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Moshood Fakorede

Publications and source records attributed to Moshood Fakorede.

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Understanding Bugs in Quantum Simulators: An Empirical Study

Quantum simulators are a foundational component of the quantum software ecosystem. They are widely used to develop and debug quantum programs, validate compiler transformations, and support empirical claims about correctness and performance. In the absence of large-scale quantum hardware, simulator outputs are often treated as ground truth for algorithm development and system evaluation. However, quantum simulators also introduce unique implementation challenges. They must faithfully emulate quantum behavior while executing on classical hardware, requiring complex representations of quantum state evolution, operator composition, and noise modeling. Yet, we still lack a large-scale and in-depth study of failures in quantum simulators. To bridge this gap, this work presents a comprehensive empirical study of bugs in widely used open-source quantum simulators. We analyze 394 confirmed bugs from 12 simulators and manually categorize them based on root causes, failure manifestations, affected components, and discovery mechanisms. Our study reveals several key findings. First, bug discovery is largely user-driven, with most crashes, exceptions, and resource-related failures not detected by automated testing and identified after deployment. Second, logical correctness failures are widespread and often silent, producing plausible but incorrect outputs without triggering crashes or explicit error signals. Third, many critical failures originate in classical simulator infrastructure, such as memory management, indexing, configuration, and dependency compatibility, rather than in core quantum execution logic. These findings provide new insights into the reliability challenges of quantum simulators and highlight opportunities to improve testing and validation practices in the quantum software ecosystem.

quant-ph

Understanding and Detecting Platform-Specific Violations in Android Auto Apps

Despite over 3.5 million Android apps and 200+ million Android Auto-compatible vehicles, only a few hundred apps support Android Auto due to platform-specific compliance requirements. Android Auto mandates service-based architectures in which the vehicle system invokes app callbacks to render the UI and handle interactions, which is fundamentally different from standard Activity-based Android development. Through an empirical study analysis of 98 issues across 14 Android Auto app repositories, we identified three major compliance failure categories: media playback errors, UI rendering issues, and voice command integration failures in line with mandatory requirements for integrating Android Auto support. We introduce AutoComply, a static analysis framework capable of detecting these compliance violations through the specialized analysis of platform-specific requirements. AutoComply constructs a Car-Control Flow Graph (CCFG) extending traditional control flow analysis to model the service-based architecture of Android Auto apps. Evaluating AutoComply on 31 large-scale open-source apps, it detected 27 violations (13X more than Android Lint), while no false positives were observed, achieving 2X faster analysis. Developers have acknowledged 14 of these violations with 8 fixes already implemented, validating AutoComply's practical effectiveness.

cs.SE