arXiv subjects

Yawen Wang

Publications and source records attributed to Yawen Wang.

26 records · Page 2Linked to original sources

arXiv2024

Understanding Individual Agent Importance in Multi-Agent System via Counterfactual Reasoning

Explaining multi-agent systems (MAS) is urgent as these systems become increasingly prevalent in various applications. Previous work has proveided explanations for the actions or states of agents, yet falls short in understanding the black-boxed agent's importance within a MAS and the overall team strategy. To bridge this gap, we propose EMAI, a novel agent-level explanation approach that evaluates the individual agent's importance. Inspired by counterfactual reasoning, a larger change in reward caused by the randomized action of agent indicates its higher importance. We model it as a MARL problem to capture interactions across agents. Utilizing counterfactual reasoning, EMAI learns the masking agents to identify important agents. Specifically, we define the optimization function to minimize the reward difference before and after action randomization and introduce sparsity constraints to encourage the exploration of more action randomization of agents during training. The experimental results in seven multi-agent tasks demonstratee that EMAI achieves higher fidelity in explanations than baselines and provides more effective guidance in practical applications concerning understanding policies, launching attacks, and patching policies.

cs.AI↗

arXiv2024

A Roadmap for Software Testing in Open Collaborative Development Environments

Amidst the ever-expanding digital sphere, the evolution of the Internet has not only fostered an atmosphere of information transparency and sharing but has also sparked a revolution in software development practices. The distributed nature of open collaborative development, along with its diverse contributors and rapid iterations, presents new challenges for ensuring software quality. This paper offers a comprehensive review and analysis of recent advancements in software quality assurance within open collaborative development environments. Our examination covers various aspects, including process management, personnel dynamics, and technological advancements, providing valuable insights into effective approaches for maintaining software quality in such collaborative settings. Furthermore, we delve into the challenges and opportunities arising from emerging technologies such as LLMs and the AI model-centric development paradigm. By addressing these topics, our study contributes to a deeper understanding of software quality assurance in open collaborative environments and lays the groundwork for future exploration and innovation.

cs.SE↗

arXiv2024

Seeing is Believing: Vision-driven Non-crash Functional Bug Detection for Mobile Apps

Mobile app GUI (Graphical User Interface) pages now contain rich visual information, with the visual semantics of each page helping users understand the application logic. However, these complex visual and functional logic present new challenges to software testing. Existing automated GUI testing methods, constrained by the lack of reliable testing oracles, are limited to detecting crash bugs with obvious abnormal signals. Consequently, many non-crash functional bugs, ranging from unexpected behaviors to logical errors, often evade detection by current techniques. While these non-crash functional bugs can exhibit visual cues that serve as potential testing oracles, they often entail a sequence of screenshots, and detecting them necessitates an understanding of the operational logic among GUI page transitions, which is challenging traditional techniques. Considering the remarkable performance of Multimodal Large Language Models (MLLM) in visual and language understanding, this paper proposes Trident, a novel vision-driven, multi-agent collaborative automated GUI testing approach for detecting non-crash functional bugs. It comprises three agents: Explorer, Monitor, and Detector, to guide the exploration, oversee the testing progress, and spot issues. We also address several challenges, i.e., align visual and textual information for MLLM input, achieve functionality-oriented exploration, and infer test oracles for non-crash bugs, to enhance the performance of functionality bug detection. We evaluate Trident on 590 non-crash bugs and compare it with 12 baselines, it can achieve more than 14%-112% and 108%-147% boost in average recall and precision compared with the best baseline. The ablation study further proves the contribution of each module. Moreover, Trident identifies 43 new bugs on Google Play, of which 31 have been fixed.

cs.SE↗

arXiv2024

CardBench: A Benchmark for Learned Cardinality Estimation in Relational Databases

Cardinality estimation is crucial for enabling high query performance in relational databases. Recently learned cardinality estimation models have been proposed to improve accuracy but there is no systematic benchmark or datasets which allows researchers to evaluate the progress made by new learned approaches and even systematically develop new learned approaches. In this paper, we are releasing a benchmark, containing thousands of queries over 20 distinct real-world databases for learned cardinality estimation. In contrast to other initial benchmarks, our benchmark is much more diverse and can be used for training and testing learned models systematically. Using this benchmark, we explored whether learned cardinality estimation can be transferred to an unseen dataset in a zero-shot manner. We trained GNN-based and transformer-based models to study the problem in three setups: 1-) instance-based, 2-) zero-shot, and 3-) fine-tuned. Our results show that while we get promising results for zero-shot cardinality estimation on simple single table queries; as soon as we add joins, the accuracy drops. However, we show that with fine-tuning, we can still utilize pre-trained models for cardinality estimation, significantly reducing training overheads compared to instance specific models. We are open sourcing our scripts to collect statistics, generate queries and training datasets to foster more extensive research, also from the ML community on the important problem of cardinality estimation and in particular improve on recent directions such as pre-trained cardinality estimation.

