arXiv subjects

Junjie Wang

Publications and source records attributed to Junjie Wang.

At least 217 records · Page 12Linked to original sources

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

EALM: Introducing Multidimensional Ethical Alignment in Conversational Information Retrieval

Artificial intelligence (AI) technologies should adhere to human norms to better serve our society and avoid disseminating harmful or misleading information, particularly in Conversational Information Retrieval (CIR). Previous work, including approaches and datasets, has not always been successful or sufficiently robust in taking human norms into consideration. To this end, we introduce a workflow that integrates ethical alignment, with an initial ethical judgment stage for efficient data screening. To address the need for ethical judgment in CIR, we present the QA-ETHICS dataset, adapted from the ETHICS benchmark, which serves as an evaluation tool by unifying scenarios and label meanings. However, each scenario only considers one ethical concept. Therefore, we introduce the MP-ETHICS dataset to evaluate a scenario under multiple ethical concepts, such as justice and Deontology. In addition, we suggest a new approach that achieves top performance in both binary and multi-label ethical judgment tasks. Our research provides a practical method for introducing ethical alignment into the CIR workflow. The data and code are available at https://github.com/wanng-ide/ealm .

cs.CL↗

arXiv2023

Why are thermal images blurry

The resolution of optical imaging is limited by diffraction as well as detector noise. However, thermal imaging exhibits an additional unique phenomenon of ghosting which results in blurry and low-texture images. Here, we provide a detailed view of thermal physics-driven texture and explain why it vanishes in thermal images capturing heat radiation. We show that spectral resolution in thermal imagery can help recover this texture, and we provide algorithms to recover texture close to the ground truth. Using a simulator for complex 3D scenes, we discuss the interplay of geometric textures and non-uniform temperatures which is common in real-world thermal imaging. We demonstrate the failure of traditional thermal imaging to recover ground truth in multiple scenarios while our thermal perception approach successfully recovers geometric textures. Finally, we put forth an experimentally feasible infrared Bayer-filter approach to achieve thermal perception in pitch darkness as vivid as optical imagery in broad daylight.

physics.optics↗

arXiv2023

Multi-agent Coordination Under Temporal Logic Tasks and Team-Wise Intermittent Communication

Multi-agent systems outperform single agent in complex collaborative tasks. However, in large-scale scenarios, ensuring timely information exchange during decentralized task execution remains a challenge. This work presents an online decentralized coordination scheme for multi-agent systems under complex local tasks and intermittent communication constraints. Unlike existing strategies that enforce all-time or intermittent connectivity, our approach allows agents to join or leave communication networks at aperiodic intervals, as deemed optimal by their online task execution. This scheme concurrently determines local plans and refines the communication strategy, i.e., where and when to communicate as a team. A decentralized potential game is modeled among agents, for which a Nash equilibrium is generated iteratively through online local search. It guarantees local task completion and intermittent communication constraints. Extensive numerical simulations are conducted against several strong baselines.

cs.MA↗

arXiv2023

Tightly-bound and room-temperature-stable excitons in van der Waals degenerate-semiconductor Bi4O4SeCl2 with high charge-carrier density

Excitons, which represent a type of quasi-particles consisting of electron-hole pairs bound by the mutual Coulomb interaction, were often observed in lowly-doped semiconductors or insulators. However, realizing excitons in the semiconductors or insulators with high charge carrier densities is a challenging task. Here, we perform infrared spectroscopy, electrical transport, ab initio calculation, and angle-resolved-photoemission spectroscopy studies of a van der Waals degenerate-semiconductor Bi4O4SeCl2. A peak-like feature (i.e., alpha peak) is present around ~ 125 meV in the optical conductivity spectra at low temperature T = 8 K and room temperature. After being excluded from the optical excitations of free carriers, interband transitions, localized states and polarons, the alpha peak is assigned as the exciton absorption. Moreover, assuming the existence of weakly-bound excitons--Wannier-type excitons in this material violates the Lyddane-Sachs-Teller relation. Besides, the exciton binding energy of ~ 375 meV, which is about an order of magnitude larger than those of conventional semiconductors, and the charge-carrier concentration of ~ 1.25 * 10^19 cm^-3, which is higher than the Mott density, further indicate that the excitons in this highly-doped system should be tightly bound. Our results pave the way for developing the optoelectronic devices based on the tightly-bound and room-temperature-stable excitons in highly-doped van der Waals degenerate semiconductors.

