Recent studies have revealed that self-sustaining cascading failures in distributed systems frequently lead to widespread outages, which are challenging to contain and recover from. Existing failure detection techniques struggle to expose such failures prior to deployment, as they typically require a complex combination of specific conditions to be triggered. This challenge stems from the inherent nature of cascading failures, as they typically involve a sequence of fault propagations, each activated by distinct conditions.

Web development involves turning UI designs into functional webpages, which can be difficult for both beginners and experienced developers due to the complexity of HTML's hierarchical structures and styles.While Large Language Models (LLMs) have shown promise in generating source code, two major challenges persist in UI-to-HTML code generation: (1) effectively representing HTML's hierarchical structure for LLMs, and (2) bridging the gap between the visual nature of UI designs and the text-based format of HTML code.To tackle these challenges, we introduce WAFFLE, a new fine-tuning strategy that uses a structure-aware attention mechanism to improve LLMs' understanding of HTML's structure and a contrastive fine-tuning approach to align LLMs' understanding of UI images and HTML code.Models fine-tuned with WAFFLE show up to 9.00 pp (absolute percentage point) higher HTML match, 0.0982 higher CW-SSIM, 32.99 higher CLIP, and 27.12 pp higher LLEM on our new benchmark WebSight-Test and an existing benchmark Design2Code, outperforming current fine-tuning methods.

A major threat to distributed software systems' reliability is vicious cycles, which are observed when an event in the distributed software system's execution causes a system degradation, and the degradation, in turn, causes more of such events. Vicious cycles often result in large-scale cloud outages that are hard to recover from due to their self-reinforcing nature. This paper formally defines Vicious Cycle, and conducts the first in-depth study of 33 real-world vicious cycles in 13 widely-used open-source distributed software systems, shedding light on the root causes, triggering conditions, and fixing strategies of vicious cycles, with over a dozen concrete implications to combat them. Our findings show that the majority of the vicious cycles are caused by incorrect error handlers, where the handlers do not obtain enough information to distinguish between 1) an error induced by incoming requests and 2) an error induced by an unexpected interference from another error handler. This paper further performs a feasibility study by 1) building a monitoring tool that prevents one type of vicious cycle by collecting information to make a more informed decision in error handling, and 2) investigating the effectiveness of one commonly suggested practice-injecting exponential backoff-to prevent vicious cycles induced by unconstrained retry.

Testing deep learning (DL) software is crucial and challenging. Recent approaches use differential testing to cross-check pairs of implementations of the same functionality across different libraries. Such approaches require two DL libraries implementing the same functionality, which is often unavailable. In addition, they rely on a high-level library, Keras, that implements missing functionality in all supported DL libraries, which is prohibitively expensive and thus no longer maintained.

Deep learning (DL) systems have been gaining popularity in critical tasks such as credit evaluation and crime prediction. Such systems demand fairness. Recent work shows that DL software implementations introduce variance: identical DL training runs (i.e., identical network, data, configuration, software, and hardware) with a fixed seed produce different models. Such variance could make DL models and networks violate fairness compliance laws, resulting in negative social impact. In this paper, we conduct the first empirical study to quantify the impact of software implementation on the fairness and its variance of DL systems. Our study of 22 mitigation techniques and five baselines reveals up to 12.6% fairness variance across identical training runs with identical seeds. In addition, most debiasing algorithms have a negative impact on the model such as reducing model accuracy, increasing fairness variance, or increasing accuracy variance. Our literature survey shows that while fairness is gaining popularity in artificial intelligence (AI) related conferences, only 34.4% of the papers use multiple identical training runs to evaluate their approach, raising concerns about their results’ validity. We call for better fairness evaluation and testing protocols to improve fairness and fairness variance of DL systems as well as DL research validity and reproducibility at large.

Deep learning (DL) training algorithms utilize nondeterminism to improve models' accuracy and training efficiency. Hence, multiple identical training runs (e.g., identical training data, algorithm, and network) produce different models with different accuracies and training times. In addition to these algorithmic factors, DL libraries (e.g., TensorFlow and cuDNN) introduce additional variance (referred to as implementation-level variance) due to parallelism, optimization, and floating-point computation.