US 2026/0300705 A1Published application

Systems and Methods For Detecting Distinct Bug Reports

AssigneeBugcrowd Inc.

InventorsMuskan Gupta, Ian Conway, Casey John Ellis

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Abstract

Techniques are disclosed for detecting distinct bug reports or BRs over a set or corpus of BRs. For this purpose, a custom feed-forward neural network (FFNN) is trained on feature sets of BR pairs derived from the corpus of BRs. The feature sets are generated by a feature engineering pipeline (FEP). The FENN outputs a set of probabilities/scores, one for each BR pair. Each probability of the set indicates the extent to which the BRs in the corresponding BR pair match/resemble each other. A threshold is identified from the set, at or below which a BR pair is considered to contain sufficiently different or distinct BRs. The FEP is then used to derive feature sets of a new BR paired with other BRs in a given scope. The trained FENN is then executed on these feature sets to determine if the new BR is distinct over all existing BRs in the scope.

Claims20

Dependent claims

A computer-implemented method for executing by at least one microprocessor, computer-readable instructions stored in a non-transitory storage medium coupled to said at least one microprocessor, said computer-implemented method comprising the steps of:

(a) deriving by a feature engineering pipeline, a first group of feature sets for respective pairs of bug reports in a first set of bug reports, said deriving based on a vulnerability rating taxonomy, priority levels of said bug reports, textual components of said bug reports, uniform resource locators of said bug reports and targets identified in said bug reports, wherein said first group of feature sets is represented by a first group of respective fixed-length feature vectors;
(b) tuning and training a feed forward neural network to obtain a trained feed forward neural network, said training based on said first group of fixed-length feature vectors, wherein said feed forward neural network generates a first set of probabilities, each corresponding to a pair of bug reports amongst said respective pairs of bug reports in said first set of bug reports;
(c) identifying a distinct bug report probability threshold from said first set of probabilities;
(d) receiving a new bug report and deriving by said feature engineering pipeline, a second group of feature sets for respective pairs of bug reports derived from said new bug report and a second set of bug reports, wherein said second group of feature sets is represented by a second group of respective fixed-length feature vectors;
and
(e) applying said trained feed forward neural network on said second group of fixed-length feature vectors to generate a second set of probabilities for said respective pairs of bug reports derived from said new bug report and said second set of bug reports, and identifying said new bug report as a distinct bug report amongst said second set of bug reports based on determining whether said second set of probabilities are equal to or lesser than said distinct bug report probability threshold.

3 more elements

A computer system comprising computer-readable instructions stored in a non-transitory storage medium and at least one microprocessor coupled to said non-transitory storage medium when executing said computer-readable instructions, said computer system further comprising:

(a) a first set of bug reports;
(b) a feature engineering module for deriving a first group of feature sets for respective pairs of bug reports in said first set of bug reports, said deriving done by employing a vulnerability rating taxonomy, priority levels of said bug reports, textual components of said bug reports, uniform resource locators of said bug reports and targets identified in said bug reports, wherein said first group of feature sets is represented by a first group of respective fixed-length feature vectors;
(c) a feed forward neural network trained on said first group of fixed-length feature vectors, wherein said feed forward neural network generates a first set of probabilities, each corresponding to a pair of bug reports amongst said respective pairs of bug reports in said first set of bug reports, and wherein a distinct bug report probability threshold is identified from said first set of probabilities;
and
(d) a submission receiving module for receiving a new bug report;
wherein said feature engineering module is used to derive a second group of feature sets for respective pairs of bug reports derived from said new bug report and a second set of bug reports, and wherein said second group of feature sets is represented by a second group of respective fixed-length feature vectors, and wherein said feed forward neural network is executed on said second group of fixed-length feature vectors to generate a second set of probabilities for said respective pairs of bug reports derived from said new bug 36 report and said second set of bug reports, and wherein said new bug report is identified as a distinct bug report amongst said second set of bug reports by determining whether said second set of probabilities are equal to or lesser than said distinct bug report probability threshold.

3 more elements

Dependencies are inferred from the claim text.