Graph-based method for inductive bug localization

    公开(公告)号:US11645192B2

    公开(公告)日:2023-05-09

    申请号:US17191964

    申请日:2021-03-04

    CPC classification number: G06F11/3624 G06F40/30 G06N3/10

    Abstract: A computer-implemented method executed by at least one processor for software bug localization is presented. The method includes constructing a bug localization graph to capture relationships between bug tickets and relevant source code files from historical change-sets and an underlying source code repository, leveraging natural processing language tools to evaluate semantic similarity between a new bug ticket and a historical ticket, in response to the evaluated semantic similarity, for the new bug ticket, adding links between the new bug ticket a set of similar historical tickets, incorporating the new bug ticket in the bug localization graph, and developing a mathematical graph expression to determine a closeness relationship between the relevant source code files and the new bug ticket.

    Method for supervised graph sparsification

    公开(公告)号:US11610114B2

    公开(公告)日:2023-03-21

    申请号:US16675596

    申请日:2019-11-06

    Abstract: A method for employing a supervised graph sparsification (SGS) network to use feedback from subsequent graph learning tasks to guide graph sparsification is presented. The method includes, in a training phase, generating sparsified subgraphs by edge sampling from input training graphs following a learned distribution, feeding the sparsified subgraphs to a prediction/classification component, collecting a predication/classification error, and updating parameters of the learned distribution based on a gradient derived from the predication/classification error. The method further includes, in a testing phase, generating sparsified subgraphs by edge sampling from input testing graphs following the learned distribution, feeding the sparsified subgraphs to the prediction/classification component, and outputting prediction/classification results to a visualization device.

    MULTI-FACETED KNOWLEDGE-DRIVEN PRE-TRAINING FOR PRODUCT REPRESENTATION LEARNING

    公开(公告)号:US20220261551A1

    公开(公告)日:2022-08-18

    申请号:US17584638

    申请日:2022-01-26

    Abstract: A method for employing a knowledge-driven pre-training framework for learning product representation is presented. The method includes learning contextual semantics of a product domain by a language acquisition stage including a context encoder and two language acquisition tasks, obtaining multi-faceted product knowledge by a knowledge acquisition stage including a knowledge encoder, skeleton attention layers, and three heterogeneous embedding guided knowledge acquisition tasks, generating local product representations defined as knowledge copies (KC) each capturing one facet of the multi-faceted product knowledge, and generating final product representation during a fine-tuning stage by combining all the KCs through a gating network.

    GRAPH-BASED METHOD FOR INDUCTIVE BUG LOCALIZATION

    公开(公告)号:US20210286706A1

    公开(公告)日:2021-09-16

    申请号:US17191964

    申请日:2021-03-04

    Abstract: A computer-implemented method executed by at least one processor for software bug localization is presented. The method includes constructing a bug localization graph to capture relationships between bug tickets and relevant source code files from historical change-sets and an underlying source code repository, leveraging natural processing language tools to evaluate semantic similarity between a new bug ticket and a historical ticket, in response to the evaluated semantic similarity, for the new bug ticket, adding links between the new bug ticket a set of similar historical tickets, incorporating the new bug ticket in the bug localization graph, and developing a mathematical graph expression to determine a closeness relationship between the relevant source code files and the new bug ticket.

    UNSUPERVISED GRAPH SIMILARITY LEARNING BASED ON STOCHASTIC SUBGRAPH SAMPLING

    公开(公告)号:US20210089652A1

    公开(公告)日:2021-03-25

    申请号:US17017048

    申请日:2020-09-10

    Abstract: Methods and systems for detecting abnormal application behavior include determining a vector representation of a first syscall graph that is generated by a first application, the vector representation including a representation of a distribution of subgraphs of the first syscall graph. The vector representation of the first syscall graph is compared to one or more second syscall graphs that are generated by respective second applications to determine respective similarity scores. It is determined that the first application is behaving abnormally based on the similarity scores, and a security action is performed responsive to the determination that the first application is behaving abnormally.

    TEMPORAL CONTEXT-AWARE REPRESENTATION LEARNING FOR QUESTION ROUTING

    公开(公告)号:US20210049213A1

    公开(公告)日:2021-02-18

    申请号:US16936541

    申请日:2020-07-23

    Abstract: A method for employing a temporal context-aware question routing model (TCQR) in multiple granularities of temporal dynamics in community-based question answering (CQA) systems is presented. The method includes encoding answerers into temporal context-aware representations based on semantic and temporal information of questions, measuring answerers expertise in one or more of the questions as a coherence between the temporal context-aware representations of the answerers and encodings of the questions, modeling the temporal dynamics of answering behaviors of the answerers in different levels of time granularities by using multi-shift and multi-resolution extensions, and outputting answers of select answerers to a visualization device.

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