. Existing continuum-scale lung models in impact HBMs are limited in that they typically use properties from excised lung tissue samples, which do not consider tensile pre-strains in the alveolar walls from lung inflation to functional residual capacity (FRC) which is the nominal in vivo conditions of the lung. Furthermore, the effects of surface tension forces, which are an important aspect of lung response, have not been characterized in the existing literature for the deviatoric deformations relevant to HBMs. Additionally, existing methods for predicting pulmonary contusion (PC) are limited to using reconstructed simulations of impact scenarios, and determining empirical correlations to various response metrics in the lung (such as strain, strain rate, etc.). Consequently, the resulting injury criteria or metrics are limited in their applicability to other models or boundary conditions, and are not mechanistically linked to any injury pathology, nor are linked with the alveolar microstructure where actual lung injury occurs. The focus of the current research was to address these limitations using a multi-scale lung modeling approach to relate the macroscopic continuum scale lung response to the microstructural features of the alveoli, and that could be implemented in contemporary HBMs. The specific objectives of this work were: (O1) to develop an alveolar scale model of lung parenchyma, and to use that model to (O2) inform a continuum scale model of lung tissue, and (O3) inform a pulmonary contusion injury prediction method. A finite element model of a representative volume of lung parenchyma was developed using a generalized tetrakaidecahedron geometry to represent an alveolar cluster. The alveolar cluster model used periodic boundary constraints to model symmetry conditions on each face, and used pressure-driven and displacement-driven boundary conditions to simulate the mechanical response of the alveolar wall network. The material properties of the alveolar wall were determined from experimentally measured stress-stretch curves of excised lung tissue, and pressure-volume curves of saline-filled whole lungs that do not include surface tension forces. An explicit implementation of the surface tension membrane was included in the model, derived from experimental data of pulmonary surfactant surface tension forces, as well as from pressure-volume curves of air-filled whole lungs. The cluster model was used to simulate the macroscopic response of lung parenchyma, and predicted that both the surface tension membrane, and alveolar pre-strains at the functional residual capacity (FRC) lung volume, stiffened the macroscopic response of the cluster. The cluster model was also used to characterize the alveolar wall strains as a function of macroscopic deformation, to determine relations between continuum-scale deformations and alveolar strains at the microstructural scale. The macroscopic response of lung parenchyma predicted by the alveolar cluster model in uniaxial tension/compression and shear, was used to determine stress-stretch properties of a continuum scale lung model that included surface tension membrane forces and alveolar pre-strains at FRC, denoted as the FRC Lung model. An Ogden hyperelastic model was used to capture the deviatoric response, and the bulk properties were derived from an analysis using the rule of mixtures for porous materials. The stress-stretch response predicted by the model without alveolar pre-strains was in general agreement with the experimental data on excised lung tissue from the literature. The FRC Lung model was validated using available experimental data that directly loaded the lungs including an impact experiment by Yen et al. (1988) where the model demonstrated good agreement. The alveolar wall strain predictions and the surface area change predictions of the cluster model were validated against available data on lungs in the physiological range of motion (i.e. volume changes from respiration). Importantly, the model demonstrated that reported alveolar injury thresholds from overdistension, also generally corresponded to the physiological limits of alveolar strain. The alveolar wall strain predictions of the cluster model were used to develop injury thresholds based on alveolar overdistension, by determining thresholds of macroscopic (continuum scale) deviatoric deformation that resulted in alveolar strains that exceeded the physiological limits. The resulting continuum scale strain thresholds were assessed in full-scale HBM simulations of thoracic pendulum impacts, and resulted in contusion predictions that agreed well with expected outcomes, and also matched or outperformed existing calibrated methods. The developed model demonstrated that existing data on excised lung parenchyma, excised alveolar wall, pulmonary surfactant surface tension, whole-lung pressure-volume with saline, and whole-lung pressure-volume with air, were all in general agreement when interpreted with the alveolar-scale model. The multi-scale modeling approach for lung tissue undertaken herein, successfully related microstructural features and injury thresholds at the alveolar scale, to macroscopic lung response and an injury prediction method for input into a continuum scale human body model. Future work can extend the methods presented here to investigate additional features of lung tissue, or to other biological tissues.