cond-mat.mtrl-sci↗

arXiv2023

A First Look at Fairness of Machine Learning Based Code Reviewer Recommendation

The fairness of machine learning (ML) approaches is critical to the reliability of modern artificial intelligence systems. Despite extensive study on this topic, the fairness of ML models in the software engineering (SE) domain has not been well explored yet. As a result, many ML-powered software systems, particularly those utilized in the software engineering community, continue to be prone to fairness issues. Taking one of the typical SE tasks, i.e., code reviewer recommendation, as a subject, this paper conducts the first study toward investigating the issue of fairness of ML applications in the SE domain. Our empirical study demonstrates that current state-of-the-art ML-based code reviewer recommendation techniques exhibit unfairness and discriminating behaviors. Specifically, male reviewers get on average 7.25% more recommendations than female code reviewers compared to their distribution in the reviewer set. This paper also discusses the reasons why the studied ML-based code reviewer recommendation systems are unfair and provides solutions to mitigate the unfairness. Our study further indicates that the existing mitigation methods can enhance fairness by 100% in projects with a similar distribution of protected and privileged groups, but their effectiveness in improving fairness on imbalanced or skewed data is limited. Eventually, we suggest a solution to overcome the drawbacks of existing mitigation techniques and tackle bias in datasets that are imbalanced or skewed.

cs.SE↗

arXiv2023

MAP: Multimodal Uncertainty-Aware Vision-Language Pre-training Model

Multimodal semantic understanding often has to deal with uncertainty, which means the obtained messages tend to refer to multiple targets. Such uncertainty is problematic for our interpretation, including inter- and intra-modal uncertainty. Little effort has studied the modeling of this uncertainty, particularly in pre-training on unlabeled datasets and fine-tuning in task-specific downstream datasets. In this paper, we project the representations of all modalities as probabilistic distributions via a Probability Distribution Encoder (PDE) by utilizing sequence-level interactions. Compared to the existing deterministic methods, such uncertainty modeling can convey richer multimodal semantic information and more complex relationships. Furthermore, we integrate uncertainty modeling with popular pre-training frameworks and propose suitable pre-training tasks: Distribution-based Vision-Language Contrastive learning (D-VLC), Distribution-based Masked Language Modeling (D-MLM), and Distribution-based Image-Text Matching (D-ITM). The fine-tuned models are applied to challenging downstream tasks, including image-text retrieval, visual question answering, visual reasoning, and visual entailment, and achieve state-of-the-art results.

cs.CV↗

arXiv2023

Automatic Static Bug Detection for Machine Learning Libraries: Are We There Yet?

Automatic detection of software bugs is a critical task in software security. Many static tools that can help detect bugs have been proposed. While these static bug detectors are mainly evaluated on general software projects call into question their practical effectiveness and usefulness for machine learning libraries. In this paper, we address this question by analyzing five popular and widely used static bug detectors, i.e., Flawfinder, RATS, Cppcheck, Facebook Infer, and Clang static analyzer on a curated dataset of software bugs gathered from four popular machine learning libraries including Mlpack, MXNet, PyTorch, and TensorFlow with a total of 410 known bugs. Our research provides a categorization of these tools' capabilities to better understand the strengths and weaknesses of the tools for detecting software bugs in machine learning libraries. Overall, our study shows that static bug detectors find a negligible amount of all bugs accounting for 6/410 bugs (0.01%), Flawfinder and RATS are the most effective static checker for finding software bugs in machine learning libraries. Based on our observations, we further identify and discuss opportunities to make the tools more effective and practical.

