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Learnware:small models do big

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Science China(Information Sciences) 2024年第1期67卷 39-50页

作者： Zhi-Hua ZHOU Zhi-Hao TAN National key laboratory for novel software technology Nanjing University

There are complaints about current machine learning techniques such as the requirement of a huge amount of training data and proficient training skills,the difficulty of continual learning,the risk of catastrophic forgetting,and the leaking of data privacy/proprietary.Most research efforts have been focusing on one of those concerned issues separately,paying less attention to the fact that most issues are entangled in practice.The prevailing big model paradigm,which has achieved impressive results in natural language processing and computer vision applications,has not yet addressed those issues,whereas becoming a serious source of carbon emissions.This article offers an overview of the learnware paradigm,which attempts to enable users not to need to build machine learning models from scratch,with the hope of reusing small models to do things even beyond their original purposes,where the key ingredient is the specification which enables a trained model to be adequately identified to reuse according to the requirement of future users who know nothing about the model in advance.

关键词： artificial intelligence machine learning learnware

When debugging encounters artificial intelligence: state of the art and open challenges

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Science China(Information Sciences) 2024年第4期67卷 42-84页

作者： Yi SONG Xiaoyuan XIE Baowen XU School of Computer Science Wuhan University state key laboratory of novel software technology Nanjing University

Both software debugging and artificial intelligence techniques are hot topics in the current field of software engineering. Debugging techniques, which comprise fault localization and program repair, are an important part of the software development lifecycle for ensuring the quality of software systems. As the scale and complexity of software systems grow, developers intend to improve the effectiveness and efficiency of software debugging via artificial intelligence(artificial intelligence for software debugging, AI4SD). On the other hand, many artificial intelligence models are being integrated into safety-critical areas such as autonomous driving, image recognition, and audio processing, where software debugging is highly necessary and urgent(software debugging for artificial intelligence, SD4AI). An AI-enhanced debugging technique could assist in debugging AI systems more effectively, and a more robust and reliable AI approach could further guarantee and support debugging techniques. Therefore, it is important to take AI4SD and SD4AI into consideration comprehensively. In this paper, we want to show readers the path, the trend, and the potential that these two directions interact with each other. We select and review a total of 165 papers in AI4SD and SD4AI for answering three research questions, and further analyze opportunities and challenges as well as suggest future directions of this cross-cutting area.

关键词： software debugging fault localization program repair artificial intelligence machine learning

Improving actionable warning identification via the refined warning-inducing context representation

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Science China(Information Sciences) 2024年第5期67卷 321-322页

作者： Xiuting GE Chunrong FANG Xuanye LI Quanjun ZHANG Jia LIU Zhihong ZHAO Zhenyu CHEN state key laboratory for novel software technology Nanjing University

Static analysis tools(SATs) have shown potential ability in software quality assurance [1]. However, SATs report an overwhelming number of warnings, where most warnings are unactionable ones(i.e., warnings that are not acted on or fixed by developers). It is not a trivial task for developers to find actionable warnings(i.e., warnings that are acted on and fixed by developers) in all reported warnings. Hence,the excessive unactionable warnings seriously impede the usability of SATs [2].

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Robust cooperative multi-agent reinforcement learning via multi-view message certification

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Science China(Information Sciences) 2024年第4期67卷 131-145页

作者： Lei YUAN Tao JIANG Lihe LI Feng CHEN Zongzhang ZHANG Yang YU National key laboratory for novel software technology Nanjing University Polixir Technologies

Many multi-agent scenarios require message sharing among agents to promote coordination,hastening the robustness of multi-agent communication when policies are deployed in a message perturbation environment. Major relevant studies tackle this issue under specific assumptions, like a limited number of message channels would sustain perturbations, limiting the efficiency in complex scenarios. In this paper,we take a further step in addressing this issue by learning a robust cooperative multi-agent reinforcement learning via multi-view message certification, dubbed CroMAC. Agents trained under CroMAC can obtain guaranteed lower bounds on state-action values to identify and choose the optimal action under a worst-case deviation when the received messages are perturbed. Concretely, we first model multi-agent communication as a multi-view problem, where every message stands for a view of the state. Then we extract a certificated joint message representation by a multi-view variational autoencoder(MVAE) that uses a product-of-experts inference network. For the optimization phase, we do perturbations in the latent space of the state for a certificate guarantee. Then the learned joint message representation is used to approximate the certificated state representation during training. Extensive experiments in several cooperative multi-agent benchmarks validate the effectiveness of the proposed CroMAC.

