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    Slice Based Fuzzing of Software
    (University of Waterloo, 2026-05-26) Murali, Aniruddhan
    Modern software systems are increasingly complex, and static analysis tools are widely used to identify potentially vulnerable code by issuing warnings. However, these warnings often require manual inspection by developers to determine whether the reported issues are genuine, making validation time-consuming and error-prone. Similarly, ensuring that individual commits (i.e., code changes) do not introduce bugs remains a fundamental challenge in software maintenance. Directed fuzzing has emerged as a powerful automated testing technique for bug detection. Yet applying directed fuzzing to entire projects for each warning or modified code location is computationally expensive, often requiring days of execution while yielding only incremental coverage improvements. We present a unified framework for bug detection based on the construction and fuzzing of compiled code slices centered on either static analysis warnings or functions modified in code commits. Unlike prior approaches that extract slices from the program entry point or impose restrictive slice-size limits, our framework constructs slices of arbitrary size and compiles them into standalone, testable units. For static analysis warning validation, we directly fuzz slices of the function containing the warning, enabling rapid elimination of false positives. We implement this approach in a tool called FuzzSlice. The key insight that we base our work on is that a warning that does not yield the bug when fuzzed at the function level in a given time budget is most likely a false positive. Evaluation on the Juliet benchmark shows that FuzzSlice detects all 864 known false positives in the ground truth. For open-source repositories, developers from tmux and openSSH independently labeled reported warnings. In these projects, FuzzSlice automatically identified 33 of 53 developer-confirmed false positives, reducing false positives by 62.26%. These results demonstrate that FuzzSlice substantially reduces manual validation effort, achieving complete elimination of false positives in Juliet and significant reductions in real-world codebases. To further strengthen static analysis warning validation, we introduce SnipTest, a framework that heuristically identifies true positives among warnings. SnipTest employs a layer-by-layer slicing strategy that incrementally expands the slice context around the target location prior to fuzzing. Unlike FuzzSlice which tests only the function containing the warning, SnipTest can incrementally grow the boundary of the code slice to include more calling context, enabling the validation of potential bugs with progressively increasing precision. We evaluate SnipTest on a benchmark comprising 97 true bugs and 97 false alarms across three real-world projects. SnipTest triggered 53 true bugs (54.6%), consistently across three slice levels, while the remaining cases were determined to be unreachable. Compared to state-of-the-art directed fuzzers, unseeded SnipTest confirms more bugs than unseeded baselines and matches the effectiveness of seeded fuzzers. Moreover, SnipTest significantly improves efficiency, achieving a 5.5–10.6× speedup in fuzzing time relative to both seeded and unseeded directed fuzzers. We further demonstrate its practical relevance by discovering three previously unknown bugs in vim and libpcap, leading to the disclosure of CVE-2025-11964. Finally, we extend our slice-based approach to commit verification. We introduce CommitGuard, a novel commit-aware differential fuzzing framework for detecting bugs introduced by code changes. Rather than fuzzing entire program versions, our method automatically identifies functions modified by the code changes and generates dedicated slices for those functions. For each function that has been modified, we create two slices: one using the updated version after the code change, and another using the older version before the code change. These slices are fuzzed independently, and their runtime behaviors are systematically compared to uncover divergences indicative of commit-induced bugs. We show the practicality of commit-level differential fuzzing by detecting five previously unknown bugs across 300 commits in widely used projects, including openSSL, libpcap, and leptonica. Additionally, CommitGuard exhibits a low false positive rate, with only 2 false positives among these 300 commits. Finally, we demonstrate that CommitGuard is efficient, requiring 32 minutes per commit, while achieving up to 75.36% code coverage of modified functions on average. Overall, we aim to show that slice-based differential fuzzing is both effective and computationally efficient, making it well-suited for integration into modern, fast-paced software development workflows, such as Continuous Integration (CI).