cs.SE↗

arXiv2023

Automatic Unit Test Generation for Deep Learning Frameworks based on API Knowledge

Many automatic unit test generation tools that can generate unit test cases with high coverage over a program have been proposed. However, most of these tools are ineffective on deep learning (DL) frameworks due to the fact that many of deep learning APIs expect inputs that follow specific API knowledge. To fill this gap, we propose MUTester to generate unit test cases for APIs of deep learning frameworks by leveraging the API constraints mined from the corresponding API documentation and the API usage patterns mined from code fragments in Stack Overflow (SO). Particularly, we first propose a set of 18 rules for mining API constraints from the API documents. We then use the frequent itemset mining technique to mine the API usage patterns from a large corpus of machine learning API related code fragments collected from SO. Finally, we use the above two types of API knowledge to guide the test generation of existing test generators for deep learning frameworks. To evaluate the performance of MUTester, we first collect 1,971 APIs from four widely-used deep learning frameworks (i.e., Scikit-learn, PyTorch, TensorFlow, and CNTK) and for each API, we further extract its API knowledge, i.e., API constraints and API usage. Given an API, MUTester combines its API knowledge with existing test generators (e.g., search-based test generator PyEvosuite and random test generator PyRandoop) to generate test cases to test the API. Results of our experiment show that MUTester can significantly improve the corresponding test generation methods and the improvement in code coverage is 15.7% to 27.0% on average. In addition, it can help reduce around 19.0% of invalid tests generated by the existing test generators. Our user study with 16 developers further demonstrates the practicality of MUTester in generating test cases for deep learning frameworks.

cs.SE↗

arXiv2023

A Survey on Automated Software Vulnerability Detection Using Machine Learning and Deep Learning

Software vulnerability detection is critical in software security because it identifies potential bugs in software systems, enabling immediate remediation and mitigation measures to be implemented before they may be exploited. Automatic vulnerability identification is important because it can evaluate large codebases more efficiently than manual code auditing. Many Machine Learning (ML) and Deep Learning (DL) based models for detecting vulnerabilities in source code have been presented in recent years. However, a survey that summarises, classifies, and analyses the application of ML/DL models for vulnerability detection is missing. It may be difficult to discover gaps in existing research and potential for future improvement without a comprehensive survey. This could result in essential areas of research being overlooked or under-represented, leading to a skewed understanding of the state of the art in vulnerability detection. This work address that gap by presenting a systematic survey to characterize various features of ML/DL-based source code level software vulnerability detection approaches via five primary research questions (RQs). Specifically, our RQ1 examines the trend of publications that leverage ML/DL for vulnerability detection, including the evolution of research and the distribution of publication venues. RQ2 describes vulnerability datasets used by existing ML/DL-based models, including their sources, types, and representations, as well as analyses of the embedding techniques used by these approaches. RQ3 explores the model architectures and design assumptions of ML/DL-based vulnerability detection approaches. RQ4 summarises the type and frequency of vulnerabilities that are covered by existing studies. Lastly, RQ5 presents a list of current challenges to be researched and an outline of a potential research roadmap that highlights crucial opportunities for future work.

cs.SE↗

arXiv2023

A spectral radius condition for a graph to have $(a,b)$-parity factors

Let $a,b$ be two positive integers such that $a \le b$ and $a \equiv b$ (mod $2$). We say that a graph $G$ has an $(a,b)$-parity factor if $G$ has a spanning subgraph $F$ such that $d_{F}(v) \equiv b$ (mod $2$) and $a \le d_{F}(v) \le b$ for all $v \in V (G)$. In this paper, we provide a tight spectral radius condition for a graph to have $(a,b)$-parity factors.