关键词： multi-agent reinforcement learning robust communication adversarial training multi-view learning message certification

LAMDA-SSL:a comprehensive semi-supervised learning toolkit

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Science China(Information Sciences) 2024年第1期67卷 306-307页

作者： Lin-Han JIA Lan-Zhe GUO Zhi ZHOU Yu-Feng LI National key laboratory for novel software technology Nanjing University

Machine learning, particularly deep learning, has achieved remarkable success across a wide range of tasks. However,most of these tasks demand a substantial amount of labeled training data, which can be challenging to obtain in many real-world applications due to difficulties, costs, or time constraints associated with labeling. In this context,semi-supervised learning (SSL) has emerged as a promising paradigm to alleviate the dependency on large labeled datasets by harnessing the power of unlabeled data [1–3].

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Understanding and Detecting Inefficient Image Displaying Issues in Android Apps

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Journal of Computer Science & technology 2024年第2期39卷 434-459页

作者：李文杰马骏蒋炎岩许畅马晓星 state key laboratory of novel software technology Nanjing UniversityNanjing 210023China Department of Computer Science and technology Nanjing UniversityNanjing 210023China

Mobile applications(apps for short)often need to display images.However,inefficient image displaying(IID)issues are pervasive in mobile apps,and can severely impact app performance and user experience.This paper first establishes a descriptive framework for the image displaying procedures of IID issues.Based on the descriptive framework,we conduct an empirical study of 216 real-world IID issues collected from 243 popular open-source Android apps to validate the presence and severity of IID issues,and then shed light on these issues’characteristics to support research on effective issue detection.With the findings of this study,we propose a static IID issue detection tool TAPIR and evaluate it with 243 real-world Android apps.Encouragingly,49 and 64 previously-unknown IID issues in two different versions of 16 apps reported by TAPIR are manually confirmed as true positives,respectively,and 16 previously-unknown IID issues reported by TAPIR have been confirmed by developers and 13 have been fixed.Then,we further evaluate the performance impact of these detected IID issues and the performance improvement if they are fixed.The results demonstrate that the IID issues detected by TAPIR indeed cause significant performance degradation,which further show the effectiveness and efficiency of TAPIR.

关键词： Android application(app) inefficient image displaying(IID) performance empirical study static analysis

Learning in games:a systematic review

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Science China(Information Sciences) 2024年第7期67卷 133-159页

作者： Rong-Jun QIN Yang YU National key laboratory for novel software technology Nanjing University Polixir Technologies

Game theory studies the mathematical models for self-interested individuals. Nash equilibrium is arguably the most central solution in game theory. While finding the Nash equilibrium in general is known as polynomial parity arguments on directed graphs(PPAD)-complete, learning in games provides an alternative to approximate Nash equilibrium, which iteratively updates the player's strategy through interactions with other players. Rules and models have been developed for learning in games, such as fictitious play and no-regret learning. Particularly, with recent advances in online learning and deep reinforcement learning,techniques from these fields greatly boost the breakthroughs in learning in games from theory to application.As a result, we have witnessed many superhuman game AI systems. The techniques used in these systems evolve from conventional search and learning to purely reinforcement learning(RL)-style learning methods,gradually getting rid of the domain knowledge. In this article, we systematically review the above techniques,discuss the trend of basic learning rules towards a unified framework, and recap applications in large games.Finally, we discuss some future directions and make the prospect of future game AI systems. We hope this article will give some insights into designing novel approaches.

关键词： non-cooperative games learning in games no-regret learning reinforcement learning superhuman AI

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 363-383页

作者： Liang Chen Jingbo Zhang Linjie Wu Xingjuan Cai Yubin Xu Shanxi key laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan030024China School of state key laboratory of novel software technology Nanjing UniversityNanjing210008China

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

关键词： Decision variable grouping large-scale multi-objective optimization algorithms weighted overlapping grouping direction-guided evolution

ASTSDL:predicting the functionality of incomplete programming code via an AST-sequence-based deep learning model

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Science China(Information Sciences) 2024年第1期67卷 86-104页

作者： Yaoshen YU Zhiqiu HUANG Guohua SHEN Weiwei LI Yichao SHAO College of Computer Science and technology Nanjing University of Aeronautics and Astronautics Collaborative Innovation Center of novel software technology and Industrialization key laboratory of Safety-Critical software Ministry of Industry and Information Technology

Code recommendation systems have been widely used in helping developers implement unfamiliar programming tasks.Many existing code recommenders or code search engines can retrieve relevant code rapidly with high accuracy,however,they cannot recommend any code outside similar ones.We propose an approach to predict the functionality of incomplete programming code by using syntactical information,and providing a list of potential functionalities to guess what the developers want,in order to increase the diversity of recommendations.In this paper,we propose a deep learning model called ASTSDL,which uses a sequencebased representation of source code to predict functionality.We extract syntactical information from the abstract syntax tree(AST) of the source code,apply a deep learning model to capture the syntactic and sequential information,and predict the functionality of the source code fragments.The experimental results demonstrate that ASTSDL can effectively predict the functionality of incomplete code with an accuracy above 84% in the top-10 list,even if there is only half of the complete code.

关键词： functionality prediction incomplete programming code syntactical information code representation model deep learning algorithm