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    Migration, Gendered Translocality, and Rural Wellbeing in the Western Highlands of Guatemala
    (University of Waterloo, 2026-05-26) Kocsis, Emily
    Background: Rural out-migration is highly prevalent in the Western Highlands of Guatemala. In response to a confluence of factors, including economic poverty, exclusionary state policies, gang violence, and the effects of climate change, men and boys are leaving rural spaces in pursuit of opportunity elsewhere. While Guatemalan women are increasingly engaging in various forms of mobility, they are still the dominant group remaining at home while spouses, older children, and their parents emigrate. For this so-called “left behind” population, male out-migration shifts socio-spatial relations, altering livelihoods, health, and community life in complex ways. Understanding these evolving dynamics requires an attentiveness to translocality – the connections between mobile and immobile actors sustained through the flow of resources, knowledge, and ideas in migration-induced translocal networks. A growing number of studies have used the lens of translocality to examine how migration reworks household and community relations across space; however, few have integrated a gender perspective. Accordingly, this research positions gender as constitutive of everyday translocal negotiations, and in doing so, explores how gendered translocality shapes various aspects of rural wellbeing in a high out-migration context. Methods: This doctoral thesis takes a translocal perspective by considering the relational ties that exist across places and scales. Semi-structured qualitative interviews were conducted across two data collection phases during May-June 2023 and October-December 2023 in Tojchoc Grande, a community in the Western Highlands of Guatemala. Data collection included interviews with women and men representing various migrant typologies, as well as with local health providers and community leaders. This thesis employs translocal Feminist Political Ecology, systems thinking, and social resilience to explore the multiscalar dimensions of how rural wellbeing was produced across connected places. Further, analysis moves from the household to the community scale to capture how translocal relations operate across everyday practices and institutional settings. Results: Findings show that contemporary rural wellbeing in Tojchoc Grande is both deeply translocal, and gendered, with women playing a critical, but overlooked role in mediating connections between households, migrants, and community institutions. Engaging in labour, care, and community organization within this context necessarily involves negotiating entrenched gender norms that privilege male authority, decision-making, and access to resources even from abroad. Within the translocal household, male out-migration leads to partial shifts in women’s roles and responsibilities, but changes rarely translate into the feminization of agriculture. Further, gendered care roles, remittance flows, and declining trust in public services interact to produce reinforcing patterns of psychosocial and physical strain for women. At the community level, gendered translocal social relations shape who participates in institutions, who makes decisions, and how community organizations function under conditions of demographic upheaval. Conclusion: This thesis underscores the gendered character of migration-induced translocality, illustrating how those left behind confront new socio-spatial realities as they are embedded into translocal networks at various scales (e.g., individual, household, community). It reveals that in Tojchoc Grande, translocal ties modify women’s everyday negotiations within agricultural production, household health, and community organization. However, these reconfigurations tended to reproduce, and in some cases, intensify inequalities in labour, responsibilities, and access to resources, rather than evening them out. In this way, rural wellbeing was marked by constraint: everyday life is still profoundly influenced by, and responsive to patriarchal gender norms and practices that transcend place. For rural communities to adapt and build resilience in response to migration, policy and research must recognize that translocal networks are not gender neutral. They can extend gender inequalities as readily as they enable transformative social change.
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    A Surrogate Modelling Framework for Time Resolved Energy Prediction in High Performance Office Buildings
    (University of Waterloo, 2026-05-26) Akbari, Ehsan
    High-performance buildings are an important component of the transition toward low-carbon and net-zero energy systems. However, evaluating building energy performance across a wide range of design and operational conditions typically requires large numbers of detailed simulations, which can be computationally expensive. This research investigates the energy behavior of high-performance office buildings and develops a surrogate modelling framework capable of predicting time-resolved energy consumption using machine learning techniques. The study follows a four-phase methodology. In the first phase, operational data from a high-performance office building located in Waterloo, Ontario are analyzed to examine daily energy-use behavior and identify the primary operational drivers of electricity consumption. This analysis provides empirical insight that informs the selection of key parameters used in subsequent modelling stages. In the second phase, a parametric physics-based building energy model is developed in EnergyPlus to represent office buildings operating under Waterloo’s climate conditions. The model incorporates variations in building geometry, envelope properties and configurations, and internal loads to represent a range of plausible design and operational scenarios. In the third phase, the parametric model is used to generate a synthetic dataset through systematic sampling of the input parameter space. The resulting dataset contains hourly energy simulation outputs across a wide range of building configurations and serves as the training and evaluation data for surrogate models. In the fourth phase, machine learning algorithms are developed, trained, and evaluated to predict building energy performance, including hourly electricity consumption and annual performance indicators. The trained models are also used to examine the relative influence of key building parameters on predicted energy outcomes. Three surrogate architectures were evaluated: a fully connected artificial neural network (ANN), a convolutional neural network (CNN), and a hybrid ANN–CNN model. The ANN showed stronger performance for annual energy prediction but limited accuracy in reproducing hourly temporal patterns, while the CNN captured hourly variations more effectively but produced less accurate annual energy estimates. The hybrid ANN–CNN model combined these complementary strengths, achieving comparable hourly prediction accuracy to the CNN and similar annual prediction performance to the ANN. The hybrid model achieved an hourly RMSE of approximately 12.89 kWh and an annual energy prediction RMSE of 1.08 kWh/m²·yr, providing the most balanced overall performance among the evaluated architectures. However, the surrogate models showed a tendency to underestimate peak energy demand, indicating limitations in capturing short-duration peak loads.