cs.DB↗

arXiv2023

CrashTranslator: Automatically Reproducing Mobile Application Crashes Directly from Stack Trace

Crash reports are vital for software maintenance since they allow the developers to be informed of the problems encountered in the mobile application. Before fixing, developers need to reproduce the crash, which is an extremely time-consuming and tedious task. Existing studies conducted the automatic crash reproduction with the natural language described reproducing steps. Yet we find a non-neglectable portion of crash reports only contain the stack trace when the crash occurs. Such stack-trace-only crashes merely reveal the last GUI page when the crash occurs, and lack step-by-step guidance. Developers tend to spend more effort in understanding the problem and reproducing the crash, and existing techniques cannot work on this, thus calling for a greater need for automatic support. This paper proposes an approach named CrashTranslator to automatically reproduce mobile application crashes directly from the stack trace. It accomplishes this by leveraging a pre-trained Large Language Model to predict the exploration steps for triggering the crash, and designing a reinforcement learning based technique to mitigate the inaccurate prediction and guide the search holistically. We evaluate CrashTranslator on 75 crash reports involving 58 popular Android apps, and it successfully reproduces 61.3% of the crashes, outperforming the state-of-the-art baselines by 109% to 206%. Besides, the average reproducing time is 68.7 seconds, outperforming the baselines by 302% to 1611%. We also evaluate the usefulness of CrashTranslator with promising results.

cs.SE↗

arXiv2023

SatIn: Hardware for Boolean Satisfiability Inference

This paper describes SatIn, a hardware accelerator for determining boolean satisfiability (SAT) -- an important problem in many domains including verification, security analysis, and planning. SatIn is based on a distributed associative array which performs short, atomic operations that can be composed into high level operations. To overcome scaling limitations imposed by wire delay, we extended the algorithms used in software solvers to function efficiently on a distributed set of nodes communicating with message passing. A cycle-level simulation on real benchmarks shows that SatIn achieves an average 72x speedup against Glucose, the winner of 2016 SAT competition, with the potential to achieve a 113x speedup using two contexts. To quantify SatIn's physical requirements, we placed and routed a single clause using the Synopsys 32nm} educational development kit. We were able to meet a 1ns cycle constraint with our target clause fitting in 4867um^2 and consuming 63.8uW of dynamic power; with a network, this corresponds to 100k clauses consuming 8.3W of dynamic power (not including leakage or global clock power) in a 500mm^2 32nm chip.

cs.AR↗

arXiv2022

Where is Your App Frustrating Users?

User reviews of mobile apps provide a communication channel for developers to perceive user satisfaction. Many app features that users have problems with are usually expressed by key phrases such as "upload pictures", which could be buried in the review texts. The lack of fine-grained view about problematic features could obscure the developers' understanding of where the app is frustrating users, and postpone the improvement of the apps. Existing pattern-based approaches to extract target phrases suffer from low accuracy due to insufficient semantic understanding of the reviews, thus can only summarize the high-level topics/aspects of the reviews. This paper proposes a semantic-aware, fine-grained app review analysis approach (SIRA) to extract, cluster, and visualize the problematic features of apps. The main component of SIRA is a novel BERT+Attr-CRF model for fine-grained problematic feature extraction, which combines textual descriptions and review attributes to better model the semantics of reviews and boost the performance of the traditional BERT-CRF model. SIRA also clusters the extracted phrases based on their semantic relations and presents a visualization of the summaries. Our evaluation on 3,426 reviews from six apps confirms the effectiveness of SIRA in problematic feature extraction and clustering. We further conduct an empirical study with SIRA on 318,534 reviews of 18 popular apps to explore its potential application and examine its usefulness in real-world practice.

cs.SE↗

arXiv2022

SOL: Safe On-Node Learning in Cloud Platforms

Cloud platforms run many software agents on each server node. These agents manage all aspects of node operation, and in some cases frequently collect data and make decisions. Unfortunately, their behavior is typically based on pre-defined static heuristics or offline analysis; they do not leverage on-node machine learning (ML). In this paper, we first characterize the spectrum of node agents in Azure, and identify the classes of agents that are most likely to benefit from on-node ML. We then propose SOL, an extensible framework for designing ML-based agents that are safe and robust to the range of failure conditions that occur in production. SOL provides a simple API to agent developers and manages the scheduling and running of the agent-specific functions they write. We illustrate the use of SOL by implementing three ML-based agents that manage CPU cores, node power, and memory placement. Our experiments show that (1) ML substantially improves our agents, and (2) SOL ensures that agents operate safely under a variety of failure conditions. We conclude that ML-based agents show significant potential and that SOL can help build them.

cs.OS↗