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    On the Performance of Pippenger's algorithm for Multi-Scalar Multiplication
    (University of Waterloo, 2026-02-25) Oleksandr, Hrabar
    Multi-scalar multiplication (MSM) is core to many zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) systems and remains a primary performance bottleneck in proof generation and verification. In practice, zk-SNARK implementations typically rely on Pippenger’s bucket method as the standard algorithm for MSM. This thesis targets high-throughput MSM, computing many scalar–point multiplications and summing them for a large number of scalars, up to $2^{21}$ on a specific curve, with the primary goal of reducing the number of group operations. Our work consists of three main parts. First, we review background and classical MSM algorithms, with more focus on standard Pippenger's method, Pippenger's with Booth encoded digits and the Bos–Coster approach. Second, we apply a family of small-factor scalar recoding techniques that extract small factors to shorten the effective scalar bitlength, and we compare the number of point operations for the afore-mentioned methods. Third, we introduce a strategy to reduce the number of point operations in Pippenger's algorithm. It is based on clustering of scalar window digits across multiple rows. Firstly, we describe the simplest case of window digit clustering in individual columns and then its generalization to window digit clustering in multiple columns. We implement these ideas in Rust, study sensitivity to window size, and evaluate combinations of recoding and reuse. Across our benchmarks, multiple columns window digit clustering reduces total point operations by \(2.59\%\)–\(18.09\%\) relative to Booth encoded Pippenger's method, while individual column clustering yields improvements up to $8 \%$ for small number of scalars. Overall, structured reuse together with modest window tuning provides consistent operation savings, indicating a practical path to faster MSMs at scale.
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    Posture and Attention
    (University of Waterloo, 2026-02-25) Caron, Emilie
    Recent research found that posture (sitting versus standing) influences performance on cognitive paradigms including the Stroop task, task-switching, and visual search (Rosenbaum et al., 2017;2018; Smith et al., 2019). The proposed mechanism suggests standing increases ‘load’, ultimately enhancing selective attention (Rosenbaum et al., 2017). Yet, early findings were ambiguous and the theory underspecified. This dissertation presents an account of systematic replications that test the robustness of the postural effect on attention, as well as includes a computational investigation into the mechanism underlying this theoretical account. Chapter 2 assessed the reliability of Rosenbaum and colleagues’ original postural effect. Chapter 3 directly replicates Rosenbaum and colleagues’ and Smith and colleagues’ Stroop experiments. Chapter 4 investigated the influence of posture on task-switching (switch-costs) and visual search (search-rates) via replications of Smith and colleagues’ paradigms. Chapter 5 applied Rosenbaum and colleagues’ theory to a computational model of attention in the Stroop task, exploring how ‘load’ and cognitive resources influence performance. The empirical experiments (Chapters 2-4) failed to produce robust postural interactions, suggesting no meaningful posture effect on Stroop, task-switching and visual search performance. Model simulations (Chapter 5) partially align with Rosenbaum and colleagues’ findings but revealed a speed-accuracy trade-off; increased load produced faster but less accurate responding rather than enhanced attention. This dissertation provides converging evidence that postural influences on cognition are not as robust as initially reported. The results underscore the importance of replication and cross-disciplinary research in establishing reliable effects and suggest caution in accepting claims without rigorous verification.
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    Does Impact Analysis Support the Review of Changes to Build Specifications?
    (Software REBELs, 2026) Nejati, Mahtab; Alfadel, Mahmoud; McIntosh, Shane
    Build systems require maintenance. Since automated quality assurance rarely targets build specifications, peer review remains the key mechanism for detecting faults; yet, previous research reports that reviews of build changes are hindered by limited tool support and the scarcity of developers with build expertise. Moreover, build changes often introduce cascading effects that propagate beyond the modified files. Such effects make it difficult for reviewers to understand the consequences of a change to build specifications, particularly without dedicated tooling. In this paper, we propose a controlled experiment to examine whether access to Build Change Impact Analysis (BCIA) enhances developer understanding and assessment of changes to build specifications. BCIA summarizes how changes propagate through build specifications via data-and control-flow relationships. We conduct this study using BuiScout, a prototype implementation of BCIA for CMake-based build systems. Using a within-subjects experimental design, participants will perform comprehension and reasoning tasks with and without access to BuiScout. These tasks will evaluate their ability to assess change impact and reason about its implications. We will measure and compare response accuracy, explanation quality, and task completion time, along with self-reported estimates of confidence and perceived difficulty. Together, these measurements will allow us to study whether BCIA (implemented in BuiScout) enhances the effectiveness and efficiency of reviewing changes to build specifications.