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    Advanced Circuit and System Techniques for High-Performance Beamforming Front-Ends
    (University of Waterloo, 2026-05-26) Hazer Sahlabadi, Mehran
    The emergence of sixth-generation (6G) wireless systems has accelerated the deployment of large-scale antenna arrays to overcome high-frequency path loss while supporting multi-gigabit data rates and improved spectral efficiency. This trend imposes increasingly stringent requirements on radio-frequency (RF) front ends, including compact implementation, broadband operation, high linearity, efficient transmit operation, and low receiver noise. At millimeter-wave (mmWave) frequencies, passive and switching losses further degrade power-amplifier (PA) efficiency and receiver (RX) noise figure (NF) in time-division-duplexing (TDD) front ends. Therefore, future RF building blocks must remain compact, broadband, highly linear, and robust under dynamically varying signal and load conditions. This thesis addresses these beamforming-driven challenges through coordinated circuit and architecture-level innovations for key front-end components. First, two variable-gain phase shifter (VGPS) architectures are developed to provide compact amplitude/phase control with high accuracy, improved linearity, and wide gain-tuning range (GTR). The active unidirectional vector-sum phase shifter (VSPS), designed using a P1dB-driven load– pull methodology, achieves 0.24 dB rms gain error, 1.5◦ rms phase error, 4.1 dBm input 1-dB compression point (IP1dB), and 14 dB GTR over 35–43 GHz. The bidirectional passive VGPS further provides 20 dB GTR with sub-0.25 dB gain error and sub-1.6◦ phase error over 24–32 GHz, enabling precise beam steering and dynamic amplitude weighting for adaptive multi-beam operation. Second, a unified co-design methodology for the low-noise amplifier (LNA), PA, and RF switch is introduced to reduce transmitter efficiency degradation and receiver noise penalties in TDD front ends. A Doherty PA improves backed-off efficiency under the high peak-to-average power ratio (PAPR) of beamformed signals, while the receiver-side LNA with an embedded switch uses a synthesized coupled lumped π-model input network to provide compact quarter-wave-equivalent impedance transformation. This approach extends bandwidth, reduces area, and satisfies noise matching, impedance matching, and transmit-to-receive isolation requirements. Measurements of the 37-41 GHz prototype demonstrate 20 dBm output power with power-added efficiency (PAE) of 23%/15% at peak and 6-dB back-off, respectively. In receive mode, the prototype achieves 21 dB gain, 4.5 dB noise figure, −16 dBm input P1dB, and 32 mW DC power consumption, while maintaining 35 dB TX-to-RX isolation. The final contribution presents a transistor-based analog predistortion (APD) scheme for PA linearization with robustness to output-power and modulation-bandwidth variations. The nonlinear error-generator (EG) path is designed to track the target PA by using the same device technology, biasing condition, and circuit topology, while a closed-form formulation guides the complex weighting coefficients and explains the use of a fixed coefficient set across operating conditions. The proposed APD is validated using a 3.5-GHz class-AB gallium-nitride (GaN) PA driven by orthogonal frequency-division multiplexing (OFDM) signals with 20–150-MHz modulation bandwidths. At 100-MHz bandwidth and 24-dBm average output power, the APD improves the adjacent-channel power ratio (ACPR) from −34.6/ − 36.3 dBc to −45.6/ − 46.5 dBc and the normalized mean-square error (NMSE) from −23.4 dB to −36.2 dB. To reduce the efficiency penalty, a scaled-transistor EG with a reconfigurable directional coupler is further introduced and analyzed in terms of the linearity–efficiency trade-off. Continuous-wave simulations show that the output 1- dB compression point (OP1dB) increases from 36.5 dBm to 44.2 dBm, while the PAE at OP1dB improves from 12.2% to 31.7%. Measurements of a multi-layer printed circuit board (PCB) prototype confirm 8–10-dB ACPR improvement across 20-, 50-, and 80-MHz OFDM bandwidths using a single fixed APD setting, with NMSE reduced from 4.5–5.1% to 1.5–2.4%. At 20 MHz and 33.5-dBm average output power, the APD–PA configuration achieves 12.1% average drain efficiency, compared with 15.8% for the standalone PA, demonstrating robust analog linearization with limited efficiency penalty and without coefficient retuning. Collectively, these contributions establish a unified circuit- and system-level design framework for beamforming front ends. The proposed VGPS, T/R front-end, and APD techniques jointly address the key requirements of high peak-to-average power ratio operation, wide gain tuning range, low receiver noise, backed-off efficiency, and robust linearity, enabling compact and bandwidth-scalable RF solutions for next-generation wireless infrastructure.
