Code generation, the automatic creation of source code from natural language descriptions, has garnered significant attention due to its potential to streamline software development. Inspired by research that links task-personality alignment with improved development outcomes, we conduct an empirical study on personality-guided code generation using large language models (LLMs). Specifically, we investigate how emulating personality traits appropriate to the coding tasks affects LLM performance. We extensively evaluate this approach using seven widely adopted LLMs across four representative datasets. Our results show that personality guidance significantly enhances code generation accuracy, with improved pass rates in 23 out of 28 LLM-dataset combinations. Notably, in 11 cases, the improvement exceeds 5%, and in 5 instances, it surpasses 10%, with the highest gain reaching 12.9%. Additionally, personality guidance can be easily integrated with other prompting strategies to further boost performance.

Recent advancements in integrating large language models (LLMs) with application programming interfaces (APIs) have gained significant interest in both academia and industry. These API-based agents, leveraging the strong autonomy and planning capabilities of LLMs, can efficiently solve problems requiring multi-step actions. However, their ability to handle multi-dimensional difficulty levels, diverse task types, and real-world demands through APIs remains unknown. In this paper, we introduce \textsc{ShortcutsBench}, a large-scale benchmark for the comprehensive evaluation of API-based agents in solving tasks with varying levels of difficulty, diverse task types, and real-world demands. \textsc{ShortcutsBench} includes a wealth of real APIs from Apple Inc.'s operating systems, refined user queries from shortcuts, human-annotated high-quality action sequences from shortcut developers, and accurate parameter filling values about primitive parameter types, enum parameter types, outputs from previous actions, and parameters that need to request necessary information from the system or user. Our extensive evaluation of agents built with $5$ leading open-source (size >= 57B) and $4$ closed-source LLMs (e.g. Gemini-1.5-Pro and GPT-3.5) reveals significant limitations in handling complex queries related to API selection, parameter filling, and requesting necessary information from systems and users. These findings highlight the challenges that API-based agents face in effectively fulfilling real and complex user queries. All datasets, code, and experimental results will be available at \url{https://github.com/eachsheep/shortcutsbench}.

Conventional automated test generation tools struggle to generate test oracles and tricky bug-revealing test inputs. Large Language Models (LLMs) can be prompted to produce test inputs and oracles for a program directly, but the precision of the tests can be very low for complex scenarios (only 6.3% based on our experiments). To fill this gap, this paper proposes AID, which combines LLMs with differential testing to generate fault-revealing test inputs and oracles targeting plausibly correct programs (i.e., programs that have passed all the existing tests). In particular, AID selects test inputs that yield diverse outputs on a set of program variants generated by LLMs, then constructs the test oracle based on the outputs. We evaluate AID on two large-scale datasets with tricky bugs: TrickyBugs and EvalPlus, and compare it with three state-of-the-art baselines. The evaluation results show that the recall, precision, and F1 score of AID outperform the state-of-the-art by up to 1.80x, 2.65x, and 1.66x, respectively.

We consider the problem of approximating a general Gaussian location mixture by finite mixtures. The minimum order of finite mixtures that achieve a prescribed accuracy (measured by various $f$-divergences) is determined within constant factors for the family of mixing distributions with compactly support or appropriate assumptions on the tail probability including subgaussian and subexponential. While the upper bound is achieved using the technique of local moment matching, the lower bound is established by relating the best approximation error to the low-rank approximation of certain trigonometric moment matrices, followed by a refined spectral analysis of their minimum eigenvalue. In the case of Gaussian mixing distributions, this result corrects a previous lower bound in [Allerton Conference 48 (2010) 620-628].

Deep Learning (DL) is increasingly being integrated into Web applications through a method known as "in-browser inference", where the DL processes occur directly within Web browsers. However, the actual performance of this method and its effect on user experience quality (QoE) is not well-understood. This gap in knowledge necessitates new forms of QoE measurement, going beyond traditional metrics such as page load time. To address this, we conducted the first extensive performance evaluation of in-browser inference. We introduced new metrics for this purpose: responsiveness, smoothness, and inference accuracy. Our thorough study included 9 widely-used DL models and tested them across 50 popular PC Web browsers. The findings show a significant latency issue with in-browser inference: it's on average 16.9 times slower on CPU and 4.9 times slower on GPU than native inference methods. Several factors contribute to this latency, including underused hardware instruction sets, inherent delays in the runtime environment, resource competition within the browser, and inefficiencies in software libraries and GPU abstractions. Moreover, in-browser inference demands a lot of memory, sometimes up to 334.6 times more than the size of the DL models themselves. This excessive memory usage is partly due to suboptimal memory management. Additionally, we noticed that in-browser inference increases the time it takes for graphical user interface (GUI) components to load in web browsers by a significant 67.2\%, which severely impacts the overall QoE for users of web applications that depend on this technology.