math.CO↗

arXiv2023

UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective

We propose a new paradigm for universal information extraction (IE) that is compatible with any schema format and applicable to a list of IE tasks, such as named entity recognition, relation extraction, event extraction and sentiment analysis. Our approach converts the text-based IE tasks as the token-pair problem, which uniformly disassembles all extraction targets into joint span detection, classification and association problems with a unified extractive framework, namely UniEX. UniEX can synchronously encode schema-based prompt and textual information, and collaboratively learn the generalized knowledge from pre-defined information using the auto-encoder language models. We develop a traffine attention mechanism to integrate heterogeneous factors including tasks, labels and inside tokens, and obtain the extraction target via a scoring matrix. Experiment results show that UniEX can outperform generative universal IE models in terms of performance and inference-speed on $14$ benchmarks IE datasets with the supervised setting. The state-of-the-art performance in low-resource scenarios also verifies the transferability and effectiveness of UniEX.

cs.CL↗

arXiv2023

Chatting with GPT-3 for Zero-Shot Human-Like Mobile Automated GUI Testing

Mobile apps are indispensable for people's daily life, and automated GUI (Graphical User Interface) testing is widely used for app quality assurance. There is a growing interest in using learning-based techniques for automated GUI testing which aims at generating human-like actions and interactions. However, the limitations such as low testing coverage, weak generalization, and heavy reliance on training data, make an urgent need for a more effective approach to generate human-like actions to thoroughly test mobile apps. Inspired by the success of the Large Language Model (LLM), e.g., GPT-3 and ChatGPT, in natural language understanding and question answering, we formulate the mobile GUI testing problem as a Q&A task. We propose GPTDroid, asking LLM to chat with the mobile apps by passing the GUI page information to LLM to elicit testing scripts, and executing them to keep passing the app feedback to LLM, iterating the whole process. Within it, we extract the static context of the GUI page and the dynamic context of the iterative testing process, design prompts for inputting this information to LLM, and develop a neural matching network to decode the LLM's output into actionable steps to execute the app. We evaluate GPTDroid on 86 apps from Google Play, and its activity coverage is 71%, with 32% higher than the best baseline, and can detect 36% more bugs with faster speed than the best baseline. GPTDroid also detects 48 new bugs on the Google Play with 25 of them being confirmed/fixed. We further summarize the capabilities of GPTDroid behind the superior performance, including semantic text input, compound action, long meaningful test trace, and test case prioritization.

cs.SE↗

arXiv2023

The Good, the Bad, and the Missing: Neural Code Generation for Machine Learning Tasks

Machine learning (ML) has been increasingly used in a variety of domains, while solving ML programming tasks poses unique challenges because of the fundamentally different nature and construction from general programming tasks, especially for developers who do not have ML backgrounds. Automatic code generation that produces a code snippet from a natural language description can be a promising technique to accelerate ML programming tasks. In recent years, although many deep learning-based neural code generation models have been proposed with high accuracy, the fact that most of them are mainly evaluated on general programming tasks calls into question their effectiveness and usefulness in ML programming tasks. In this paper, we set out to investigate the effectiveness of existing neural code generation models on ML programming tasks. For our analysis, we select six state-of-the-art neural code generation models, and evaluate their performance on four widely used ML libraries, with newly-created 83K pairs of natural-language described ML programming tasks. Our empirical study reveals some good, bad, and missing aspects of neural code generation models on ML tasks, with a few major ones listed below. (Good) Neural code generation models perform significantly better on ML tasks than on non-ML tasks. (Bad) Most of the generated code is semantically incorrect. (Bad) Code generation models cannot significantly improve developers' completion time. (Good) The generated code can help developers write more correct code by providing developers with clues for using correct APIs. (Missing) The observation from our user study reveals the missing aspects of code generation for ML tasks, e.g., decomposing code generation for divide-and-conquer into two tasks: API sequence identification and API usage generation.

