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Effective file system testing relies on coverage to detect bugs and enhance reliability. We analyzed real file system bugs and found a weak correlation between code coverage, the most commonly used metric, and test effectiveness; many bugs were in covered code but remained undetected. Our study also showed that covering diverse file system inputs and outputs—system call arguments and return values—can be key to detecting the majority of observed bugs. We present input coverage and output coverage as new metrics for evaluating and improving file system testing, and have developed the IOCov framework for computing these metrics. Unlike existing system call tracers, IOCov computes coverage using only the calls relevant to testing, excluding unrelated ones that should not be counted. To demonstrate IOCov’s utility, we used it to extend the existing testing tool CrashMonkey into CM-IOCov, which achieves broader input coverage and more thorough detection of crash consistency bugs. Our experimental evaluation shows that IOCov com- putes input and output coverage accurately with minimal overhead. IOCov is applicable to different types of file system testing and can provide insights for improvement as well as identify untested cases based on coverage results. Moreover, the bugs found exclusively by CM-IOCov are 2.1 and 12.9 times more than those found exclusively by CrashMonkey on the 6.12 and 5.6 kernels, respectively, demonstrating the effectiveness of the IOCov-based coverage approach. 

Initialization profoundly affects evolutionary algorithm (EA) efficacy by dictating search trajectories and convergence. This study introduces a hybrid initialization strategy combining empty-space search algorithm (ESA) and opposition-based learning (OBL). OBL initially generates a diverse population, subsequently augmented by ESA, which identifies under-explored regions. This synergy enhances population diversity, accelerates convergence, and improves EA performance on complex, high-dimensional optimization problems. Benchmark results demonstrate the proposed method's superiority in solution quality and convergence speed compared to conventional initialization techniques. 

Identifying knee and elbow points in performance curves is a critical task in various domains, including machine learning and system design. These points represent optimal trade-offs between cost and performance, facilitating efficient decision-making and resource allocation. However, accurately determining the knees and elbows in curves poses a significant challenge. To address this challenge, we introduce Kneeliverse, an open-source library dedicated to knee/elbow point detection. Kneeliverse incorporates a suite of well-established knee-detection algorithms, including Menger, L-method, Kneedle, and DFDT. Additionally, Kneeliverse extends these algorithms to detect multiple knees and elbows in complex curves, employing a recursive approach. Kneeliverse further includes Z-Method, a recently developed algorithm specifically designed for multi-knee detection. 

We present a comprehensive pipeline, integrated with a visual analytics system called GapMiner, capable of exploring and exploiting untapped opportunities within the empty regions of high-dimensional datasets. Our approach utilizes a novel Empty-Space Search Algorithm (ESA) to identify the center points of these uncharted voids, which represent reservoirs for potentially valuable new configurations. Initially, this process is guided by user interactions through GapMiner, which visualizes Empty-Space Configurations (ESCs) within the context of the dataset and allows domain experts to explore and refine ESCs for subsequent validation in domain experiments or simulations. These activities iteratively enhance the dataset and contribute to training a connected deep neural network (DNN). As training progresses, the DNN gradually assumes the role of identifying and validating high-potential ESCs, reducing the need for direct user involvement. Once the DNN achieves sufficient accuracy, it autonomously guides the exploration of optimal configurations by predicting performance and refining configurations through a combination of gradient ascent and improved empty-space searches. Domain experts were actively involved throughout the system’s development. Our findings demonstrate that this methodology consistently generates superior novel configurations compared to conventional randomization-based approaches. We illustrate its effectiveness in multiple case studies with diverse objectives. 

We study the problem of Open-Vocabulary Constructs (OVCs)—ones not known beforehand—in the context of converting natural language (NL) specifications into formal languages (e.g., temporal logic or code). Mod- els fare poorly on OVCs due to a lack of necessary knowledge a priori. In such situations, a domain expert can provide correct constructs at in- ference time based on their preferences or domain knowledge. Our goal is to effectively reuse this inference-time, expert-provided knowledge for future parses without retraining the model. We present dynamic knowledge- augmented parsing (DKAP), where in addition to the input sentence, the model receives (dynamically growing) expert knowledge as a key-value lexicon that associates NL phrases with correct OVC constructs. We pro- pose ROLEX, a retrieval-augmented parsing approach that uses this lexicon. A retriever and a generator are trained to find and use the key-value store to produce the correct parse. A key challenge lies in curating data for this retrieval-augmented parser. We utilize synthetic data generation and the data augmentation techniques on annotated (NL sentence, FL statement) pairs to train the augmented parser. To improve training effectiveness, we propose multiple strategies to teach models to focus on the relevant subset of retrieved knowledge. Finally, we introduce a new evaluation paradigm modeled after the DKAP problem and simulate the scenario across three formalization tasks (NL2LTL, NL2Code, and NL2CMD). Our evaluations show that DKAP is a difficult challenge, and ROLEX helps improve the performance of baseline models by using dynamic expert knowledge effectively. 

Sustainability has become a critical focus area across the technology industry, most notably in cloud data centers. In such shared-use computing environments, there is a need to account for the power consumption of individual users. Prior work on power prediction of individual user jobs in shared environments has often focused on workloads that stress a single resource, such as CPU or DRAM. These works typically employ a specific machine learning (ML) model to train and test on the target workload for high accuracy. However, modern workloads in data centers can stress multiple resources simultaneously, and cannot be assumed to always be available for training. This paper empirically evaluates the performance of various ML models under different model settings and training data assumptions for the per-job power prediction problem using a range of workloads. Our evaluation results provide key insights into the efficacy of different ML models. For example, we find that linear ML models suffer from poor prediction accuracy (as much as 25% prediction error), especially for unseen workloads. Conversely, non-linear models, specifically XGBoost and xRandom Forest, provide reasonable accuracy (7–9% error). We also find that data-normalization and the power-prediction model formulation affect the accuracy of individual ML models in different ways. 

Archival systems are often tasked with storing highly valuable data that may be targeted by malicious actors. When the lifetime of the secret data is on the order of decades to centuries, the threat of improved cryptanalysis casts doubt on the long-term security of cryptographic techniques, which rely on hardness assumptions that are hard to prove over archival time scales. This threat makes the design of secure archival systems exceptionally difficult. Some archival systems turn a blind eye to this issue, hoping that current cryptographic techniques will not be broken; others often use techniques--—such as secret sharing—that are impractical at scale. This position paper sheds light on the core challenges behind building practically viable secure long-term archives; we identify promising research avenues towards this goal.