STRATA: unified framework for task assignments in large teams of heterogeneous agents

Ravichandar, Harish; Shaw, Kathryn; Chernova, Sonia

doi:10.1007/s10458-020-09461-y

Cited by 45 publications

(34 citation statements)

References 31 publications

(83 reference statements)

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“…Yefet et al [47] generated adversairal example based on gradient for CODE2VEC. Ramakrishnan et al [33] and Ravichandar et al [34] both performed adversarial attacks and adversarial training on CODE2SEQ. They all concentrate on method name prediction instead of generating long comment that help programmers understand.…”

Section: Adversarial Examples Generation Adversarial Examples Were Irst Proposed By Szegedy Et Al In Image Classiicationmentioning

confidence: 99%

Adversarial Robustness of Deep Code Comment Generation

Zhou

Zhang

Shen

et al. 2022

ACM Trans. Softw. Eng. Methodol.

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Deep neural networks (DNNs) have shown remarkable performance in a variety of domains such as computer vision, speech recognition, and natural language processing. Recently they also have been applied to various software engineering tasks, typically involving processing source code. DNNs are well-known to be vulnerable to adversarial examples, i.e., fabricated inputs that could lead to various misbehaviors of the DNN model while being perceived as benign by humans. In this paper, we focus on the code comment generation task in software engineering and study the robustness issue of the DNNs when they are applied to this task. We propose ACCENT ( A dversarial C ode C omment g EN era T or), an identifier substitution approach to craft adversarial code snippets, which are syntactically correct and semantically close to the original code snippet, but may mislead the DNNs to produce completely irrelevant code comments. In order to improve the robustness, ACCENT also incorporates a novel training method, which can be applied to existing code comment generation models. We conduct comprehensive experiments to evaluate our approach by attacking the mainstream encoder-decoder architectures on two large-scale publicly available datasets. The results show that ACCENT efficiently produces stable attacks with functionality-preserving adversarial examples, and the generated examples have better transferability compared with the baselines. We also confirm, via experiments, the effectiveness in improving model robustness with our training method.

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Section: Adversarial Examples Generation Adversarial Examples Were Irst Proposed By Szegedy Et Al In Image Classiicationmentioning

confidence: 99%

Adversarial Robustness of Deep Code Comment Generation

Zhou

Zhang

Shen

et al. 2022

ACM Trans. Softw. Eng. Methodol.

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“…We consider a heterogeneous team of N robots that must collectively execute a set of tasks. Each robot is defined by its abilities or traits (Neville et al, 2021;Prorok et al, 2017;Ravichandar et al, 2020), which are modeled as continuous variables, encoded by the trait vector…”

Section: Trait-based Time-extended Task Allocationmentioning

confidence: 99%

GRSTAPS: Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling

Messing

Neville

Chernova

et al. 2021

The International Journal of Robotics Research

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Effective deployment of multi-robot teams requires solving several interdependent problems at varying levels of abstraction. Specifically, heterogeneous multi-robot systems must answer four important questions: what (task planning), how (motion planning), who (task allocation), and when (scheduling). Although there are rich bodies of work dedicated to various combinations of these questions, a fully integrated treatment of all four questions lies beyond the scope of the current literature, which lacks even a formal description of the complete problem. In this article, we address this absence, first by formalizing this class of multi-robot problems under the banner Simultaneous Task Allocation and Planning with Spatiotemporal Constraints (STAP-STC), and then by proposing a solution that we call Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling (GRSTAPS). GRSTAPS interleaves task planning, task allocation, scheduling, and motion planning, performing a multi-layer search while effectively sharing information among system modules. In addition to providing a unified solution to STAP-STC problems, GRSTAPS includes individual innovations both in task planning and task allocation. At the task planning level, our interleaved approach allows the planner to abstract away which agents will perform a task using an approach that we refer to as agent-agnostic planning. At the task allocation level, we contribute a search-based algorithm that can simultaneously satisfy planning constraints and task requirements while optimizing the associated schedule. We demonstrate the efficacy of GRSTAPS using detailed ablative and comparative experiments in a simulated emergency-response domain. Results of these experiments conclusively demonstrate that GRSTAPS outperforms both ablative baselines and state-of-the-art temporal planners in terms of computation time, solution quality, and problem coverage.

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“…Yefet et al [46] generated adversairal example based on gradient for CODE2VEC. Ramakrishnan et al [32] and Ravichandar et al [33] both performed adversarial attacks and adversarial training on CODE2SEQ. They all concentrate on method name prediction instead of generating long comment that help programmers understand.…”

Section: Adversarial Examples Generation Adversarial Examples Were Fi...mentioning

confidence: 99%

Adversarial Robustness of Deep Code Comment Generation

Zhou¹,

Zhang²,

Shen³

et al. 2021

Preprint

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Deep neural networks (DNNs) have shown remarkable performance in a variety of domains such as computer vision, speech recognition, or natural language processing. Recently they also have been applied to various software engineering tasks, typically involving processing source code. DNNs are well-known to be vulnerable to adversarial examples, i.e., fabricated inputs that could lead to various misbehaviors of the DNN model while being perceived as benign by humans. In this paper, we focus on the code comment generation task in software engineering and study the robustness issue of the DNNs when they are applied to this task. We propose ACCENT, an identifier substitution approach to craft adversarial code snippets, which are syntactically correct and functionality-preserving with respect to the original code snippet, but may mislead the DNNs to produce completely irrelevant code comments. In order to improve the robustness, ACCENT also incorporates a novel training method, which can be applied to existing code comment generation models. We conduct comprehensive experiments to evaluate our approach by attacking the mainstream encoder-decoder architectures on two large-scale publicly available datasets. The results show that ACCENT efficiently produces stable attacks with functionality-preserving adversarial examples, and the generated examples have better transferability compared with baselines. We also confirm, via experiments, the effectiveness in improving model robustness with our training method.

show abstract

STRATA: unified framework for task assignments in large teams of heterogeneous agents

Cited by 45 publications

References 31 publications

Adversarial Robustness of Deep Code Comment Generation

Adversarial Robustness of Deep Code Comment Generation

GRSTAPS: Graphically Recursive Simultaneous Task Allocation, Planning, and Scheduling

Adversarial Robustness of Deep Code Comment Generation

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