cs.SE↗

arXiv2023

Spectral condition for spanning $k$-ended trees in $t$-connected graphs

For any integer $k\geq 2$, a spanning $k$-ended tree is a spanning tree with at most $k$ leaves. In this paper, we provide a tight spectral radius condition for the existence of a spanning $k$-ended tree in $t$-connected graphs, which generalizes a result of Ao, Liu and Yuan (2023).

math.CO↗

arXiv2023

NER-to-MRC: Named-Entity Recognition Completely Solving as Machine Reading Comprehension

Named-entity recognition (NER) detects texts with predefined semantic labels and is an essential building block for natural language processing (NLP). Notably, recent NER research focuses on utilizing massive extra data, including pre-training corpora and incorporating search engines. However, these methods suffer from high costs associated with data collection and pre-training, and additional training process of the retrieved data from search engines. To address the above challenges, we completely frame NER as a machine reading comprehension (MRC) problem, called NER-to-MRC, by leveraging MRC with its ability to exploit existing data efficiently. Several prior works have been dedicated to employing MRC-based solutions for tackling the NER problem, several challenges persist: i) the reliance on manually designed prompts; ii) the limited MRC approaches to data reconstruction, which fails to achieve performance on par with methods utilizing extensive additional data. Thus, our NER-to-MRC conversion consists of two components: i) transform the NER task into a form suitable for the model to solve with MRC in a efficient manner; ii) apply the MRC reasoning strategy to the model. We experiment on 6 benchmark datasets from three domains and achieve state-of-the-art performance without external data, up to 11.24% improvement on the WNUT-16 dataset.

cs.CL↗

arXiv2023

Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence

Nowadays, foundation models become one of fundamental infrastructures in artificial intelligence, paving ways to the general intelligence. However, the reality presents two urgent challenges: existing foundation models are dominated by the English-language community; users are often given limited resources and thus cannot always use foundation models. To support the development of the Chinese-language community, we introduce an open-source project, called Fengshenbang, which leads by the research center for Cognitive Computing and Natural Language (CCNL). Our project has comprehensive capabilities, including large pre-trained models, user-friendly APIs, benchmarks, datasets, and others. We wrap all these in three sub-projects: the Fengshenbang Model, the Fengshen Framework, and the Fengshen Benchmark. An open-source roadmap, Fengshenbang, aims to re-evaluate the open-source community of Chinese pre-trained large-scale models, prompting the development of the entire Chinese large-scale model community. We also want to build a user-centered open-source ecosystem to allow individuals to access the desired models to match their computing resources. Furthermore, we invite companies, colleges, and research institutions to collaborate with us to build the large-scale open-source model-based ecosystem. We hope that this project will be the foundation of Chinese cognitive intelligence.

cs.CL↗

arXiv2023

Giant atom induced zero modes and localization in the nonreciprocal Su-Schrieffer-Heeger chain

A notable feature of non-Hermitian systems with skin effects is the sensitivity of their spectra and eigenstates to the boundary conditions. In the literature, three types of boundary conditions-periodic boundary condition,open boundary condition and a defect in the system as a boundary, are explored. In this work we introduce the other type of boundary condition provided by a giant atom. The giant atom couples to a nonreciprocal SuSchrieffer-Heeger chain at two points and plays the role of defects. We study the spectrum and localization of eigenstates of the system and find that the giant atom can induce asymmetric zero modes. A remarkable feature is that bulk states might localize at the left or the right chain-atom coupling sites in weak localization regimes. This bipolar localization leads to Bloch-like states, even though translational invariance is broken. Moreover, we find that the localization is obviously weaker than the case with two small atoms or open boundary conditions even in strong coupling regimes. These intriguing results indicate that nonlocal coupling of giant atom to a nonreciprocal SSH chain weakens localization of the eigenstates. We also show that the Lyapunov exponent in the long-time dynamics in real space can act as a witness of the localized bulk states.

quant-ph↗