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    Data-Driven Multiscale Optimization of Proton Exchange Membrane Fuel Cells: Materials, Components, and Systems
    (University of Waterloo, 2026-05-26) Madhavan, Pramoth Varsan
    The widespread deployment of proton exchange membrane (PEM) fuel cells requires the development of durable materials, optimized component architectures, and reliable system-level operation under realistic and often highly dynamic conditions. Progress across these domains is traditionally hindered by the extensive experimental effort, long testing times, and high costs associated with evaluating bipolar plate coatings, catalyst formulations, membrane electrode assembly (MEA) designs, and fuel cell behaviour under real-world automotive drive cycles. This thesis addresses these challenges by developing a multiscale, data-driven modelling framework that supports predictive insight and optimization at the materials, component, and systems levels. Through the use of machine learning, hybrid optimization algorithms, and advanced time-series models, this research aims to accelerate the design, performance assessment, and operational diagnostics of PEM fuel cells and supporting hydrogen infrastructure, contributing core elements toward future digital-twin-enabled systems. At the materials level, the thesis first investigates the corrosion resistance of metallic bipolar plates (MBPs), one of the most cost and durability critical components of PEM fuel cells. Stainless steel substrates coated with diamond-like-carbon material of varying thicknesses are experimentally evaluated using potentiodynamic polarization, electrochemical impedance spectroscopy, and surface wettability measurements. Using these datasets, extreme gradient boosting (XGB) and artificial neural network (ANN) models are developed to predict corrosion current density and impedance characteristics directly from coating thickness, electrochemical parameters, and contact angle values. The models achieved high predictive accuracy and successfully reproduced experimental trends, demonstrating that data-driven approaches can rapidly assess coating performance without requiring extensive physical testing. In parallel, the thesis addresses the optimization of oxygen reduction reaction (ORR) catalysts by developing an ANN-genetic algorithm (ANN-GA) framework capable of navigating the high-dimensional composition space of Pt-Co catalysts. Using experimental linear sweep voltammetry datasets obtained before and after accelerated stress tests, the hybrid framework identified catalyst compositions with improved catalytic activity and performance, demonstrating the potential of integrated machine learning and optimization tools to accelerate catalyst discovery. At the component level, the thesis developed a predictive and optimization framework for hybrid MEAs that combine catalyst-coated membrane (CCM) and catalyst-coated substrate (CCS) regions. This configuration has shown promise for enhancing water management, reactant transport, and overall electrochemical performance; however, determining the optimal CCM-to-CCS ratio remains a complex experimental challenge. An ANN-GA model is developed using single cell performance datasets under multiple operating conditions, including varied flow rates and backpressures. The framework accurately predicted polarization and power density curves and identified the optimal CCM_4_CCS_1 configuration (a hybrid MEA where the catalyst is distributed in a 4:1 ratio between the CCM and CCS sides), which outperformed both pure CCM and pure CCS structures. These results show that data-driven optimization can guide MEA design, reducing reliance on resource-intensive experimental screening. At the systems level, the thesis focuses on dynamic behaviour and diagnostic modelling. A long short-term memory (LSTM) network is constructed to predict transient thermal behaviour in a 50 cm2 PEM fuel cell under the new European driving cycle (NEDC). The model incorporated 72 input features, including 24 operating parameters and 48 spatial current distribution sensor readings, and generated 48 temperature predictions covering the 50 cm2 active area. Shapley additive explanations (SHAP) analysis revealed that both spatial current variations and operating conditions, such as reactant temperatures and pressures, strongly shape local thermal dynamics. The thesis also extends LSTM modelling to hydrogen infrastructure by predicting hydrogen valve outlet pressure across varied cycling conditions and temperatures (25°C, 85°C, and −40°C). The models achieved strong generalization and captured the long-term degradation signatures in valve behaviour, highlighting their suitability for predictive maintenance and operational safety. Collectively, the multiscale approaches developed in this thesis illustrate how data-driven models can provide rapid, accurate, and interpretable insights across materials, components, and system operation. Although independently developed, these methods collectively form a complementary set of predictive tools that support the broader vision of digital-twin PEM fuel cell systems. By advancing data-driven capabilities for materials screening, component optimization, and system-level diagnostics, this thesis lays essential groundwork for future platforms designed to enhance durability, enable real-time operational control, shorten development cycles, and support safe and efficient hydrogen energy technologies.